How To Become A Data Scientist Without A Degree

Becoming a data scientist is not an easy task but there are ways on how to become a data scientist without a degree. In order to become a data scientist, you need to have relevant skills, experience and training. You can also choose an online school or course in IT or other social science fields if you have a capable computer.

The data scientist will sort through mountains of data to find the hidden truths and patterns that can be used by businesses to make decisions. The role is increasing in importance with the growth of big data analytics, and there’s a clear trajectory for how to become a data scientist without a degree.

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

Mathematics

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

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

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.

data scientist high school requirements

Educational Requirements

To become a Data Scientist, you will need at least a Bachelor’s degree in Data Science, Mathematics, Statistics, or Computer Science. This means your secondary school subjects must be within these fields or those related to them. Your university education is more direct as a good number of schools offer coursework which cover Data Science in their Degree programs. Below are a few universities offering accredited programs.

Swansea University in the United Kingdom: Offers a BSc (Hons) in Computer Science. The tuition for international students is £ 16,650 and you can study for 3 years full-time or 4 years full-time with industry work. A Computer Science degree from this university will help you develop skills to identify Data solutions and measure their efficiency. Other transferable skills you will learn are team-working, communication, presentation, and problem-solving skills. The university also offers an MSc in Data Science, if you are interested in further study. The tuition is £ 18,000 for international students and you can choose to study for 1 year full-time or 2 years part-time.

Carleton University in Canada: Offers a B.C.S Honours in Computer Science. The tuition for international students is CAD 33,281.  The degree also gives provision for concentration in different aspects of Computer Science. At Carleton University, you will learn to use computing and information technology to help solve the problems that we face in business, science, and society today, as well as those to come.

Kent State University in the United States of America: Offers a Bachelor of Science degree in Computer Science. The tuition is $21,400 for international students, however, the tuition may vary if you choose to go for a part-time mode of study. The degree will teach you how to understand, design and build complex computer software systems.

how to become a data analyst 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.

You can master all these concepts for FREE at DataFlair’s Data Science Tutorials Library. Bookmark the link now and thank me later.

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.

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.

DataFlair is always ready to help you. For those who searching how to build a data science portfolio, we have recently added an article on our list.

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.

data scientist bachelor degree

Bachelor of Data Science

Harbour.Space University

Barcelona, Spain +1 More

The Data Science programme sets out to develop the skills needed to cut through the deluge of data we’re dealing with on a global scale. Students learn to cut through the noise and employ automated analytical tools to create useful knowledge out of big data. 

Bachelor Data Science (BSc)

IU International University of Applied Sciences – Online

Bad Honnef, Germany

Data is one of the greatest strengths in the digital age. With the IU Online Bachelor Data Science, you learn exactly how to deal properly with this strength. In this study programme, you will acquire mathematical and statistical knowledge, expertise in different data processing technologies and an overview of different machine learning techniques. A wide range of electives allows you to expand your knowledge in application areas and to be an expert in this field at the end of your studies. With IU you can study from the comfort of your home,100% online. 

Bachelor in Computer Science

IU International University of Applied Sciences

Bad Honnef, GermanyThe quicker business and society develop, the more multifaceted Computer Science becomes in response. IU’s Bachelor programme in Computer Science is designed to equip you with the dynamic skillset required to successfully enter this area, where you will be at the forefront of such innovative fields as Artificial Intelligence, Big Data, Digital Development, and more. Additionally, you’ll hone your professional profile through primers in related topics like Project Management and IT Law. With IU, you can study 100% online, on campus, or switch between both with their new flexible studies option.

BSc (Hons) Computing

Arden Study Centre, Berlin

Berlin, Germany

The BSc (Hons) Computing course at our Berlin campus in Germany focuses on the key areas that matter to organisations today. You’ll cover topics including data mining and analysis, cloud solutions setup, and use of CRMs such as Salesforce and Microsoft. We also provide you with one-to-one support and contextualised learning, so that you fully understand the latest developments and concepts being put into practice.

Online BSc Data Science and Business Analytics

University of London – LSE

Holborn, United Kingdom +1 More

The online BSc Data Science and Business Analytics from the University of London, one of the world’s foremost providers of distance education, prepares students to pursue new careers in data science. With academic direction from the London School of Economics and Political Science, this programme equips students with essential technical skills – including statistics, mathematics and computer science – and teaches them how to address real-world problems using data.

BSc (Hons) Computer Science and Digitisation

Berlin School Of Business & Innovation

Berlin, Germany

Passionate about computer science? BSBI is proud to offer the BSc Computer Science and Digitisation programme as part of our Faculty of Computer Science and Informatics. This three/four-year programme is perfect for anyone with a desire to pursue a career in the field of computer science and digitisation. This will enable students to develop the specialist knowledge required for a successful professional career.

B.Sc. Digital Business & Data Science

University of Europe for Applied Sciences

Hamburg, Germany +1 More

Learn how to apply cutting-edge analytics tools on Big Data in order to facilitate mission-critical management decisions and graduate into one of the leading segments of Industry 4.0. This study program is aimed specifically at students who are curious and have the creativity to imagine and the aspiration to learn and develop something new that impacts the world and changes their lives. You will learn how to develop entrepreneurial and innovative projects that combine new knowledge on digital technologies and business administration & management while responding to disruptive corporate industry challenges.

BSc in Applied Data Science

Modul University Vienna

Vienna, Austria

The emergence of big data and the continuous advancements made regarding tools and methods for analyzing this data e.g. artificial intelligence, machine learning, and blockchain, offer promising career opportunities for data science graduates. Interdisciplinary data scientists who know how to apply theoretical data science knowledge to solve real-world problems in the field of business and services are particularly in demand. Data scientists support managers in their fundamental goal of making data-driven decisions. Furthermore, ubiquitous data collection and processing capabilities enable decision-makers to adapt business strategies in a fast-changing environment. Solving complex, real-world problems in a wide variety of industry and services require specialized skills in big data processing, analysis, reporting, visualization, machine learning, web and computer programming, as well as a fundamental understanding of business management. 

Bachelor in Mathematical Engineering/Data Science

Fontys University of Applied Sciences

Eindhoven, Netherlands

This programme prepares you to become a Data Scientist/ Mathematical Engineer with a focus on applications in Logistics and Engineering. You will develop as a professional, advisor and team player. In the first two years, our lecturers will equip you with a broad, but solid mathematical basis. You will work on projects with your fellow students and you will gain practical experience during an internship. We guide you in mathematical modelling, analysis and solving more complex problems. 

BA in Criminology and Data Analytics

University of Manchester

Manchester, United Kingdom

Examine today’s fundamental questions using applied statistical and data-analytic methods.

Bachelor of Information Technology and Data Analytics

University of South Australia Online

Adelaide, Australia

Combining two highly sought after areas of technology – data analytics and information technology – this degree will help you enter the workforce, or advance your career in the revolutionary field of IT and big data. Taught by leading researchers, you’ll learn to analyse and visualise rich data sources, how to spot data trends and to generate data management strategies. Discover how data can be used to achieve organisational objectives, whether it be through cost reductions, faster and more effective decision-making processes, or the development of new products and services.

Leave a Comment