1- Considering it as a complete application

One of the biggest reasons behind the failure of many blockchain projects is that businesses often consider it as a complete business solution, which isn’t true. While the technology can be used in a diverse range of scenarios and industries, it doesn’t come with features like business logic, user interface, interoperability mechanisms etc. So, it’d be wise to consider blockchain as a protocol that can be used within a full application.

2- Considering it as a pure storage mechanism

Blockchain was designed to offer an immutable, trusted, and authoritative record of events triggered by a collection of untrusted parties. In its present form, blockchain technology doesn’t come with the key features of a traditional database management technology. So, you should review your data management requirements of the blockchain project and implement a traditional data management solution when required.

3- Assuming it as a permanently dominant technology

Businesses often assume that blockchain will always remain a dominant technology, which isn’t true. It’s evolving constantly in both application and technology and thus, it’s important to consider it as a short-term option to attain a business solution.

4- Assuming the smart contract technology is mature

Business leaders sometimes assume that smart contracts are fully matured, what they aren’t. They may demonstrate the most powerful aspect of the blockchain, but conceptually they’re software scripts. So, instead of planning for full adoption, you should run small experiments first.

5- Assuming the presence of interoperability standards

While some vendors of blockchain platforms may try to promote interoperability standards, it’s difficult to envision when most platforms are still being developed or designed. So, it’d be wise to consider the discussions about interoperability as a marketing strategy as it may not be able to necessarily deliver the benefits you’re looking for.

6- Overlooking the governance issues

Blockchain may seem a less expensive option than others, but it raises lots of questions regarding governance issues. As it’s still in the nascent stage, it may not be possible to predict all possible scenarios and thus, it’s recommended to plan thoroughly and look at it as a short-term option.

7- Confusing the future with the present-day scenario

It’s crucial to ensure that your plan for blockchain implementation with its evolving capabilities. It’s still limited in scope and you shouldn’t consider it as a way to deal with a global scale platform economy.

8- Ignoring the importance of a learning process

As with all other technologies, blockchain also comes with a learning curve which is often overlooked by businesses. It’d be wise to take a hands-on approach to the blockchain projects to make the implementation successful.

9- Overlooking the actual purpose

You shouldn’t take on a blockchain project as it’s trending. Instead, you should review the core intentions of it to get the most out of it.

Conclusion

If you’re at the verge of implementing a blockchain project, keep in mind the above rules and you should be able to sail through effortlessly.

.  .  .

To learn about blockchain, click here and read our another article.

The post 9 Blockchain mistakes and how to avoid them appeared first on Magnimind Academy.

1- Develop problem-solving skills

While it’s crucial for any data scientist to stay up-to-date with the latest tools and technologies, it’s mandatory to have robust problem-solving skills. Assuming you’ve got the required qualifications to become a data scientist, focus on developing this skill as much as you can. In reality, most businesses value experience much more than traditional educational qualification because with more experience, comes more ability to solve different business problems.

2- Develop a solid focus on business impact

One of the biggest factors that separate an average data scientist from a good one is the latter’s natural curiosity to identify patterns in data. Try to delve into the detailed work of exploring datasets, explore the latest techniques in the data science field, test their effects systematically etc. Try to develop a mindset that would help you identify the larger goal of projects and question the core assumptions. The data science field is still not a standardized field and thus, there’s much room for thinking outside of the box to solve pressing problems.

3- Ability to manage expectation carefully

Data science can be quite a confusing field from the outside. It’s almost impossible for those, who’re not associated with this field, to understand whether a data science project is a machine learning project and things like that. The expectation around what a data scientist can do may greatly vary between different people. Thus, it’s imperative for a data scientist role candidate to be able to consistently and proactively communicate with the stakeholders to identify clear expectations and find out misunderstandings early to get everybody on the same page.

4- Ability to work with cloud services

Cloud computing has already become a core part of the field of data science. There’re lots of cases when a data scientist needs to use cloud services. These may include querying databases for scalable analytics, managing and sharing datasets, etc. The ability to work with major cloud service providers like AWS, GCP, Microsoft Azure etc is steadily becoming one of the preferred skills for businesses looking to hire data scientists.

Conclusion

It’s certainly a great time to start your journey to become a data scientist if you aren’t one already, but you’ve to prove that you stand out of the pack. Focus on developing the skills mentioned above, put in real effort and you should be able to become a data scientist role candidate for hiring managers.

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To learn more about data science, click here and read our another article.

The post How can I be a Data scientist role candidate that companies are looking for? appeared first on Magnimind Academy.

1- What is blockchain?

At its simplest form, blockchain is a distributed, encrypted database which records data. It’s a distributed ledger where a transactional record is incorporated by a block and all the records in the database are connected simultaneously with the hash capacity. Once the users create records, a group of computer verifies them and the couples them up with the preceding record present in the chain. In this decentralized database, transactions are protected by powerful cryptographic algorithms and the network status of a blockchain is maintained by consensus algorithms. This technology promises to change the way we buy and sell things, share information, and verify information’s authenticity. And because of its ability to accomplish all these in efficient, transparent, and secure ways, blockchain is being steadily adopted by almost every domain – from individual and business to government.

You can get a clear understanding of blockchain’s ability from the prediction made by Gartner that by 2025, it’ll lead to $176 billion in added business value. In order to understand the reasons behind this prediction, we need to take a look at the key features of blockchain and the key advantages of using it. But before that, let’s have a quick look at the working process of this technology.

2- The working method of blockchain

A block refers to a record of a set of new transactions that can mean anything – from medical data to the location of cryptocurrency. Once a block is completed, it gets added to the chain, resulting in a chain of blocks or a blockchain. As the newly-created block appears as a part of the ledger, the next block cryptographically links itself to this block. At this stage, the transaction receives its second confirmation. Then transactions are reconfirmed each time with the creation of a new block.

3- Key features of blockchain

Though blockchain is generally associated with cryptocurrencies, with Bitcoin in particular, there is much more to it. For a huge number of tech companies, this technology has become an integral part of their business operations. Here’re three key features of blockchain.

3.1- Immutability

The ability to create immutable ledgers is one of the most important features of blockchain. Any database which is centralized stands a huge risk of getting hacked and they have to put trust in third-party in order to keep the database secure. In a blockchain, the ledgers are kept in the never-ending status of forwarding momentum. Here, you can only add data with time-sequential order and it’s almost impossible to modify that data and thus, blockchain is considered practically immutable.

3.2- Consensus

This is probably the biggest feature of blockchain. Here, the ledger is updated through consensus, which is the biggest power of decentralization. In order to update the ledger, no central authority with controlling ability is needed. Instead, every update made to it is validated against stringent criteria in accordance with the blockchain protocol. And the update is only added to the blockchain once a consensus has been reached among all the participating nodes/peers on the network.

3.3- Increases capacity with greater security

The most remarkable thing about blockchain is that it enhances the capacity of the entire network. The key reason is there are lots of computers working together that in total provide a great power than a limited number of devices with limited capacity. It’s a general consideration that with more operational capacity, there comes more security risks. But with blockchain, this statement doesn’t hold true. Despite the huge capacity, this technology offers greater security because it’s just not possible to shut the system down. While the highest level of systems stand a huge risk of getting hacked, the blockchain network is extremely secured by a significant number of computers (widely known as nodes) that confirm every transaction that takes place on this network.

