What can data science do in the future?

Data science is almost an indefinite pool of diverse data operations by leveraging the power of which a data scientist should be able to accomplish the following in the future.

• AI will become omnipresent: Most of us have seen the power of AI applications in the fields of automation and robotics to some extent. But in the next few years, data science professionals are expected to take AI to such a level that robots will be able to do almost everything – from cleaning the houses to running a business. Probably you’ve seen that Google Assistant has already reached a level that it can make phone calls and book reservations in restaurants and in the upcoming future, its activities will become more specialized and broaden even more.
• Advanced personalization: With billions of users across the globe using smartphones, the amount of data being generated seems to be even more massive in the next few years. Therefore, businesses, with the help of data scientists, will be able to maximize the value of data in an even better way that’ll help them address their user-base more thoroughly.
• More accurate predictions: We can expect big data analytics tools to become so advanced that data science professionals will be able to help in real-time decision making. As a result, many key functions could be predicted with much higher accuracy than they’re being done today.

Apart from the above, we can expect to see more specialized career paths evolve. With advancements in the field, the overall status of data literacy will likely to improve across the workforce where employees other than data science professionals will obtain a better understanding of the usage of data. And thus, the future of data scientists would probably become even more specialized, handling the most complex and business-critical challenges which will help their companies become even more successful in their respective fields.

Conclusion

Today, it can be safely said that data scientists will have a prominent future and the field will stay for years to come. If you’re thinking of pursuing a data scientist career, perhaps this is the best time to start your journey. Magnimind Academy’s data science bootcamp in Silicon Valley helps students to become future-proof data scientists with unique combination skills which will be always be in great demand.

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1- Guiding actions based on trends

One of the key responsibilities of a data scientist is to examine and explore the data captured by the organization. Once these processes are done, he/she can recommend different types of actions which bring a huge scope of improvement in the business performance of the company. And after these improvements are made, it can leave a significant impact on the organization in terms of increased profit.

2- Informed decision making

With the help of a data scientist, it has become possible for business owners to predict effective measures and different trends for the success of their businesses. One of the biggest data science benefits is that it has eliminated the possibilities of upper-level risks. By reviewing different types of models created by data scientists based on already existing data, business owners can easily understand which road will lead them to success.

3- Testing the decisions

Once you’ve implemented the changes based on the insights discovered by a data scientist, it’s time to observe how these changes are impacting your business. And this is exactly where the expertise of a data scientist becomes evident again. He/she would be able to measure the key metrics which are related to those changes and quantify their true impact.

4- Real-time data

Once there was a time when marketers used to collect the info about their consumers in bulk after every campaign and analyze that information to track the progress of the campaign. But the emergence of data science has opened up a whole new field of digital marketing. Now you can build your present and future digital marketing campaigns based on real-time data, which means you don’t need to analyze distant past behavior anymore. Instead, you can focus on the present market patterns to make your campaigns highly effective. A data scientist can tell you everything about your target market trends, customer response, their buying patterns, the effectiveness of timing, and much more, helping you target your consumer base at the right time.

Parting Thoughts

These are only some of the major data science benefits that any business would be able to experience by hiring a data scientist. It’s also safe to say that the importance of these professionals will only increase over time, thanks to the increasingly connected world. If you’re an aspiring data scientist and looking for a great start, this data science bootcamp in Silicon Valley offered by Magnimind Academy would be worth checking out.

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1- The three main data science skill sets you need

Even if you don’t have a computer science background, you will need the three main data science skill sets namely programming, statistics, and business knowledge if you aim to have a successful data scientist career.

If you plan to become a data scientist, you’ll need to use programming skills to handle data at scale that can fill terabytes of space. You’ll also need a solid grasp on statistics and mathematics to evaluate patterns in data and manipulate it using different methods. Understanding business fundamentals is an equally important skill to ensure you’re capable of communicating your findings to the concerned teams or management people and encourage them to make informed decisions based on such data-driven insights.

Though you’ll need a diverse skill set to excel in the field of data science, you don’t need to worry as most data scientists won’t have picked up all of these skills in an academic environment. This indicates there’s often a lot of self-learning involved in the process, which would be advantageous for you, especially if you don’t have a computer science or statistics/math background.

