Can You Learn Data Science On Your Own?

Data Scientists are in high demand all over the planet, and the absence of competition for these roles makes data science a very lucrative career option. In renowned platforms like Glassdoor and LinkedIn, there are approximately 10,000 open opportunities in analytics and data science in each platform due to a talent shortage. Every day, new job openings for data scientists appear on job boards, yet competent candidates are still hard to come by.


  1. Enrolling in online data science courses

The internet’s greatest gift to the world is the simple access to knowledge on almost anything; data science is no exception. There are many data science courses available, including massive open online courses (MOOCs) taught by some of the best data scientists in the world. Many are free, and they all follow a similar curriculum and organization.

Advantages of MOOCs for learning data science:

  • The majority of MOOCs are free, and even those that aren’t free are generally inexpensive.
  • Experts from both the industry and academics teach them.
  • They provide international exposure, with examples and applications from

Disadvantages of MOOCs for learning data science:

Learners do not receive personalized attention because they are usually taken in a large group. Furthermore, because these are recorded sessions, you will be missing the human factor, and your motivation may suffer as a result. One of the primary reasons MOOC completion rates are poor is that they are paired with a lack of support. Consider it like joining a gym without a personal trainer; it’s entirely up to you to exercise and push yourself.

The curriculum is frequently theoretical and unfocused on job readiness. Many MOOCs lack hands-on projects, limiting the amount of practical/technical experience students receive. Even if there are assignments, the experience may be subpar and not lead to employment.

  1. Obtaining information from books and other internet sources

Books are another tried-and-true way to learn anything. Whether you require a general overview or a detailed study, there are a plethora of books available on data science. In fact, you can begin learning the fundamentals of data science before diving into the area, such as mathematics, statistics, programming, and so on.

  1. Adding to your knowledge by gaining real-time experience

Data science is a highly practical field that cannot be mastered solely through MOOCs and books. If you want to pursue a career in data science, you must show that you can do it rather than just know about it. Hackathons and contests can assist you in achieving your goals.

Pros of gaining experience from competitions:

  • You’ll have access to real-world data and business problems.
  • Competing against peers from all over the world gives you a sense of belonging.
  • The ability to gain knowledge from successful solutions.

Cons of relying on contests in data science:

  • When you’re working alone, it might be lonely and irritating.
  • Many of these contests are tough to win, which can be discouraging at times.
  • The datasets used in these competitions are often overly clean or pre-processed, making them different from those used in data science jobs.
  • The setting is clearly not ideal for gaining real-world work experience.
  1. Internships provide valuable experience.

In data science, an internship is the closest thing to genuine employment. It might assist you in learning about the daily work and responsibilities of professional data scientists. It might assist you in expanding your network, which is vital in your job search.

Benefits of Internships:

  • Real-world project experience
  • The first step toward a data science profession

Disadvantages of internships:

  • It can be unproductive and a waste of time unless it’s a clearly structured internship oriented toward making the most of your skills.
  • Your learning may be limited depending on the length of the internship.
  • An internship does not imply that you will be hired at the end of it.

7 tips for Data Science Self-Study

  • Begin anywhere—but begin
  • Improve Your Fundamentals
  • Examine the technical aspects
  • Go Deeper Into More Advanced Subjects
  • Get to Know Your Tools
  • Improve Your Soft-Skills


Data science course are 3 months to 6 months intense programs that consolidate your foundation in data science skills and tools for data analysis, preparing you for entry-level positions. Courses typically take two to six months, and certifications combine many courses on related topics.


You’ll need to master database and programming languages like SQL, Python, and R, as well as how to manage data pipelines from beginning to end. This includes data wrangling, using inferential statistics to investigate trends and characteristics of the data, and using graphing and plotting techniques to turn data-driven insights into decisions that can influence business and product outcomes.


If you want to pick a quality course, make sure it has a robust community that performs the same function as many top colleges’ alumni networks. Data science courses are a fast-paced, well-structured way to focus on your goals, and they are competitive in terms of admissions. On the downside, they may not offer some of the internship chances that come with formal degrees, so you’ll have to complement your knowledge with other activities.


Data science is a rapidly expanding field that affects numerous sectors. There are exponential opportunities to learn at the rate that demand is expanding. Today, this field is seen as a comprehensive and developing one in which a person can advance and build a career. In 2022, data science will be a growing field. Demand for Data Scientists is on the rise worldwide, and the lack of competition for these positions makes data science an extremely lucrative career choice. Several institutes are available today that offer the best data science programs with distinct features and benefits. MIT (Massachusetts Institute of Technology), one of the renowned universities worldwide, offers a first-class program in applied data science.

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