4- Key advantages of using blockchain

As we’ve mentioned earlier that while the huge success of Bitcoin gave the world an idea of the power of blockchain technology, it can immensely benefit a lot of other sectors. Let’s have a look at the key advantages offered by this technology.

4.1- Fraud prevention

You may already know that a huge number of frauds revolve around altering, manipulating, deleting financial records etc. With blockchain, it becomes extremely difficult to perform fraudulent activities. Here, every transaction gets recorded at the time it takes place and then it gets encoded to avert anybody from interfering with the records.

4.2- Improved data security

Some data breaches happen because of poor security management. Databases or networks get left unsecured and that results in compromised data security. Often, the old ways of securing networks and data fail to keep pace with advanced hackers. The distributed nature of blockchain together with native encryption makes it extremely tougher to crack. In addition, data security gets bolstered by digital signatures that need several users to authenticate data access.

4.3- Instant transactions

Often the growth of a business gets heavily impacted by time-consuming contractual transactions. This is particularly true for businesses that process a huge amount of communications on a regular basis. With smart contracts offered by blockchain, businesses can automatically validate, sign, and enforce agreements. This eliminates the need for interference by mediators and hence, saves the business time and money.

4.4- Improved transparency

Traditional financial transactions sometimes get enveloped in mystery by financial institutions. While the financial institutions have good reasons for doing this, sometimes a deal may be slowed down by this. When utilizing a blockchain platform, once the payment is issued by a party, the other party can instantly check the transaction record without having to depend on any financial institution.

5- Industries that commonly use blockchain technology

We’ve already mentioned that blockchain is being heavily utilized by a significant number of major industries. Let’s have a look at the most prominent ones among them.

5.1- Healthcare

In the healthcare industry, blockchain plays a crucial role in terms of increasing security, privacy, and interoperability of information. It also eliminates the need for interference of a third-party while avoiding the overhead costs.

5.2- Financial services

In this section, blockchain has already been implemented in different innovative ways. From simplifying and streamlining the processes to eliminating the need for intermediaries or brokers in order to ensure effective management of transactional data and transparency – blockchain helps the industry in almost every aspect.

5.3- Retail

The retail is one of the biggest industries where blockchain is heavily implemented, from preventing fraudulent transactions to ensuring the authenticity of costly goods to enabling virtual warranties, and many more.

5.4- Government

Government sectors are also leveraging the power of blockchain. The proper sharing and linking of data using blockchain enable efficient management of data between departments. It also improves transparency and offers an efficient way to monitor, as well as, audit the transactions.

6- Why you should learn blockchain technology right now?

Blockchain is one of the biggest candidate-short industries that are in high demand. If you want to step into this cutting-edge field, this is probably the best time for you to get certification in blockchain technology. Let’s have a look at the key reasons why you should do it.

6.1- Premium salary

The paucity of blockchain professionals will soon spread across every domain – from marketing and finance to logistics, and many more. The organizational structures, functions, governance structures etc are changing at a fast pace for the majority of the businesses. In this scenario, even a fundamental blockchain certification can increase your pay heavily. There’s a good number of courses on blockchain are being offered by some premium institutions like Magnimind Academy. Instead of throwing money around speculations in different fields, you should go ahead and obtain a blockchain certification right away.

6.2- Huge flexibility

Blockchain is playing a crucial role in terms of helping a lot of industries already. These include banking, healthcare, energy sector, retail, and many more. By observing the trend of adoption of this technology, it can be safely said that this scenario will become even larger over time. So, with a blockchain certification in your hand, you can rest assured of having plenty of premium job opportunities across industries.

Conclusion

As with any major technology, blockchain also comes with some disadvantages. However, the huge potential of this technology can easily make businesses to overlook them. It may still have a long road ahead toward mainstream adoption, but a huge number of industries are gearing up to accomplish this. Over the next few years, we can expect to see a lot of organizations experimenting with new blockchain applications in order to make the most out of it.

.  .  .

To learn about blockchain, click here and read our another article.

The post What is Blockchain with 3 features? appeared first on Magnimind Academy.

You may often come across professionals working in the field of data science, who suggest a lot of different things and paths to take. It’s true that you can’t learn it all in a day or even within just a few months. But don’t get overwhelmed when someone tells you that you need to learn it all – right from the fundamentals to programming, machine learning, statistics, database technologies, and several other domain-specific technologies. It all depends on how quick a learner you are, your background (like whether you have a Mathematics/Statistics background, or have worked in the IT industry), and the time and effort you are ready to put into learning and mastering data science.

So, before you decide to learn data science, let’s delve deeper to check what the job titles are that you can aim to land once you have finished your course.

In the field of data science, three job profiles that are often touted as the “big three” are Data Analyst, Data Engineer, and Data Scientist. Let’s take a look at the job responsibilities and skills required for each.

1- Data Analyst

Though some may call it an “entry-level” position in the domain of data science, not all data analysts are junior. The salaries you get too can vary widely based on your experience and the nature of the job you do. No wonder why it features highly on the list of many who learn data science.

Primarily, your job as a data analyst would include looking at industry or company data and analyzing it to find insights that can answer business questions and help in making business-driven decisions. An instance could be where you are asked to analyze sales data from a current marketing campaign to evaluate its efficiency and spot strengths and weaknesses. The task would involve getting access to the data, probably cleaning it, and executing some statistical analysis to answer related business questions followed by visualizing and communicating these results to other teams in the company (and even to those in the management) so that they can act upon it.

Over time, you may need to work with different teams within a company. So, you may help the company CEO to find reasons into what the company did right (or wrong) in its expansion plans by using data one month, while the next month, you could be dealing with marketing analytics. Unlike data scientists who often find interesting trends on their own and predict future results, your job will typically involve mining useful insights from data and answering business questions that are given to you.

Though your job specifics may vary from position to position, the skills you need to handle the job of a data analyst successfully include:

• Intermediate data science programming in either R or Python along with the use of popular packages
• Data cleaning
• Intermediate SQL queries
• Probability and statistics
• Data visualization

Additionally, you should have good communication skills to convey complicated data analysis with clarity and in an easy-to-understand manner to people having no programming or statistics background.

When you consider the career prospects as a data analyst after you learn data science, you will have a fairly open-ended career path as you will get to work in a wide range of positions. Many professionals in this field continue building their data science skills, usually with an emphasis on machine learning, to make their transition into the role of a data scientist easier. You may even work toward becoming a data engineer in case you are more interested in data infrastructure, software development, etc. Thus, taking up the post of a data analyst after you learn data science could be a prudent move.

2- Data Engineer

This job profile involves a lot more programming and software development skills while needing less statistical analysis skills. When you work as a data engineer with a data team, it would be your responsibility to create data pipelines to get the latest marketing, sales, and revenue data to data scientists and data analysts speedily and in a usable format. You are also likely to be responsible for creating and maintaining the infrastructure required for storing and accessing past data quickly.

The skills that you will need, in general, for this position are:

• Advanced programming skills (possibly in Python) for working with massive datasets and creating data pipelines
• Advanced SQL skills (and possibly knowledge of Postgres)

When you consider your career prospects, you can draw upon your skills and continued experience to move into other software development specialties. You may even have the potential of moving into management roles as the leader of the data engineering team.

