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.

.    .    .

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

The post Why do people see Data Science as part of the future? appeared first on Magnimind Academy.

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.

.   .   .

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

The post How do you become a Data Scientist without a computer science background? appeared first on Magnimind Academy.

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.

.  .  .

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

The post How a Data Scientist works? appeared first on Magnimind Academy.

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.

.  .  .

To learn more about machine learning, click here and read our another article.

The post Prepare for the future with Machine Learning appeared first on Magnimind Academy.

1- E-learning

Artificial Intelligence is changing e-learning drastically. You can use AI to personalize learning for every individual student. Rather than having the same study and training materials for each student, you can use AI-enabled hyper-personalization to create a custom learning profile of every student and then customize the study materials based on their preferred style of learning, ability, and experience. This would be a boon to educators as they can take advantage of augmented intelligence assistance that offers them an extensive variety of materials leveraging the same core curriculum and yet, let them meet the specific needs of every student. Apart from helping deliver smart content and personalized learning, you can also use artificial intelligence to automate administrative tasks like evaluating homework, grading exams, offering valuable responses to students, etc.

2- E-commerce platforms

Artificial Intelligence can help in predicting customers’ likes and preferences based on their past shopping behavior. Amazon’s artificial intelligence has been doing it for a long time and the e-commerce giant has given its sales a massive boost with AI’s predictions and suggestions. What’s impressive is that more than a third of the company’s sales are attributed to its recommendations. With the passage of time, Amazon’s algorithms have become more and more sophisticated, precise, and useful. In case you decide to join an e-commerce platform after your AI training, you can use artificial intelligence to find out your customers’ preferences and shopping behaviors with the most incredible precision.

Though Amazon hasn’t been there yet, the company is planning to put into practice a shipping system using AI that delivers products even before you put a request for them or know you require them.

3- OTT platforms

OTT (over-the-top) platforms have changed the way we consume audio or video. Over the past few years, a significant consumer shift from traditional audio/video content to home entertainment via Internet-connected devices has been observed. With the trend predicted to grow in the coming years, the war has heated up among streaming service providers like Netflix, Amazon, etc. As it’s a big challenge to acquire and retain OTT customers, especially in a highly competitive market, the leading players are using artificial intelligence to build a robust recommendation engine and content discovery mechanism that helps to deliver consistent user experience. After you learn artificial intelligence and complete your AI training, you too can take a plunge into the world of OTT platforms to experience first-hand how it could help decide the fate of a platform by helping to offer content and an experience that’s a notch above the rest.

.  .  .

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

The post What can we do with Artificial Intelligence? appeared first on Magnimind Academy.

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.

.  .  .

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

The post Learn programs that make your life easier with Magnimind Academy appeared first on Magnimind Academy.

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.

.  .  .

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

The post As a Data Scientist what we need to know in professional life? appeared first on Magnimind Academy.

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.

.  .  .

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

The post Is Being a Data Analyst a Step to Becoming a Data Scientist? appeared first on Magnimind Academy.

1- Projects come first

Having a good amount of diverse data science projects can dramatically improve the quality of your portfolio. Projects demonstrate that you have the skills and expertise to work on real-life business problems. If you’re pursuing some sort of data science program from a reputable institute, you shouldn’t have to face any problem in having projects to be solved. If you have opted for the self-learning method, you should focus on carrying out some personal data science projects to build up your portfolio.

2- Explore blogging platforms

While having your own website can surely help you develop your online presence, you should focus on getting some visibility as well. And popular blogging platforms are simply excellent for this purpose. Look for a couple of blogging platforms that get a decent amount of footfalls and come with a good tagging system that would help you reach greater audiences. Once you have your profile set up, post the successful assignments you have completed so far.

3- Have a GitHub profile

Today, Github is one of the most effective online platforms targeted at tech enthusiasts. Over the years, the platform has gained immense popularity. When you have solved a critical problem and truly want people to see the way you have done it, GitHub should be your best bet. Whether it’s a write-up or a code, drop it on the platform and share it with others. There’re lots of companies across the globe keep on looking at GitHub profiles to identify competent and genuine data science professionals.

4- Focus on social media

Having a strong presence on popular social media platforms like Twitter, LinkedIn etc can greatly help you in building a strong data science portfolio. On those platforms, you not only get chances to interact with other data science professionals and go through their inputs but can also share your insights and articles to people who may be your future employer.

Final Thoughts

When you have a strong data science portfolio, it’s up to you to opt for the way to demonstrate it to prospective employers. Depending on the data science position you’re looking it should be decided. Apart from the above tips, there’s one thing you should never overlook – the importance of practice. When people see your work and provide feedback or praise, you can rest assured of getting a bit closer to what the world calls an “expert”.

.  .  .

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

The post How do you build a Data Science portfolio? appeared first on Magnimind Academy.

1- Data scientist

In general, data scientists design and develop new processes for data production and modeling. Apart from interpreting and performing product experiments and data studies, these people are tasked with developing predictive models, prototypes, algorithms, and custom analysis. They also need to be able to work with a wide range of techniques to deal with data including machine learning and data mining. To be able to perform all these, holding advanced degrees like a master’s or Ph.D. is crucial to becoming a successful data scientist.

2- Data analyst

Data analysts sift through massive amounts of data and generate reports and visualizations that explain the insights on what the data is hiding to help companies make strategic business decisions. In general, data analysts come with an undergraduate degree in science, technology, engineering, or math major, and sometimes hold an advanced degree. These professionals also have experience in science, math, programming, databases, predictive analytics, and modeling.

3- Skills required to become a data scientist or a data analyst

3.1- Data scientist

If you want to become a data scientist, you should focus on developing the following qualifications:

• Master’s or Ph.D. in mathematics, statistics, or computer science
• Ability to work with statistical computer languages like Python, R, SQL etc
• Ability to develop and work with data architectures
• Knowledge of machine learning techniques
• Knowledge of advanced statistical concepts and techniques
• Ability to work with web services
• Ability to present/visualize data for stakeholders
• Strong verbal and written communication skills, and more

3.2- Data analyst

To become a successful data analyst, you should focus on the following:

• Degree in statistics, mathematics, or business
• Ability to work with languages like Python, R, SQL etc
• A solid combination of intellectual curiosity, analytical skills, and reporting acumen
• A strong understanding of various emerging technologies
• Ability to work in agile development methodology
• Exceptional common on Office and Excel, and more

4- Data scientist vs. data analyst

The skills required to be either of a data scientist or a data analyst overlap in some areas. For instance, both the positions require some fundamental know-how of mathematics, knowledge of software engineering, understanding of algorithms, and good communication skills. The major difference is that a data scientist comes with all the skills of a data analyst together with solid business acumen and the ability to clearly communicate their findings in comprehensible formats to business stakeholders and IT leaders in such a manner that in can influence the way a business approaches a challenge.

.  .  .

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

The post What are the basic requirements for a Data Analyst or a Data Scientist? appeared first on Magnimind Academy.