You should remember that if you can prove through project work that you’ve got serious data science skills, it won’t matter whether you acquired them on your own, through a formal degree program, or via a data science bootcamp in Silicon Valley.

2- The fast-track to becoming a data scientist

Online and offline bootcamps typically offer a mentor-guided curriculum tailored to get you working with data from day one. With industry experts and experienced data scientists as mentors, who use real-world data to teach you, you’ll get your hands on real-world data from the beginning of the program. You’ll even get hands-on, project-focused classes that prepare you for data science employment by the time you end the training.

Unlike lengthy traditional degree courses, these bootcamps offer extremely targeted learning that demands you stay committed to your studies and invest 15-20 hours (or even more, at times!) regularly right from the beginning. With a hands-on learning approach, these bootcamps make you work with real data sets to analyze interesting problems and even give you additional opportunities for guided real-life projects.

When you join a leading data science bootcamp in Silicon Valley, you’ll not only get real-world experts as your mentors but even get the chance to learn and grow via peer interaction. When working on group projects with other aspiring data scientists, you’ll be able to ask questions, brainstorm to find solutions, and even learn from your peers.

If you plan to become a data scientist but don’t have a computer science background, find a data science bootcamp in Silicon Valley to make your dream come true.

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Before we delve deeper into the subject of this post, let’s understand what data science is.

With data at its core, data science works involve multiple disciplines that include technology, algorithm development, and data inference. A data scientist is someone who works with big data extensively utilizing his/her expertise in multiple disciplines and analyzes it to generate business value. And data scientists are often assumed to perform multiple roles – from data miner, data analyst, and software engineer to manager, business communicator, and a key person in any data-driven business that helps the management in decision-making.

However, the responsibilities of a data scientist always understood easily and are often used to describe a broad range of data-related work. If you’re planning to pursue a data scientist career, it’s important that you develop a clear understanding of the working process of a data scientist.

The working process of a data scientist

Though the working methods of data scientists may vary a bit based on their approaches and project goals, they generally follow these steps.

• Acquisition, collection, as well as, storage of data
• Goal identification
• Access, absorb, and integrate data
• Data munging/wrangling (processing and cleaning)
• EDA (exploratory data analysis)
• Choosing algorithms and one or multiple potential models
• Applying data science techniques and methods (artificial intelligence, machine learning, statistical modeling etc)
• Validation and tuning of results
• Presenting and/or communicating final results

Based on the final results, business stakeholders make business decisions and/or implement changes.

Now, let’s have a look at the typical deliverables and goals accomplished by a data scientist using the above process.

• Recommendations (think Netflix and Amazon recommendations)
• Prediction of a value based on provided inputs
• Grouping and pattern detection
• Actionable insights
• Recognition of text, image, audio, video etc
• Segmentation (think demographic-based marketing)
• Forecasts (think revenue and sales)
• Automated decision-making and processes

Each of the above is dedicated toward solving a certain problem and/or addressing a certain goal. While these may not seem like serious issues initially, in reality, these are the pillars of the success of any data-driven business.

By reading till now, if you feel interested to kick-start your data science career and want to take a relatively affordable and quicker pathway, the popular data science bootcamp in Silicon Valley offered by Magnimind Academy is what you should opt for.

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1- Major reasons to become a machine learning professional

Here’re some major forecasts about machine learning’s future.

1.1- Enhanced personalization

Personalized recommendations can entice users to complete certain actions. With the help of machine learning personalization algorithms, the information in a data can be synthesized to make appropriate conclusions like a user’s interests. For instance, on an e-commerce website, it can be deduced from a user’s browsing activity that he/she is looking for a garden chair.

1.2- Better cognitive services

Cognitive services offered by machine learning professionals all developers to incorporate intelligent capabilities into their applications. Developer can empower those applications to perform various duties including speech detection, vision recognition, speech understanding. As machine learning is evolving continuously, we can expect to see the emergence of highly intelligent applications which can increasingly see, speak, hear, and even reason with surroundings.