3- Data Scientist

This is often touted as the most coveted job with a fat pay packet, which is why many who learn data science have their eyes set on becoming a data scientist. Though your job would involve doing several things, which are the same things done by data analysts (such as obtaining, cleaning, and visualizing data), you would also usually set up machine learning models to make precise predictions about the future by using past data. When you learn data science and take up the job of a data scientist after finishing your course, you will often enjoy more freedom than other job profiles to chase your own ideas and experiment to locate remarkable trends and patterns in the data that the management might not have even given a thought to.

As a data scientist, you will need the skills of a data analyst along with the following:

• A rock-solid understanding of both unsupervised and supervised machine learning methods
• Programming skills in Python or R (and preferably, familiarity with other tools such as Apache Spark)
• A strong base in statistics and the capability of assessing statistical models

When you consider your career prospects, you can start work as a junior data scientist, and then rise to become a senior data scientist or decide to specialize further in the field of machine learning to become a machine learning engineer. Either of these career paths would bring a significant pay raise your way, which explains why many who decide to learn data science aim to become data scientist, often as a stepping stone to transition into other high-paying jobs. You may even contemplate roles with a bend toward management like chief data officer, lead data scientist, etc.

Time required to learn data science for landing a good job

Here’s a curriculum roadmap to learn data science and start your career in this field:

• Python Programming: Since this is a fundamental skill that you will need, get to know the syntax of Python. In your quest to learn data science, focus on understanding how you can run a python program in different ways.
• Linear Algebra and Statistics: When you decide to learn data science, this would be a precondition for data analysis and machine learning. If you already possess a solid understanding in these fields, you can spend just a week or two to brush up on the key concepts. Remember to emphasize especially on descriptive statistics because the ability to understand a data set is a skill that’s extremely valuable. When your primary aim to learn data science is to get a good job, you surely need to get robust knowledge of linear algebra and statistics.
• Pandas, Numpy, and Matplotlib: You will need to learn ways to manipulate, load, and visualize data. In your road to learn data science, mastery of these libraries will be vital to the success of your personal projects.
• Machine Learning (ML): You will need to learn the theory and application of ML (machine learning) algorithms at first and then apply the concepts that you have learned to real-world data.
• Production Systems: after you learn data science and are job-ready, you will have to take on real-world data and convert it into action. To handle this task successfully and comfortably, you will need to be trained in ways of using a business’ computational resources to acquire, convert, and process data.

While undergraduate and master’s courses in colleges and universities often taken 2-3 years to teach you all the above, many say you can learn them in about 6 months by dedicating around 6-7 hours every day. If you already know the fundamentals, you may even opt for bootcamps that will get you job-ready within just a few weeks.

.  .  .

To learn more about data science, click here and read our another article.

The post How Long Does It Take To Learn Data Science Totally For Finding A Good Job? appeared first on Magnimind Academy.

1- What is deep learning?

In its simplest form, deep learning, also known as deep machine learning or deep structured learning, is a subset of machine learning and refers to neural networks that have the ability to learn the input data’s increasingly abstract representations. These days, implementation of deep learning techniques can be found to a great extent, from self-driving cars to academic researches.

2- What sets deep learning apart?

If you follow prominent job portals, you can find that there’s a significant number of deep learning professionals job positions almost all of which are paying really well. Now, you may wonder why do companies hire these professionals? Or, what can such a professional bring to them? Let’s have a look.

2.1- Quality and accuracy

Every company wants quality and sometimes work produced by human employees come inferior and with errors. This is particularly true for data processing repetitive tasks. However, a worker powered by deep learning is capable of developing new understandings and producing high-quality, accurate results.

With the help of deep learning, software robots can understand spoken language, recognize more images and data, and work more efficiently. These are the main reasons why companies across the globe are hiring deep learning professionals.

2.2- Increased cost and time benefit

In its simple form, neural networks can be considered as trainable brains. These networks are provided with information and trained to do tasks, and they’ll use that training together with new information and their own work experience when it comes to accomplishing those tasks.

Implementation of deep learning in business can save the company a significant amount of time and money. In addition, when time-consuming or repetitive tasks are done efficiently and quickly, employees are freed up to take care of creative tasks that actually need human involvement.

3- Deep learning vs. Machine learning

As deep learning is a branch of machine learning, general people often become confused about when to use over the other. In general, when it comes to large datasets, deep learning should be the ideal technique while traditional machine learning models can do perfectly well with small datasets.

Deep learning outperforms traditional machine learning in the context of complex problems like speech recognition, natural language processing, image classification etc. Another key difference between them is that deep learning algorithm needs a long time to be trained because a large number of parameters while traditional machine learning algorithms can be trained within a few hours. Interpretability is another reason for which many companies prefer using machine learning over deep learning.

4- Guide to deep learning structure

Deep learning is a complex field consisting of several components. In this deep learning structure guide part of the post, we’ve put together the major elements that you’d need to master upon.

Also, we’ve designed this deep learning guide assuming you’ve a good understanding of basic programming and basic knowledge of probability, linear algebra and calculus. Let’s have a look at the guide.

4.1- Fundamental of machine learning

It’s imperative to get a good understanding of the basics of machine learning before you dive into deep learning. Basically, it’s distributed in three types of learning – supervised, unsupervised and reinforced learning.

In deep learning, a significant amount of machine learning techniques like logistic regression, linear regression etc are used. There’re lots of resources available that can help you accomplish this goal. You should also learn Python at this stage. Try to get yourself introduced to scikit-learn, a widely used machine learning library. At the end of this stage, you should have a good theoretical as well as a practical grasp of machine learning.

4.2- Introduction to deep learning

The first thing you should do is understand the frameworks of deep learning. Deep learning professionals mainly need to work with algorithms which are inspired by neural networks. Though there’re lots of resources available online that you can use to learn the basics of deep learning, it’s recommended to take a course from a reputed institute.

Try to get access to a GPU (graphics processing unit) to run your deep learning experiments. If possible, try to read some research papers in deep learning as they cover the fundamentals. At this stage, try to pick any of the three – PyTorch, TensorFlow or Keras. Whatever you choose, be sure to become very comfortable with it.

4.3- Introduction to neural networks

A neural network comes with a layered design that contains an input layer, a hidden layer, and an output layer. It functions like the human brain’s neurons such as receiving inputs and generating an output.

There’re several types of artificial neural networks that are implemented based on a set of parameters needed to determine the output and mathematical operations. The functions of these neural networks are utilized in deep learning which helps in image recognition, speech recognition, among others.

4.4- Fundamentals of Convolutional Neural Networks

Put simply, Convolutional Neural Networks are multi-layer neural networks which consider the input data as images. It’s widely used in facial recognition, object detection, image recognition and classification etc. The best thing about Convolutional Neural Networks is the need for feature extraction is eliminated. The system learns to perform feature extraction.

The fundamental concept of CNN is, it utilizes convolution of images and filters to produce invariant features that are passed on to the next layer. In the next layer, the features are convoluted with a different set of filters to produce abstract and more invariant features and this process continues till we get final output/feature that is invariant to occlusions.

