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How to Become a Data Scientist without a Degree

Last Updated on January 18, 2023 by Team College Learners

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How to Become a Data Scientist

Focusing on a person’s work experience instead of their education? Makes sense. After all, there are plenty of examples of college dropouts who went on to become wildly successful within the tech space. Think Steve Jobs, Mark Zuckerberg, and Bill Gates. And some of the data science sphere’s most influential leaders have non-technical degrees. Doug Cutting, the creator of the Hadoop framework, has a bachelor’s degree in linguistics. Tim O’Reilly, whose company O’Reilly Media is the world’s foremost publisher of data and programming resources and who was dubbed “the oracle of Silicon Valley,” has a bachelor’s in the classics.

What do Data Scientists do?

At a high-level, Data Scientists use mathematics, programming tools and techniques, software, and statistical methods to derive insights from data. In interviews with several Data Scientists, some of the things they reported doing day-to-day included:

  • Extracting salary figures from job announcements, storing, and analyzing them
  • Simulating the spread of an epidemic
  • Leveraging industrial psychology to create better HR models
  • Dissecting data to obtain risk groups for low-socioeconomic status students
  • Using data, models, and analytics to make decisions on how to sell products more effectively

How to Become a Data Scientist Without a Degree?

Data Science is the most sought after field. Since it is a recent field, there is a very low probability of candidates having an actual degree in Data Science. Therefore, in order to compensate for that, below are five key steps that will allow you to become a data scientist without any degree. Step are  –

  • Gain Necessary Prerequisite Knowledge
  • Learn Data Science
  • Explore real-time case studies
  • Work on live projects
  • Get Certified
  • Build Portfolio
  • Participate in Hackathons

1. Gain Necessary Prerequisite Knowledge

Data Science is a vast field that stems from multiple disciplines of Mathematics, Computer Science and Statistics. There are various books through which you can acquire the knowledge of these subjects. Some of the important concepts of statistics that are useful for Data Science can be learned from DataFlair’s detailed guide on Statistics for Data Science.

Furthermore, you can gain knowledge about mathematical concepts like calculus, linear algebra, probability, discrete math etc. For attaining the knowledge of the basic concepts of Computer Science, you can learn Python and R, the two most popular languages in the field of Data Science.

2. Learn Data Science

Now, the second important step for becoming a data scientist is to start learning data science. What essentially is Data Science? There are various components in Data Science like data extraction, data transformation, cleaning, visualization, and prediction. Each of these components requires a separate mastery. Another important aspect of data science is storytelling. In order to acquire these skills, you must be well versed with various tools. For example, for visualization you must know tools like matplotlib, seaborn, ggplot2 etc.

Another important aspect of Data Science is Machine Learning. While there are several blackbox tools like scikit-learn and TensorFlow that allow you to implement machine learning algorithms through condensed functions, it is important for you to at least know basic algorithms like linear regression, logistic regression, k-means clustering, etc.

3. Explore real-time Case Studies

Once you have got a good grasp on Data science and the various tools used in Data Science process, you should research and read about different case studies of how big enterprises are using data science to help them improve the organization and its profits.

Exploring more case studies will help you in finding out problems to solve, and how to approach towards solving a particular problem.

DataFlair has numerous Data Science Case Studies for you. Read them and grasp a good knowledge of this field.

4. Work on live projects

Data science is more of a practical field, in which to attain the true knowledge you have to actually solve real problems by working on live projects. You will get hands-on experience in solving real-world problems and this will improve your Data Science skills. Getting data science job as a fresher can be tedious, so make sure you work on good live projects and enhance your skills.

5. Get Certified

This step is optional, but getting a certificate will only improve your chances of becoming a Data scientist. An official certification will showcase your skills in Data Science that you have implemented. Some of the companies that offer certifications in Data Science are Microsoft, Cloudera, SAS, etc. Here is a list of some of these certifications –

  • SAS Certified Data Scientist
  • Cloudera Certified Associate: Spark and Hadoop Developer Certification
  • Microsoft Certified Azure Data Scientist Associate

6. Build a Portfolio

Your portfolio reflects your work that is performed in the field of Data Science. You can enrich your portfolio through several Data Science projects. Through building your presence on websites like Github, Linkedin, Kaggle, Tableau Public, etc. you can draw attention of many job recruiters.

You can craft your portfolio based on the type of job. For example, a job role demanding machine learning will require you to have a portfolio that reflects projects involving machine learning algorithms. Another type of portfolio is the data analysis portfolio through which you can demonstrate data transformation, cleaning, visualization, etc. The third type of portfolio is storytelling portfolio which is a comprehensive project that translates a business problem into data science.

7. Participate in Hackathons

The best way to learn Data Science is by doing it. There are various online platforms like Kaggle that allow active participation in data science competitions. Through these competitions, you can gain experience that will be appended to your resume and it will augment your portfolio. Through intensive data cleaning, transformation, analysis, visualization, you can have an in-depth idea of implementing data science in real-life scenarios.

You can build your expertise by gaining experience through solving data science problems of varying degrees.

The Skills Data Scientists Need


The amount of mathematical skill required to be an effective Data Scientist is hotly debated. Some argue that deep mathematical knowledge is required, while others argue that since most statistical analyses are carried out via programming libraries like NumPy anyway, math knowledge is less important than you’d think. DataScienceWeekly offers this list of the minimum mathematical concepts you should be comfortable with in order to be an successful Data Scientist:

  • Linear algebra, including multivariate calculus. You can learn Linear Algebra for free at Khan Academy.
  • Regression, including the ability to handle both linear and nonlinear models appropriately. You can learn about Linear Regression at Coursera.
  • Probability theory, including Bayes’ Law and Central Limit Theorem. You can learn about probability and data at Coursera.
  • Numerical analysis, including time series analysis and forecasting. You can learn about time series forecasting at Udacity.
  • Core machine learning methods, including clustering, decision trees, and k-NN. You can learn about machine learning for free via Stanford University’s course on Coursera.

Programming Tools and Techniques

The ability to program helps data scientists in a variety of ways. They can write scripts to automate one of the most time-consuming tasks in data science: cleaning and preparing data for analysis. They can write scripts to transform data from one format to another, such as transforming the result of an SQL query into a neatly formatted CSV report, or the opposite, persisting CSV data to a relational database. In most cases, data analysis is carried out using purpose-built libraries that abstract away many of the repetitive or complex calculations involved, such as pandas. Matplotlib can be used to visualize the results of a data analysis.

Machine learning

Machine learning is finding increasing application in the world of data science. Machine learning is the means by which computers can learn (and improve at) tasks without being explicitly programmed. Machine learning techniques can be used to make decisions and predictions based on data, and has many applications in the field of data science.


SQL, or Structured Query Language, is a language used for interacting with relational databases. Worldwide, the majority of data is stored in relational databases. To work with this data, you need to be able to query the database to extract the data you need. This is why understanding the fundamentals of SQL is essential as a Data Scientist.


Software packages used by Data Scientists include Tableau, Microsoft Excel, RapidMiner, and KNIME. You may be surprised to see Excel on this list, but CSV reports are sometimes the only common language between Data Scientists and business at large (in 2016, Excel was almost as commonly used as SQL among Data Scientists).

Statistical Methods

A strong understanding of statistics is probably the most important skillset for Data Scientists. Simply put, all of the programming, mathematical, and software skills in the world will not help you if you don’t understand how to analyze and report on statistics accurately and fairly.

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