How To Become A Data Scientist

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Daniel Keys Moran says you cannot have information without data, but you can have data without information. You will get counsel from many people on how to become a data scientist, but you need to be wary of the actions you take after the advice. It is surprising to know that there are Paris, Texas and Paris, France. Both are lovely locations to visit, but if you needed the directions to one place and got the wrong one, you will not be amused. Similarly, many people have various interpretations of what a data scientist is, and what they do. Data science, commonly called data-driven science, is an interdisciplinary field of systems, scientific methods, and processes that help to extract insights and knowledge in various forms, either unstructured or structured. 

Facebook and LinkedIn coined data scientist as a new job title. This article is a guide on how you can become one.

  1. Learn more about factorizing matrices

Enroll for a course in Numerical Analysis or Computational Linear Algebra, also referred to as Matrix Analysis, Matrix Computations, or Applied Linear Algebra. You need to understand how to decompose algorithms, since many applications are critical in the course. Many antique tools such as MATLAB are not fit for the job, and eig () cannot run on Big Data. Matrix computations like the ones in Apache Mahout have attempted to fill the gap, but it is important to know how to use BLAS or LAPACK so that you utilize them correctly. Many numeric courses use undergraduate calculus and algebra so you can excel in prerequisites. There are plenty of  numerical analysis resources for you to study on your own.

  1. Learn how distributed computing works

Data analysts should understand the basics of working with Linux cluster, and how to make scalable algorithms. This is important if you want experience with large sets of data. You can replace the ancient Connection and Crays Machines with inexpensive cloud examples. If you need to experience the best in your hardware as you work, use the fully powered multicore. Distributed computing is not a typical Machine Learning track, but there are reliable materials such as Parallel Programming or Distributed Computing Systems. After you have studied the essentials of distributed systems and networking, it is important to focus on other distributed databases. Many developers and data analysts anticipate the distributed databases will soon convert to universal since data deluge will hit the limits of scaling.

  1. Learn statistical analysis

Start learning to code using R, which works in a unique way. Numbers like 2 are two-element vectors of length 2. The reason why you can multiply any number by a vector is that the number expands by the same length of the vector, and you can multiply the vectors component-wise. If you have experienced Python or C++, R is even better since you can do complex programs. Use resources that Cosma Shalizi compiled on computational statistics. To enjoy a career in data analysis, go to a field where causality principles and quantitative techniques are inevitable. You could also work in cancer research, or a narrow domain.

  1. Learn more about optimization

This is an essential subject if you need to understand Signal Processing and Machine Learning algorithms. Machine learning helps you to make predictions about data. You can draw important guidance from signal processing, information theory, and computer science. Optimization aids data analysts in making a decision based on knowledge from various areas. It uses classification or regression methods such as logistic regression, least squares regression, support vector machines, and robust regression. It is not easy to work with data in its raw form, and you need to transform it by passing the data through a neural network.

  1. Question everything

A data scientist has to answer some interesting questions using a code and actual data. An example might be “can I predict what time my flight will be?” Such a question requires an analytical mindset. You can develop such a mindset by reading news articles such as whether sugar is good for you, or whether you gain anything by running. Think about how the writers reach their conclusions using the data they have, how you may conduct a study to do further investigations or the kind of questions you could ask if you had similar data. Read articles such as gun deaths in U.S. that have actual data. Download the data, and analyze it using tools such as Excel. Check whether you can develop patterns, and ascertain if the data you have supports the conclusions in the article. Visit credible sites that have data-driven articles such as VoxFiveThirtyEightThe Intercept, and the New York Times. After you have analyzed the articles, reflect on whether you have enjoyed answering questions. The road to becoming a successful data scientist is long, and you need to be passionate about the career to become successful. Data scientists know how to come up with questions and answer them using data analysis tools, and mathematical models. If you find no fun in asking questions and compiling data, try to think of things you enjoy, and find any overlaps between data therein. You could enjoy compiling stock market data.

  1. Learn the basics

Once you know how to come up with various questions, you are a step closer to learning technical skills with which to answer them. It is advisable to learn Python since it has consistent syntax, and it is the best for beginners. Moreover, it is versatile enough to help you perform complex data analysis and machine learning. Many people worry about the choice of languages, but you need to keep in mind that:

  • Building and sharing projects are some of the necessary activities of a data scientist. Learning this way gives you a head start.

  • It is better to learn the concepts of learning syntax.

  • Data science entails answering questions, and adding value to your business. It is not entirely about tools.

You should endeavor to learn the technical aspects of data analysis, and you will understand data science tools. Learn how to build projects through using sites such as:

The key is to understand the basics, start answering questions, and learn how to set analytical questions. This helps in your learning, and building your portfolio.  

  1. Build projects

Once you understand the basics of coding, start building various projects that can help you display your data science skills. They don’t have to be complicated projects. For instance, you can find patterns in Super Bowl winners. The key is to analyze interesting datasets, query any data, then answer all questions through coding. As you build projects, recall that:

  • All data scientists start somewhere. Though you may feel as if you are not doing enough, your small steps are worthwhile.

  • Linear regression is the most common technique in machine learning

  • Most of the work in data science is data cleaning

Other than helping you understand data science and practice, building projects also help you to build a portfolio that you can show to prospective employers. There are many prospective employers on who await your skills in data science. Access the site and work on the available projects. You can be sure it is rewarding.

It is not easy to understand data science. The key is to remain motivated and to enjoy your work. If you consistently build projects and share them, you show your expertise. Though you may not have the exact data map to your career, following these guidelines will take you further than you imagined.

If you have any more tips that can guide one to be successful in data science, share them in the comments below!


İlan Edilmiş 16 Ağustos, 2017


Freelance Journalist & Reporter

Alice is a Community Correspondent at She drifts between London & Sydney.

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