4.5- Understanding unsupervised deep learning

Unsupervised learning is a complex method with the goal of creating general systems which can be trained using a very minimum amount of data. It comes with the potential to unlock unsolvable problems which were done previously. This method is widely used to solve the problems created by supervised learning.

4.6- Introduction to natural language processing

Natural language processing is focused on making computers capable of understanding and processing human languages in order to get them closer to the human-level understanding of language. This domain mainly deals with developing computational algorithms that can automatically analyze and represent human language. It can also be used for dialogue generation, machine translation etc.

4.7- Introduction to deep reinforcement learning

Through this technique, software or a machine can learn to function in an environment by itself. Though some may compare reinforcement learning with other forms of learning like supervised and unsupervised learning, there remains a major difference. It’s that reinforcement learning isn’t provided with outcome instructions, instead it follows trial and error mechanism to develop appropriate outcomes.

5- Major applications of deep learning

Here’re some real-life applications where deep learning is used heavily.

5.1- Speech recognition

You’ve probably heard about Apple’s intelligent assistant Siri, which is controlled by voice. The tech giant has started working on deep learning to develop its services even more.

5.2- Instant visual translation

You’re probably aware of that deep learning is utilized to identify images which contain letters and once they’re identified, those can be turned into text and translated, and the image can be recreated using that translated text. In general, this is called instant visual translation.

5.3- Automatic machine translation

You may have already heard about the translation ability of Google. But did you know what’s the technology behind Google Translate? It’s machine translation that tremendously helps people who cannot communicate between themselves because of the difference in language. You may ask that this feature has been around for some time now, so there shouldn’t be anything new in this. Using deep learning, the tech giant has completely reformed the machine translation approach in Google Translate.

Here, we’ve only mentioned some popular real-life cases that use deep learning extensively and showing promising results. There’re lots of other applications where deep learning is successfully being implemented and demonstrating good results.

Final thoughts

So, this is the overview of deep learning in a simple form. Hopefully, by now you’ve got a clear idea of what should be a good deep learning structure to follow in order to become a deep learning professional.

With the entire business landscape steadily leaning toward artificial intelligence together with massive amounts of data being generated every single day, the future surely holds a great place for deep learning professionals. The key reason behind this is the supremacy of deep learning in terms of accuracy when properly trained with an adequate amount of data. If you’re interested to step into the field, probably this is the best time to start your journey because the big data era is expected to provide massive amounts of opportunities for advancement and new innovations in the field of deep learning.

.  .  .

To learn more about data science, click here and read our another article.

The post Deep Learning Structure Guide for Beginners appeared first on Magnimind Academy.

But is it actually possible to become a data scientist for anyone? Of course, one can learn some tools used in the data science field and call himself or herself a data scientist, but that’s actually far from the truth. However, it’s possible for everyone to become a data scientist these days, indeed with a robust plan. In this post, we’re going to discuss the data scientist learning path following which anyone can become a part of this sophisticated, smart and glamorous league of professionals.

1- What’s a data scientist?

Before delving deeper into the learning path, let’s have a quick look at what a data scientist actually does. Data science is a complex field and it involves lots of different skills that contribute toward making the position even more important. In its simplest form, a data scientist can be considered as someone who has the ability of capturing and analyzing a massive amount of data in order to reach a conclusion. They perform this through different high-end tools and techniques. Essentially, a data scientist looks for meaning in huge amounts of data.

The key reason behind the emergence of the data scientist role is the need to understand the usually messy and huge amounts of data captured by fast-growing companies. These companies are trying to glean actionable insights about their business as well as customers with this data, but the professionals needed to perform this task are in short supply.

Another factor that contributed heavily toward increasing the demand for data scientists is that business leaders of today not only just want to know what happened, but they also want to know what’s happening, what’ll happen in the future, and how will it impact their business operations.

2- How can anyone become a data scientist?

As we’ve already discussed that it’s almost impossible for companies to manipulate and make sense of the data they capture on their own. As a result, many organizations are more than willing to pay an attractive salary for a good data scientist. With an excellent number of high-paying job opportunities, data science has become the field to be in at the moment. The data science field is growing and it’ll continue to do so for the foreseeable future.

Whether you’re a working professional looking to step into the field, a student planning for your future or are belong to a different background like the non-coders league, there’re ways to become a data scientist and it’s never too late to start your journey. Regardless of your present exposure to data science, here’re the skills you need to have to succeed in your endeavor.

2.1- Mathematics and statistics

Mathematics is a subject of which lots of people are scared of, but if you want to be a successful data scientist, you’ve to get your concepts cleared on things like probability, linear algebra etc. Put simply, probability refers to the measure of how likely something is going to happen.

In the data science field, there’re lots of events that cannot be predicted with complete certainty. So, concepts like Bayes Theorem, probability distribution etc are much needed to perform data science. Linear algebra deals with vector spaces. It’s crucial to understand different ideas behind different techniques of linear algebra like Time Series, Clustering, among others to understand their applicability.

Statistics is a crucial part of analyzing and interpreting the data. A lot of statistical concepts are used to perform data science, so a good understanding of them is essential.

2.2- Coding

More and more employers of data scientists are looking for candidates who’re conversant with programming languages like Python, R, Java etc. A good understanding of these languages is a must to succeed as a data scientist. You should understand that this isn’t about being an excellent coder but it’s all about being comfortable with different programming environments to be able to work with data as and when required. If you can demonstrate the expertise to adapt to the changes in the technological landscape, it’ll surely be considered as a good advantage.

2.3- Machine learning

It’s a field that provides computers with the ability to make decisions based on earlier data or previous experience. It’s a group of algorithms that use machine power to derive insights for you. To become a good data scientist, you should have a good understanding of neural networks, adversarial learning, reinforcement learning, supervised machine learning, logistic regression, decision trees, among others. In the data science field, different machine learning skills are used to perform different activities. So, it’s wise to be familiar with them.

2.4- Data visualization

Once you’ve done working with your data analysis, you’ll need to convince others to adopt your insights. Being visual creatures, it’s typically much easier for humans to consume the information by examining a graph or chart than by going through the numbers.

As a data scientist, you’ve to be able to visualize data with the help of data visualization tools like Tableau, ggplot, D3.js etc. These tools help you to convert complicated results from your findings to an easily consumable format. With data visualizations, organizations can grasp insights quickly to act on different business opportunities.

2.5- Analytical mindset

An overall analytical mindset is required to do well as a data scientist. Essentially, these people need to spend a huge percentage of their time in discovering and preparing data. So, as a part of that league, you’ve to be able to raise questions about data. Keep on updating your knowledge by reading relevant resources to be able to channel your thinking in the right direction.

2.6- Strong business acumen

As a matter of fact, most organizations that work with data depend on their data scientists not only to mine huge datasets but also to communicate the insights to decision makers. An effective data scientist should not only come with the ability to work with complex, massive datasets but with the understanding of the intricacies of the business he/she works for.

Having good business knowledge allows him/her to ask the right questions and come up with actionable solutions which are actually feasible for the business. In the context of data science, being able to understand which problems are crucial to solving for the business plays an extremely important role.

