How to Learn Data Science (A Complete Guide)

By Indeed Editorial Team

Updated November 24, 2022

Published September 29, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you have an analytical mind and love working with computers, a career in data science may be an excellent choice. These professionals often work for private companies and governments agencies to find, clean, and organize their data. Understanding how to learn and develop relevant data science skills can help you excel in this role and become a more employable candidate. In this article, we explain what data science is, explore how to learn data science, discuss the relevant skills you can develop to excel in this role, and share how to become a data scientist.

What is data science?

Data science is an interdisciplinary field that uses scientific approaches, procedures, technologies, and systems to extract information and insights from organized and unstructured data. The field of data science includes preparing data for analysis, creating data science issues, analyzing data, generating data-driven solutions, and presenting findings to guide high-level conclusions across a wide range of applications. It requires expertise in computer science, mathematics, information science, statistics, information visualization, data integration, web development, complex systems, communication, and finance.

Related: Everything You Need To Know About Data Science as a Career

How to learn data science

Here is a step-by-step guide to follow when discovering how to learn data science:

1. Figure out what you need to learn

Before learning data science, you first need to understand what data science is and what the role entails. There is a lot to learn, so developing an understanding of the topics to cover can help you get started. To learn data science, you need a knowledge of a programming language and the ability to extract information from data in that language. While it requires mathematical fluency to excel in data science, a fundamental grasp of mathematics is all you need to get started. You can then begin building knowledge on those more specialized subjects and branches of math, like statistics or information science.

2. Learn by practising

Learning about neural networks, machine learning, image recognition, and other innovative approaches is critical. Working on projects is one of the easiest ways to learn data science. This allows you to develop instantly practical and helpful skills because real-world data scientists must manage data science projects from beginning to end. The majority of their work requires fundamentals, such as data cleaning, organizing, and management. By practising what you learn during your education regularly, you can master those skills and become more proficient with other aspects of data science.

Related: What's the Difference Between Hard Skills and Soft Skills

3. Communicate your findings to others

Data scientists must continuously communicate the findings of their analyses to others. Being able to do this successfully can be the difference between being a good data scientist and a great one. Data analysis is generally only useful in a business environment if you can persuade others in your organization to act on what you discovered, which requires how to communicate data.

To improve your ability to communicate complicated ideas effectively, consider:

  • Creating a blog and share the findings of your data analysis or send in a proposal and write for blogs that embrace data-related material

  • Teaching data science ideas to your less tech-savvy friends and relatives

  • Making an effort to speak at meetings and other tech-related groups

  • Using tech spaces to host and share all of your research

  • Participating in online data learning communities

4. Learn from your colleagues

In data science, collaboration is also highly essential in the workplace. Data scientists frequently operate as part of a team, and data scientists in smaller organizations generally collaborate with other teams to resolve specific challenges. Data scientists usually move between teams as they work on resolving data issues for various parts of the organization. Cooperation may be more crucial for data scientists than employees in other fields.

5. Build algorithms from scratch

We recommend attempting to build an algorithm from scratch once you understand how it works in practice. It allows you to understand the underlying arithmetic and other mechanisms that enable it to function. You certainly need to grasp the concept behind it as well.

Learning in this manner is considerably more intuitive than attempting to understand the theory and then applying it. Theory becomes extremely important as your data science career progresses. You provide value by applying the appropriate algorithm to the problem. The theory connected with the algorithm considerably aids this procedure.

Skills you need to develop

Successful data science experts have both hard skills, such as technical understanding, and soft skills, which include excellent communication. Having both sorts of skills can demonstrate to companies that you are a well-rounded candidate. Many data science experts possess the following hard and soft skills:

Hard skills

Data scientists use a variety of software tools and computer languages, which need hard skills to operate. Here are some of the hard skills that can help you excel as a data scientist:

  • Data visualization: Data visualization is the process of converting raw data into easily understandable forms. SAS, the R libraries, and Tableau are data visualization tools used by data scientists.

  • Programming skills: Data scientists must have a strong foundation in programming languages such as Python and C++. These languages are helpful for data analysis and the development of automated systems.

  • Statistics: Data scientists must have a solid understanding of mathematics and statistics. This ability enables data scientists to acquire a more in-depth understanding of the data they're studying.

  • Machine learning: Although optional, machine learning can distinguish you from other data scientists. This expertise includes automating the creation of analytical models and might make you more appealing to organizations that work with huge volumes of data.

Read more: Technical Skills: Definition and Examples

Soft skills

Data scientists benefit from soft skills to be productive professionals and team members. These skills include:

  • Analytical skills: Improving your analytical skills might assist you in identifying patterns and trends in data. Analytical skills help you pay close attention to detail.

  • Critical thinking: Improving your critical thinking abilities will assist you in evaluating facts and drawing appropriate judgements.

  • Communication skills: Data scientists rely on their writing and speaking abilities to make complicated information accessible and understandable. Strong reading and listening abilities can also aid in the analysis and comprehension of criticism.

  • Adaptability: Adapting to new tools, methods, and information can assist you in keeping your data science expertise current and relevant.

  • Teamwork: As a data science expert, you may collaborate with multiple corporate departments to enhance their operations. Strong collaboration and coordination abilities can aid in the achievement of corporate objectives.

Related: A Guide To Soft Skills

How to become a data scientist

To become a data scientist, you can follow these steps:

1. Get a degree

To gain the level of knowledge required to be a data scientist, you need a very solid educational background. A bachelor's degree in computer science, social sciences, physical sciences, or statistics is relevant when starting a data science career. Mathematics and statistics are the most frequent subjects of study, followed by computer science and engineering.

2. Consider an advanced degree

Data scientists can benefit from pursuing advanced education like a master's or Ph.D. Consider a master's degree program in data science, computer science, statistics, mathematics, astrophysics, or another relevant subject. You can apply the advanced skills you learn to your career in data science with the skills and niche expertise you gain.

Related: 6 Astrophysics Jobs (With Salaries and Primary Duties)

3. Pursue relevant certifications

Getting relevant certifications in data science is the next step to becoming more employable after getting a degree. You can get some of these certifications while obtaining your degree, or you can do it afterwards. There is always the opportunity to advance your knowledge, and completing various data science certificates can help you advance your career. These certifications aim to improve your skills in data science and make you desirable in the job market.

4. Find an entry-level job

Getting an entry-level job is a critical step that allows you to acquire hands-on experience in data science. To find employment opportunities, you can contact people in your professional network or search for roles on professional job listing sites. Many data science jobs can be remote, which gives you additional options and chances to apply for vacant positions in other parts of the country or around the world. To improve your application, be sure to conduct research about the companies you apply to, then tailor your resume and cover letter to highlight all the most relevant skills they're looking for in a candidate.

Related: Data Science Specialization: Types, Benefits, and Tips

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