1.3- Fraud prevention

Machine learning professionals can analyze transactions of an e-commerce website and the machine will pick out the fraud unlike rules-based, traditional system. In the future, we’re likely to witness machine learning technology becoming more sophisticated and the machines will modify, through self-learning, to prevent fraud.

1.4- Better use of quantum computing

The machine learning spectrum can be transformed through implementation of quantum machine learning algorithms. If machine learning can be integrated into quantum computers, it could result in faster processing of data that could dramatically accelerate the ability of synthesizing information and derive insights – what the future probably holds for us.

Though many people consider machine learning to be in its nascent stage, its future is clearly bright and so is the future of machine learning professionals.

2- Key career opportunities as a machine learning professional

Let’s have a look the major job opportunities as a machine learning professional.

2.1- Data scientist

Professionals, who extract meaning from a massive amount of data and analyze and interpret it.

2.2- Machine learning engineer

Programmers, who develop the machines and systems that can learn and apply the acquired knowledge without requiring any specific direction or lead.

2.3- Deep learning engineer

These professionals specialize in developing tasks related to AI with the help of deep learning platforms.

3- Roadmap to become a machine learning professional

As you’re aware of the reach of machine learning, let’s discuss how you can become a machine learning professional. Though this field involves a broad range of skill sets, there’re plenty of resources like Magnimind Academy that would help you master all these skills. They offer some valuable machine learning mini bootcamps for students with different skill sets. And if you’re interested in becoming a data scientist, you can always join their full-fledged data science bootcamp in Silicon Valley to move toward that direction.

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Key reasons to join Magnimind Academy

First of all, data scientists need solid skills in programming, statistics, and computer science. Regardless of your major, you’ve to focus on the courses that would help you develop a solid understanding of these. For instance, Python – the full-fledged programming language is a perfect fit when it comes to implementing algorithms. Almost every newer data scientist gravitates toward this language because of its simplicity and ease of use. It also offers a programming platform which comes with high compatibility with data science, machine learning, and many other emerging technologies. In short, Python won’t let you bog down with complex programming requirements by allowing you perform tasks in a hassle-free manner. If you’re new to this language, the Data Analysis with Python mini bootcamp should be the perfect course for you while the Introduction to Python mini bootcamp would be ideal for students who’ve got some fundamental understanding of Python.

Next comes SQL or Structured Query Language which is considered to be one of the most sought-after skills in data science, together with Python and R. One of the key advantages of SQL is that when you’re working with data, it’s directly accessed and that speeds up workflow executions considerably. From a data science viewpoint, SQL can be used for both machine learning and data pre-processing purposes. If you’re interested in obtaining more knowledge about the language, we’d strongly suggest you to join the SQL for Data Science mini bootcamp offered by Magnimind.

Lastly but perhaps the biggest reason of joining Magnimind is its offering of completely immersive training programs in form of bootcamps. They’ve designed these training programs for students coming from a diverse range of technical backgrounds. To excel in their field, data scientists must need a variety of tricks and tools to process Big Data and their bootcamp is fully focused on the details. The concepts supporting various technologies are greatly explained by the instructors while allowing students to obtain valuable, hands-on experience. And when it comes to career preparation, Magnimind places a great emphasis on it, including job interviews and hiring fairs. And the best part is their data science bootcamp in Silicon Valley fully embodies the techno-meritocracy spirit of the technology hub, where anybody with a love of data science can hack his/her way to competency.

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1- Python

If you were to deliver Oscars to programming languages, the most-deserving candidate would have been Python. It has been the fastest-growing and most used major programming language today. Thanks to its versatility and user-friendliness, Python can be used for almost all the steps involved in data science processes. The massive libraries of Python, which are extremely easy to learn even for a beginner in the field of data science, are used for data manipulation. Apart from being an independent platform and an open source language, Python also easily integrates with any existing infrastructure, which you can then use to solve the most complex problems in data science. Python is used by many banks for crunching data while several institutions use it for data visualization and processing. Even weather forecast companies like ForecastWatch use and leverage Python.