3- Different ways to become a data scientist

There’re different ways to become a data scientist, but it’s completely impossible to become one without a college education. At the very least, you’ll need a Bachelor’s degree to pursue further study. Also, if your goal is to land a leadership position, you should try to earn a Master’s degree or Ph.D.

There’re three main ways to become a data scientist – the traditional way, self-learning, and by attending a reputed school (like Magnimind Academy) that offers data science prep course and data science bootcamp. If you look at the traditional way, it may not be feasible for everyone to go back to school to complete a Master’s degree, both in terms of time and increasing educational cost.

If you consider the self-learning method, you can obviously learn many things but one of the major drawbacks of being self-taught is that your knowledge may not be complete and you may not be aware of that. Also, you won’t be able to measure your learning progress through this method.

Coming to the third option, institutes that offer data science degrees have become quite an obvious choice to aspiring data scientists. Even if you’re coming from a different background, the non-coders group, for example, data science prep courses offered by these schools are sufficient enough to provide you with the necessary skills based on which you can move forward to attend a data science bootcamp. Another major advantage of attending these schools is that they let you step into the field in a much shorter span of time (usually data science bootcamps come with the duration of 6-12 weeks) and around the one-fifth cost of attending a 2-year Master’s program. So, if you have the basics right like having a college degree, analytical bend of mind and mindset to put your best effort in, you can surely become a data scientist by attending one of these institutes.

In conclusion

Regardless of the path you prefer to take to become a data scientist, it’s always crucial to keep some things in mind. For example, finding a mentor, working on increasing your network, visiting data science conferences, meetups etc play important roles in establishing yourself in the industry.

In addition, as different technologies will come and go in the field, it’s important that you keep on learning continuously about new tools and technologies to stay on the same page with industry trends and remain in demand.

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To learn more about data science, click here and read our another article.

The post Guide to Becoming a Data Scientist for Everyone appeared first on Magnimind Academy.

However, there’re still some technical issues with virtual reality related to optimization and rendering. Until now, all advancements in the field were focused mainly on better hardware and uninterrupted and increased frame-rate. However, recently an idea of using AI for virtual reality has emerged, which will bring a multitude of benefits. The main reason is that big data and AI are perfectly suited for pattern recognition and hence, similar pattern generation. This method of working can generate a new bunch of advantages.

Over the next few years, virtual reality applications will likely to become increasingly sophisticated with the emergence of more powerful devices that are capable of developing higher quality visuals. The understanding of how we can usefully interact and navigate within virtual environments will also evolve, resulting in the development of more natural methods of exploring and interacting with virtual space. Here’re some near-coming effects of immersive virtual reality experiences boosted by the power of AI.

1- Hazard warnings

Apart from their own ability to judge a situation within a fraction of a second, humans have developed a diverse range of mechanisms that can help them stay safe from danger. However, these judgments that are usually called intuitions aren’t infallible.

What if a machine that has a combined experience of thousands of people could overtake such a task? Such a development can save millions of soldiers on the battlefield by helping them in anticipating the moves of opponents and alerting them in advance. AI has already been employed in different military strategies. But with this implementation, battlegrounds of the future will become a more high-tech environment.

2- Customized simulators

AI combined with virtual reality/augmented reality is a strong combination that can be used as a tool for educating the next generation of pilots, surgeons, among others. Today, with the help of virtual reality, we can learn to drive a car safely, without endangering our or the instructor’s life. In addition, for some activities, this also proves to be an effective way of reducing costs, as some real-life activities involve expensive supplies.

AI can replace numerous situations that occur randomly and learn from the student’s behavior. As the student gets better, the system will present increasing difficult situations. AI has the ability to improve simulated training by incorporating more data points, comparing as well as contrasting different techniques, and by personalizing the education. The improved system will act more like a customizable trainer instead of a static simulator. With a simple headset and a set of sensors, we should be in a position to learn everything. Virtually anyone should be able to get access to world-class coaching at any sporting or academic discipline.

3- Physical environment mapping

Today some furniture providers offer apps that provide the users with the ability to try out furniture, after carefully inputting the size and obstacles such as doors and windows of their rooms. What if the process becomes faster and more accurate by just scanning the room with a user’s phone?

AI has the ability to help map environments in real-time and merge those results with a virtual world. The result is that users get a fully immersive virtual reality experience with real-world structures. The fledgling system comes with the ability to generate CAD-quality models of a house so that users can try decorations and furniture before they buy. With a bit more training, the system can offer on-demand design services. For instance, the users select a style and the necessary things, and the system comes up with a complete plan, much like what an interior designer does.

4- Game development

As a primary application of immersive technologies, it’s safe to assume that gaming will continue to be one of the major driving forces for virtual reality’s progression, and in this endeavor, AI can help to a great extent. First, it’ll replace the present method of animation. Right now, two methods are applied for animating characters – manual CG work and motion capture.

Motion capture is restricted to the physical capabilities of the actor while handcrafted animations are highly laborious. Motion capture involves recording a huge array of movements which are essentially repeated time and time again. New systems utilize machine learning to merge a huge library of the stored movements and then map them onto characters that are being developed. This’ll open up a new domain of realistic animation in the context of cartoons, video games, and virtual reality environments. Even non-player characters may become part of the story in a more believable and relatable way.

5- Immersive traveling experience

Virtual reality isn’t only about beautiful worlds where people can lose themselves. It can also come up with an amazing replica of locations in the real world that are costly or somewhat impossible to reach for the common people.

Development of immersive travel experiences can be as close as it gets to the actual thing for some demographics. It can also become a new type of entertainment for people who’re passionate about traveling.

6- True socializing

Facebook’s heavy invest in virtual reality with its acquisition of Oculus Rift, we’ve already received a hint about that one day, social media will likely to get a boost from the virtual reality immersive experiences powered by AI.

In the future, AI may have the task of designing users’ social media avatar by considering both their pictures and preferences. In the near future, we may be in a position to meet our friends in virtual environments. The concept requires mind-boggling processing power, but AI together with virtual reality has the ability to make it possible.

7- Rendering optimization

One of the major challenges in virtual reality/augmented reality is delivering realistic graphics with present day’s consumer hardware. A huge amount of complexity results into lag and pixelated images that in turn results into problems for virtual reality headset wearers. As a result, most of the virtual reality experiences available today are lacking in convincing detail and simplistic.

However, in virtual reality, AI techniques can be used for selective rendering where only some specific portions of a scene are dynamically generated. AI techniques can also help to compress images intelligently, enabling faster transmission over wireless connections without any understandable loss in quality.

8- Challenges with more immersive content

Implementation of AI for virtual reality/augmented reality is expected to offer more immersive technology which will be increasingly personalized. The drive to capture people’s attention generates two challenges. First, a lack of authority over personal data may drive the users away from the long-term adoption of the new technologies. User privacy and data controls have become key concerns for customers. Given the improved data tracking features of immersive technologies, from tracking facial expressions to eye-movements, the personal data will become at more risk, making privacy a more serious concern. Secondly, the well-being of the users will become at stake. Let’s have a look at some probable steps that can be taken to mitigate these challenges.

8.1- More authority to users about their personal data

It’s a fact that major virtual reality companies use cookies to store data, while collecting information on the browser and device type, location, among others. In addition, communication with other users in virtual reality environments is being stored and sometimes shared with third parties for marketing purposes. It leads to the necessity of a solution that acts like a buffer between companies and users.

