Having the skills to derive key insights from data is much more important than just having a degree in the field. There are plenty of resources on the Internet to kick-start your career in Data Science, but a majority of them do not make you industry-ready.

This article will cover a comprehensive list of resources and courses available on the Internet that certainly help you spearhead towards Data Science. There is plenty to learn in Data Science, but with the help of the right mentors, and just the right amount of resources, you might just land your next job as a Data Scientist! By the end of the article, you will have a fair understanding about the minimum skills you would need to pick up to be a Data Scientist, even without having a degree in it.

But first, what is Data Science?

What is Data Science?

According to Wikipedia, Data Science is an interdisciplinary field that consists of statistics, programming, and mathematics.

Business intelligence and domain knowledge are also important soft skills. It is essential to learn the role of each building block in data science.

The role of Statistics

One of the building blocks of information science is insights. Without a good degree of statistical information, it would be profoundly hard to comprehend or decipher the information.

Statistical insight assists us with clarifying the information. We use insights to deduce results about a dependent on an example drawn from that population. Besides, AI, Machine Learning, and statistics have a lot of similarities.

So once you have a firm grip over the foundations of Statistics, it is easy to dive into other fields related to Data Science as well.

A youtube playlist for your statistical needs :https://www.youtube.com/user/joshstarmer

Learning SQL

SQL is a must-have skill for Data Scientists because if there’s data, there is a need for SQL. You might be asking the right questions to a dataset, have amazing theoretical knowledge, but all goes in vain you can’t solve your Data Science needs without a grip on SQL. SQL is used to manage and manipulate data through its various queries. SQL certainly looks daunting at first, but with the help of the right guide, it is fairly easy to understand. I would highly suggest you find a mentor/coach that helps you with 1:1 coaching.

The best resource available for such coaching is hands-down the Board Infinity Data Science Learning path where you get a dedicated coach who is already working in the industry with plenty of experience.


Programming for Data Science - Python

No IT field is complete without a programming language. The simplest, most efficient language for Data Science is Python. You can manipulate data, plot graphs, create a web app, run data science models and deploy it. Python is also used by NASA, Tesla, Google, Microsoft, and every other tech giant in the world. Learning Python can help you stand out and increase your odds of landing a job.

The most effective way to learn python is by referring to their official documentation.

For the ones who are more visually inclined, here’s a youtube video of about 4 hours.


If you need a hand-holding, 1:1 teaching to learn the most popular language:


Working on real time datasets

Science is always backed by data. Without legitimate information, even the most developed models can't make any worth. Accordingly, we need datasets for rehearsing. I will list one asset that you can download datasets free of charge. You can undoubtedly discover more datasets on the web.

Kaggle: www.kaggle.com

A list of other skills :

  1. Tableau
  2. Linear Algebra
  3. Amazon Web Services
  4. Hive


Your background doesn’t matter in Data Science, what matters is if you have the skills that can be used by a company. It’s always advised to post your learnings on various platforms like LinkedIn, Medium, and so on. It is equally important to show your real-time projects (which can be posted on GitHub or Kaggle).

What is of prime importance is having a mentor who guides you in this journey, and is with you in every step that you take because being lost in a pool of resources is very common when it comes to Data Science.