2- R

Once, this open source language was the primary language for data science. Though it has been replaced by Python as the leading programming language that data scientists need to know, it’s still not far behind Python. The roots of R are in statistics, and it’s still extremely popular with statisticians. Be it statisticians, data scientists, or analysts – anyone wanting to make sense of data can use R for data visualization, statistical analysis, and predictive modeling. Thanks to its open interfaces, R can easily integrate with other applications and systems.

3- Machine Learning and Artificial Intelligence (AI)

If you’re opting for a data scientist career, you should be familiar with ML (machine learning) and AI. Since the field of data science needs the application of skills in different areas of machine learning, you should learn and hone your skills in various machine learning areas and techniques like reinforcement learning, supervised machine learning, neural networks, adversarial learning, logistic regression decision trees, etc. Knowing these will help you to solve different data science problems that are based on forecasts of key organizational outcomes.

Whether you’re doing a full-time course or an intensive short-term data science bootcamp in Silicon Valley, you should also learn (in addition to the above) Hadoop, SQL, and Apache Spark. Apart from the technical skills, your professional life would also demand you to be an expert in some non-technical skills. These include having:

• Intellectual curiosity to ask questions that help you discover and prepare data;
• Business acumen to have a good knowledge of the industry you’re working in and knowing what business problems you’re going to help the company solve;
• Good communication skills to fluently and clearly translate your technical findings to a non-technical team; and
• A collaborative attitude to work in a team, which could involve product managers and designers, server and client software developers, company executives, marketers, etc.

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1- Data Analyst vs. Data Scientist

The job of a data analyst is to collect, process, and apply statistical algorithms to structured data to derive benefits and help in informed decision making.

Though the goal of a data scientist is similar, a data scientist also possesses robust skills for handling large amounts of unstructured data, potentially processing them in almost real-time. If you’re a data scientist, you’ll find out important information and have the ability to clean and process it, which is then followed by running advanced algorithms on the data, which could have originated from an extensive range of sources. Data scientists also have strong business acumen, intellectual curiosity, storytelling and visualization skills, and a positive attitude toward teamwork.

2- Data Analyst to Data Scientist – The Road to Transition

Though most data analysts will have a good foundation, it would still take them some time to become a data scientist. This could be from a couple of weeks to some years depending on whether you opt for a data science bootcamp in Silicon Valley or take the arduous route of full-time degrees and programs. A data analyst would need to invest time, effort, and money to develop skills to apply cutting-edge approaches comprehensively on awkward structures and/or large/unstructured data sets.

3- Data Analyst to Data Scientist – Skills Needed for the Transition

Answering this question is difficult as sophisticated data science projects may have an intricate pipeline with several elements, and mastering all at the same time is impossible. Still, you should hone your skills (as you may have already worked with these technologies as a data analyst) or at least, touch upon a reasonable part of these:

• Data Science languages: Python and R
• Distributed computing: Spark and Hadoop
• Relational databases: PostgreSQL and MySQL
• Non-relational databases: MongoDB
• Machine learning models: Boosted Trees SVM (Support Vector Machines), Regression, NNs (Neural Networks)
• Graph databases: GraphX and Neo4J
• Cloud: AWS/GCP/Azure
• Data Visualization and Webapps: RShiny and D3
• API Interaction: Rest and OAuth
• Specialist domains: OCR (optical character recognition), NLP (Natural language processing), and Computer Vision

To fast-track your transition (from a data analyst to data scientist), you can choose a data science bootcamp in Silicon Valley that has industry leaders as its instructors. With projects, hands-on assignments, and mentorship from your instructors, such a bootcamp will get you trained in the most in-demand skills, tools, and expertise essential to think and work as a modern data scientist. Thus, you can be job-hunt ready faster than waiting for years to complete traditional or full-time classroom courses to get a job in the field of data science.

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A significant number of newcomers in data science tend to spend a huge amount of time to develop theoretical knowledge and earn certifications only. While theoretical knowledge is certainly required to become a good data science professional, recruiters don’t put much emphasis on certifications only. Instead, they tend to evaluate the potential of a candidate by going through his/her work.