8.2- Regulatory frameworks

The privacy concerns associated with traditional media has already started arising in immersive content. If developers aren’t willing to provide agreeable and clear terms of use, regulators need to step in to protect the consumers, as already done by some jurisdictions.

As companies develop advanced applications using immersive technologies, they should focus on the transition from using metrics that only measure the amount of user engagement. Alternative metrics may include something like a net promoter score for the software that would indicate how strongly consumers recommend those services to their friends based on their own experience with them.

Final thoughts

Lagging hardware and costly barriers have caused virtual reality to become overhyped over the last few years. With the implementation of AI, organizations can overcome earlier technical barriers while improving realism. These are only some of the possible applications of AI in virtual reality.

As the technology becomes more widely accepted, we can expect to see more innovative applications in the near future. However, more work on the part of the developers will be required if immersive technologies are to generate more interactions with the content and media.

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To learn more about artificial intelligence, click here and read our another article.

The post Immersive Virtual Reality AI and Its Near-Coming Effects appeared first on Magnimind Academy.

However, it’s not easy to become a data scientist. One needs to have an adaptable and definite set of skills. It requires a perfect mix of structured thinking, problem-solving and a lot of technical skills in order to become a successful data scientist. If you’re planning to become a data scientist, read on as we’ve put together some essential things that you’ve to keep in mind to become successful in your endeavor.

1- Good academics

Educational qualifications play a crucial factor in being a data scientist. Organizations often prefer candidates with a Master’s degree in the field of computer science, mathematics, statistics etc.

Also, there’re some research-oriented companies that look for data scientists who come with a PhD. So, if you’re just starting out, it’s wise to focus on building your educational qualifications.

2- Robust programming skills

To become a successful data scientist, your programming skills have to be at an exceptional level. Among other programming languages used in the field, Python is the most preferred and widely-used one. It’s Python’s adaptability that has helped it gain this position. You can use it for almost every step involved in the process of data science. You can work with different sets of data and create datasets.

Good knowledge of R is also preferred for data scientists. R is widely used to solve various statistical problems. However, if you’re not comfortable with programming, it may be a little difficult to master it because of its steep learning curve. If you’re not coming from a tech background, programming as a whole may seem to be extremely difficult.

There’re several courses offered by reputed institutes that can easily help you get started. Just don’t expect to do super cool stuff from the very beginning because that doesn’t happen. But once you’ve overcome the initial challenges and remain consistent, you’ll surely be able to master them.

3- Strong business intelligence skills

As an aspiring data scientist, you should focus on developing strong business intelligence skills that is one of the essentials of the field. These skills need the ability to communicate your findings to business decision makers. Engaging these people in a manner which captures their attention both logically and emotionally has become imperative for data scientists.

In any data-driven organization, a massive amount of data is produced on a regular basis that has to be interpreted to decision makers in an easily consumable format. Pictorial representations in the forms of charts and graphs are naturally more consumable to people than just plain numbers.

To become a successful data scientist, you should have robust communication skills together with the ability to use data visualization and data management tools. So, try to become familiar with tools like D3.js, ggplot, Tableau, matplotlib etc to be able to represent complex things in a simple manner. It’s also equally important to work on your communication skills. Though these are usually the least talked about skills a data scientist needs, they’re extremely important.

You can master multiple tools and latest techniques, but if you fail to communicate your analysis properly to the decision-makers of your company or your client, it’ll raise a question on your expertise. One effective way to overcome this is if you’re working as a data scientist, find someone from a non-technical department and try to explain data science terms to him/her. It’ll help you gauge your progress to a good extent. There’re lots of resources available on the web, so with a good amount of practice, you should be doing good.

4- True expertise in machine learning

In today’s data-driven tech world, machine learning has become one of the heavily demanded skills for data scientists. To be proficient to deal with a massive amount of data on a regular basis, focus on learning machine learning techniques and methodologies like ensemble methods, k-nearest neighbors, random forests, among others. You can carry out these techniques further with the help of R and Python libraries.

Also, it’s extremely important to understand that the datasets you usually work with in machine learning competitions are usually clean and they’re different from what you’ll be working with in real-life projects. In real-life projects, you’ll have to deal with unclean and messy data. It’s a difficult part and eventually becomes a part of your routine. There’s one thing you can do to overcome this hurdle is by reaching out to working data scientists and knowing about their experience. Of course, having a great score in a competition can greatly help you in measuring your learning progress, but the employers will want to know how you can leverage your knowledge in a real-life scenario.

5- Don’t try to learn multiple things at once

This is a common mistake done by many aspiring data scientists. There’re lots of tools used in the data science field and these people tend to focus on multiple things once at a time but they often end up mastering none of them. Ideally, you should pick one tool and get a solid understanding of it. For example, if you’ve started learning Python, don’t try to focus on learning R before you’ve mastery over Python. There’re lots of resources available that can help you learn each tool. So, take help of them and keep your patience.

6- Study and practice as much as you can

Despite what you may find in various posts, it’s never easy to become a data scientist. You’ve to devote a whole lot of quality time to become a successful one. You can always start with simple things and develop on that but you’ve to make sure that you spend extensive and quality time studying and practicing.

Data science as a field is huge so there’re certain areas that need to be studied over and over again. You should understand that without adequate practice, your study will never get complete. With practice, more questions keep on coming up and you’re compelled to study again to clear them out. Also, there’re lots of existing concepts to learn in the field and there’re new ones coming up almost regularly. So, you’ve to keep yourself abreast of industry trends and changes. Visit forums for data scientists, read articles, books to make sure you stay on the same page with those happenings.

7- Join a community

Data science communities can greatly help in throughout your journey of becoming a data scientist. As we’ve discussed earlier that data science is a huge and difficult field, chances are that you’ll burn out quickly and may spend a huge amount of unnecessary time attaining your goal.

However, with buddies and mentors, you could sail through this. Don’t go months of wasting time on a concept that someone could have helped you understand in a few hours. In data science communities, there’re people who’ve already done what you’re trying to do now. On the other hand, you shouldn’t look for help too fast when you haven’t tried well. Remember that there’re lots of things that you can learn from your own study, research and mistakes.

8- Do projects

To become a successful data scientist, you need to implement your learning. Take up real-life projects and try to understand the architecture behind them. In the data science field, hands-on experience matters a lot and large organizations often prefer applicants with this. In this context, communities can again help you to a good extent. If possible, try to collaborate on projects with fellow members. It’ll not only give your learning a boost but will also help you in establishing your expertise as a data scientist among your peers.

With the growing competition in the business world, the task of finding an effective data scientist has become difficult these days. As you can see, a data scientist needs to have a mixture of multidisciplinary skills like the ones discussed above. So, if you want to hold the century’s hottest job someday, start your journey now if you haven’t already. Just remember that becoming a data scientist isn’t an easy goal to accomplish, you need to remain consistent and stay focused to become successful.

Final takeaway

While certifications and degrees are surely valuable, relying on them solely may not be able to take you much further. This is because understanding a data science project lifecycle, dealing with deadlines, handling clients etc – all are valuable parts of becoming an effective data scientist.

So, try to apply your knowledge outside the classroom whenever possible. For example, you can maintain a blog where you consistently write about your analysis, post them on data science forums and ask for feedback. This’ll help you learn a lot and will benefit you greatly when you look forward to advancing your career as a data scientist.

.  .  .

To learn more about data science, click here and read our another article.

The post Things That Data Scientists Should Be Aware Of appeared first on Magnimind Academy.

For example, the internet that has entirely transformed the way we interact, socialize, work, and share information with each other. According to many, the emergence of blockchain is probably the biggest tech develop after the internet that has the ability to disrupt technology.

1- What’s blockchain technology?

Put simply, it’s a distributed ledger where transactions between two parties are recorded efficiently and in a permanent and verifiable manner. You can consider it as a developing list of blocks that can also save transactions which haven’t entered any previous block. Blocks can be considered as a lasting store of records that, once encrypted, cannot be changed or removed. In this technology, information is distributed without letting others copy it.

All blocks characteristically contain previous block’s data transaction, timestamp, and cryptographic hash. No central company or person owns blockchain. Instead, information is stored across many personal computers so that there’s no middleman. It’s distributed and decentralized in nature so that no one can corrupt it or take it down. However, anyone can utilize the system and help run it.

2- Major features of blockchain

Though for many people, the effectiveness of blockchain is unknown, it’s important to know if you’re planning to pursue a career in this disrupting technology. Let’s have a look at the key features of it.

2.1- Distributed ledger

All participants validate the information individually without any central authority. Each and every node contains indistinguishable copies of all the information.

2.2- Chronological

Each block comes with a unique timestamp which is the time when it was incorporated in the system. Timestamp acts like a variation for the hash function and no two blocks can contain the same timestamp. The timestamp is also used to evaluate whether to accept or deny a block.

2.3- Immutable

The records in the system are immutable which means the information on the system is safe and tamper proof.

2.4- Consensus-based

On a blockchain, a transaction has to be approved by each and every participant (node), else it’s rejected.

2.5- No need of a middleman

The members of the blockchain make sure that there’s no malpractice and thus there’s no need of a middleman to monitor and take care of the transactions.

2.6- Trustless operation

In the blockchain, the consensus ensures that no mal-intended or wrong transaction takes place and thus the operation is trustless in nature. So, wrong transactions don’t get validated and entered in the system.

3- What’s bitcoin?

Presently, we’re living in a huge technology expansion and one of this is certainly the most innovative product to finance – cryptocurrencies. Popularized by bitcoin, these virtual currencies utilize blockchain technology to process transactions.

Bitcoins have been gaining a huge amount of importance over the past few years. Let’s have a look at why these digital currencies are being accepted across the world.

3.1- Better acceptance

Today, more customers are using bitcoins because more legitimate companies and businesses are accepting them as a form of payment.

3.2- Control over capital

Many currencies and their usage outside of their native country are being restricted to an extent, thus increasing the demand for bitcoin.

3.3- Reduced remittance

Around the world, many governments are implementing policies that regulate remittance made from other countries either by writing new regulations or making the charges significantly high. This restriction of not being able to send money overseas is driving more people toward cryptocurrencies such as bitcoin.

3.4- Security

As we’ve discussed earlier, bitcoins use blockchain technology which is a solid and secure technology. Users of cryptocurrencies have already started to experience the benefits of using such a robust technology. This offering of a more secure way of transacting in our present ecosystem is a huge plus.

4- A look into blockchain’s future developments

Blockchain’s future developments will be mainly based on its robust built-in abilities. It’ll act like a tool for bringing everybody at the highest level of accountability. Here’re some major impacts of blockchain’s future developments.

4.1- Cybersecurity

In the blockchain, all information is verified and encrypted utilizing advanced cryptography, making the technology resistant to hacks and unauthorized changes. While centralized servers can be highly susceptible to hacking, human error, corruption or data loss, using a distributed, decentralized system like blockchain will allow data storage to be more robust and safe against attacks.

4.2- Internet of Things

There’re lots of systems like doorbells, buildings etc that are powered by Internet of Things. These systems are embedded with sensors, network connectivity, and software. However, as these systems operate from a centralized location, hackers can gain access to them. Blockchain comes with the potential to address these security concerns as it decentralizes all the information, which is becoming increasingly important together with the increase in IoT capabilities.

4.3- Healthcare

While patients’ medical information can be stored in a central location, this centralization of such personal information makes it highly vulnerable. With the huge amount of private information collected by healthcare providers, it’s necessary to have a secure platform.

With the emergence of blockchain and its implementation, healthcare organizations can create a secure database to store medical records and strictly share them with patients and authorized doctors.

4.4- Unified communications

With the implementation of blockchain, it’s possible to enable safer, faster and more reliable communications. Digital or automated communication based on pre-built algorithms is already taking place in some industries.

Implementation of blockchain can shift the entire landscape to allow authorized communications that occur more freely in the automated environment, thus enhancing the reliability and safety of the communications.

4.5- Making donations

People, who donate for noble causes, are often concerned about the fact that what percentage of their donation is truly being given to charities. Implementation of blockchain can ensure that these donations reach exactly where they actually needed to go.

Already, bitcoin-based charities are developing trust through smart contracts together with online reputation systems and letting donors see where their donations actually go through a transparent and secure ledger.

5- Artificial intelligence versus blockchain

Both artificial intelligence and blockchain are major trends of today’s world and are being talked about widely. A lot of implementation of AI can be seen today across industries – from advanced computer vision and machine translation to processing and analytics of huge datasets. Companies with adequate resources are already making use of this technology to improve their operational efficiency and increase profitability.

On the other hand, the emergence of blockchain that is equipped with distributed ledgers and advanced cryptographic tools. Popularized by bitcoin, blockchain is considered as one of the biggest innovations that have the ability to disrupt technology. Let’s have a look at a small AI-blockchain comparison to get a clear idea of the differences between these two technologies.

• Blockchain in distributed and decentralized in nature as opposed to AI’s more centralized infrastructure.
• For now, AI is like a black-box solution while blockchain tends to be more transparent in every transaction processed.
• While centralized providers own and operate a significant number of AI technologies, a lot of the blockchain players publish all their codebases as open-source code which can be inspected by anyone at any given point of time.
• AI is based on a lot of probabilistic formulas, while the blockchain is more deterministic by nature.

Presently, AI startups are being increasingly acquired by tech giants. These organizations rely on massive amounts of data for training their AI agents to gain a huge competitive advantage. Centralized AI leaves room for abuse like huge surveillance of people using computer-vision-powered technology and face recognition. Also, creating solutions based on a centralized environment needs organizations to hand over the control of their data to third parties.

6- Combining AI and blockchain

The concept of AI is heavily used for denoting computers which can work in projects where the intervention of human intelligence is required. Technologies like machine learning, artificial neural networks, deep learning etc make this possible.

Blockchain stores digital information in a distributed and encrypted manner. It allows developing a highly secured database that can store all the information in a structured manner and make it publicly available. While humans can teach computer algorithms to increase their capabilities, the developers of AI aren’t able to predict an AI system’s way of thinking.