As a data science professional, you may have worked on lots of crucial problems, but if you fail to present them to the recruiters, getting a good job in the field may become even more difficult. And this is exactly where data science projects come to your rescue. They help you demonstrate your data science skills to prospective employers. Therefore, it’s important to pick your data science projects carefully. The process of picking up data science projects can be overwhelming, especially when you’re planning to mention them in your CV. In this post, we’ve outlined five top data science projects to help you in your endeavor.

1- Movie Recommendation Engine

This is one of the most common data science projects for everyone in the field. Every successful data science professional has developed at least one recommendation engine in the entire career. Personalized recommendation engines are considered highly effective when it comes to demonstrating data science skills.

Problem: To analyze the Movie Lens dataset in order to comprehend patterns and trends that will help the system to recommend new movies to users.

2- Walmart Sales forecasting

Retail is one of those industries where data science is being used extensively and thus, it’s important to have worked on at least one project related to it. There’re a plethora of tasks including inventory management, product placement, product bundling, customized offers etc are being handled efficiently utilizing different types of data science techniques.

Problem: Predict the department-wise sales of the store.

3- Text Mining

A text mining project in your portfolio may dramatically improve your chances of being hired as a data science professional. It involves data mining and advanced analytics that can prove your skills as a professional. Text mining is heavily used in social media monitoring as it helps to obtain an overview of a broader public opinion on specific topics.

Problem: Classify a set of documents according to specific labels.

4- Urban Sound Classification

This is one of those data science projects that will help you demonstrate skills in machine learning. This project is designed to getting you introduced to audio processing in the context of the usual classification scenario.

Problem: Classify the kind of sound from an audio.

5- Chicago Crime Analysis

Law enforcement agencies take help of data science techniques to understand the actual reasons behind crimes and thus, to be able to prevent their repetitions. While the problem may seem easy, data management is the key here.

Problem: Predict the type of crime.

Parting Thoughts

If you’re a complete beginner in the data science field, it’s important to select data science projects with limited variables and data. The above ones may seem a little challenging, but they should be fun to do.

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When it comes to the structure of big data, you can consider it a collection of data values, the relationships between them together with the operations or functions which can be applied to that data.

These days, lots of resources (social media platforms being the number one) have become available to companies from where they can capture massive amounts of data. Now, this captured data is used by enterprises to develop a better understanding and closer relationships with their target customers. It’s important to understand that every new customer action essentially creates a more complete picture of the customer, helping organizations achieve a more detailed understanding of their ideal customers. Therefore, it can be easily imagined why companies across the globe are striving to leverage big data. Put simply, big data comes with the potential that can redefine a business, and organizations, which succeed in analyzing big data effectively, stand a huge chance to become global leaders in the business domain.

Structures of big data

Big data structures can be divided into three categories – structured, unstructured, and semi-structured. Let’s have a look at them in detail.

1- Structured data

It’s the data which follows a pre-defined format and thus, is straightforward to analyze. It conforms to a tabular format together with relationships between different rows and columns. You can think of SQL databases as a common example. Structured data relies on how data could be stored, processed, as well as, accessed. It’s considered the most “traditional” type of data storage.

2- Unstructured data

This type of big data comes with unknown form and cannot be stored in traditional ways and cannot be analyzed unless it’s transformed into a structured format. You can think of multimedia content like audios, videos, images as examples of unstructured data. It’s important to understand that these days, unstructured data is growing faster than other types of big data.

3- Semi-structured data

It’s a type of big data that doesn’t conform with a formal structure of data models. But it comes with some kinds of organizational tags or other markers that help to separate semantic elements, as well as, enforce hierarchies of fields and records within that data. You can think of JSON documents or XML files as this type of big data. The reason behind the existence of this category is semi-structured data is significantly easier to analyze than unstructured data. A significant number of big data solutions and tools come with the ability of reading and processing XML files or JASON documents, reducing the complexity of the analyzing process.

Conclusion

While data analytics aren’t new, the emergence of big data has dramatically changed the nature of work. It’s important for businesses looking to make most out of the big data to try to adopt advanced tools and technologies to keep up with the pace at which the data is growing.

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