Put simply, we can develop the algorithm that’ll teach the computer to analyze massive amounts of data, we cannot predict how that algorithm will develop. If an AI system’s decisions are recorded in the blockchain, we’ll receive the database and will be able to see the decision taken by the AI system and to explain their logic. It’ll also ensure the security of the information as the information stored in the blockchain cannot be altered.

Conclusion

Despite the benefits of merging AI and blockchain, there’re some challenges related to security that need to be taken care of in order to make the integration successful. However, uniting both these progressive technologies has the potential to revolutionize the way business is conducted across the globe.

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To learn about blockchain, click here and read our another article.

The post The Change Started with Blockchain appeared first on Magnimind Academy.

In today’s competitive business landscape, consumer social norms have changed a lot and as a result, expectations too have escalated. It compels businesses to leverage their biggest asset i.e. data to rise above the competition, which is the reason behind data science being employed continuously. It has already become clear that data science benefits are simply not possible to overlook for a business or an organization that wants to grow. If you’re new to data science field, you may be wondering what are the other data science benefits for which businesses are constantly looking for data science professionals and offering them a fat pay packets.

Here, we’ve put together seven such benefits to help you get a clear understanding.

1- Surprising benefits of data science

It has become a globally accepted truth that businesses can tremendously benefit from the appropriate use of data and analytics when it comes to driving positive outcomes for expanding and improving various aspects of business. However, there’re also some other aspects where data science benefits can be leveraged to a great extent. Let’s have a look at them.

1.1- Redefined customer success

You’re probably aware that data is being heavily used to identify opportunities, modify marketing strategies, and design marketing campaigns, among others. But did you know that predictive power of data sometimes remains where businesses don’t expect it? It may sound a little different but it does and that’s one of the most surprising data science benefits.

Sometimes, information lies in data beyond what businesses can target and exercise. For, example, data-driven companies usually heavily rely on their audience performance to define marketing messaging in order to maximize results. Sometimes, the attributes are made limited by those companies based on what they consider crucial for defining customer success. In this case, crucial actionable insights can remain beyond those attributes and employing the power of data science methodologies can help more customer attributes to be leveraged and put to use.

1.2- Agricultural sector

You may not be able to believe it immediately, but the agricultural sector is yet another aspect that can reap data science benefits. Farmers use this technology to decide on the amount of fertilizer, water, and other inputs that are required to grow the best crop.

Another surprising thing is that farmers take advantage of solutions to plant the right amount of seeds so that they can gain maximum benefit out of them, even before they start growing the crops. In addition, they also depend on the weather forecast to a good extent just like us. While we use the information to decide what we’ll wear tomorrow, farmers interpret that information to understand whether it’ll affect their harvests’ quality and act accordingly.

1.3- Journalism

Have you ever heard of something like “Data-Driven Journalism” or “Data Journalism”? If you haven’t, it has become a popular trend and is considered as one of the unexpected data science benefits. Here, data heavily influences the jobs of journalists and the entire workflow is being driven by data – from data analysis and visualization to storytelling.

This data-driven approach is being used by media houses to evaluate journalists’ and their articles’ performance by considering likes, click through rates, shares on social media etc to incentivize journalists to develop content completely targeted to the readership of the website, newspaper, or magazine for which they’re writing.

1.4- Education sector

This is one of those data science benefits that may sound futuristic and a bit unusual to many people, but it isn’t. Today, educational institutes are all set to screen applicants with the help of analytic insights. There’re some universities that not only utilize analytics to evaluate students but are also inclined toward developing marketing strategies to enroll a greater number of eligible students.

Implementation of data science has made them enable to make calculated decisions about student admissions based on prospective students’ digital footprints. Advantages such as lower drop out and higher enrollment ratio, which have been made possible with the help of data science, cannot be overlooked.

1.5- Airline industry

One of the surprising data science benefits can be experienced in the airline industry. Across the globe, this industry is known to experience heavy losses. Except for a couple of service providers, most of them are struggling to maintain their operating profits and occupancy ratio.

The situation has worsened in the recent times because of certain factors like steadily increasing air fuel prices and heavy discounts offered to flyers. However, the situation has started to change as the airline companies have decided to employ data science to identify areas of improvements. With the help of this technology, airline service providers can enjoy a multitude of benefits including predicting flight delay, deciding on the class of airplanes to buy, identifying whether to take one or multiple halts or go direct etc.

1.6- Image and speech recognition

You’re most probably familiar with this thing but may not know that this is another of the many surprising data science benefits. You upload an image on a social media platform and start getting recommendations to tag your friends. This feature uses face recognition algorithm.

When it comes to speech recognition, some of the finest examples can be Google Voice, Cortana, Siri etc. Even if you’re not in a position to type something, you can use speech recognition to get the job done. All you need to do is speak out our message and it’ll be converted to text. However, speech recognition may not be able to perform accurately, at times.

1.7- Healthcare industry

You’re probably aware of healthcare providers taking the help of data science to predict patient admission rates but you’ll be surprised to know that doctors also get benefitted from this technology. For example, with the help of data science, doctors can diagnose patients quickly and more accurately, and hence, make faster decisions that play a crucial role in saving lives.

Data science has also helped greatly in the emergence of applications and wearables that can monitor patients on a constant basis to help prevent potential health problems. It also plays a crucial role in the progress of pharmaceutical research when it comes to finding a cure. Here, machine learning algorithms are used to extract and analyze biological samples from patients to develop cures.

2- What’s in it for you?

The above mentioned ones are just some of the key unconventional data science benefits. There’re lots of other benefits that data science has brought to us. In light of the above, it can be said that this is probably the best time to step into the data science field, if you haven’t already.

As modern businesses are getting flooded with data, data science professionals are experiencing a high demand across industries. It has become a universal truth that without the expertise of these professionals who can use cutting-edge technology to turn the gathered data into valuable insights, big data is of no use, and that’s why more and more companies are coming up with positions for data science professionals.

If the above read has motivated you enough, there’s a wide range of courses available in the field of data science that can help you take your career to the next level. Just keep in mind that you need to carefully select the institute from where you’ll be pursuing a course or tow to fast-track your data science career. Consider everything from curriculum, cost, and faculty to success rate and post-program guidance prior to getting enrolled to make the most out of your investment in terms of money, time. and effort.

Final thoughts

Considering the above data science benefits, it can be said that this technology is transforming the way organizations or businesses think, execute, and perform. Today, it has become an almost inherent part of one’s daily life – whether we realize it or not. When it comes to data science, many people may only think of corporate offices, where data science professionals work to deliver solutions to critical business problems. While this is completely true, it’s also a fact that data science comes with far wider reach and is being embraced across industries for a large number of unconventional advantages. Data has been there with companies for a long time. It’s the emergence of data science and advanced technologies and tools in the field that have entirely transformed the entire scenario.

So, if you’re geeky at heart, and love to work with data and face the related challenges, data science is the field you should jump into. Data science has already started transforming the global business landscape and it’ll do so in a much bigger aspect in the future, when we’ll be seeing more advanced technologies and tools in the field. So, once again, this is the ideal to time to put your best foot forward and prepare yourself to join the coveted league of data science professionals.

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To learn more about data science, click here and read our another article.

The post 7 Surprising Data Science Benefits appeared first on Magnimind Academy.