An Ultimate Guide On How To Start A Career In Data Science | For Beginners| Tools | Subject |

Hello, and welcome back folks! In the previous article, we looked into the career prospective of Data Science and what you should choose between Data Scientist and Data Analyst as your career option. Now, both of these professions are, based on Data Science, and so, first, we need to understand what is Data Science. Today I am going to discuss what exactly is Data Science and why it has become so popular these days, and how should you get started, what should be the Learning Path towards Data Science.

What is Data Science?

What exactly is Data Science? Is it about creating cool visualizations? Is it about statistics? Or about coding? Or about writing complex machine learning models? What is it?

Data science in simple words is solving problems and creating impact using data and past experiences. For example, a Data Scientist working at a product-based company, would be working on some new product recommendations, or they would be working on improving the existing products or they would be working on some Data Analytics platforms, which would be used by the internal teams or the external teams.

How did it start?

It all started back in 2001 when William S. Cleveland published his paper, “Data Science: An Action Plan for Expanding Technical Areas of the Field of Statistics”, where he combined Data Mining with Computer Science and made the practical usage of statistics, a lot more technical. We could now use computing power along with statistics, and this amalgamation was called Data Science.

Hierarchy of needs

So, now let's try to understand what the Data Science space looks like:

So, this a Data Science pyramid, basically, this explains to us what's the hierarchy of needs or the skills that comprise the Data Science domain. Now the solution to any Data Science problem starts with the collection of data, which is placed at the bottom. Next comes, how easy is it to access the data, and how efficient is the data infrastructure, which is ensured by data engineers in the second layer.

Now, once you have the data infrastructure setup, the analysts explore and transform this data to uncover hidden patterns, store analytics, and create visualizations, that make the data easy to read.

Then comes Scientists and Senior Analysts, who have expertise in AI, Deep Learning, Designing experiments for A/B testing, etc... So, you can see that the Data Science Domain, in itself gives rise to several job descriptions, that you can aim for, Data Engineers, Data Analysts, Machine Learning Engineer's, Data Scientists, Research scientists, Core Scientists, they're a bunch of opportunities that you can aim for.

Why has it become a Buzzword?

So, why has data science become a buzzword these days? We see a lot of news around Data-Science based companies and startups, raising a lot of money. We saw Neural Magic gets 15M $, Alteryx acquires machine learning startup Feature Labs.

Companies raising seed funding of 50 million dollars, and just to launch some machine learning framework, the list just doesn't stop. There is an immense number of possibilities in Data Science, with a large number of investors, ready to fund these companies. This is going to be the future, be it Banking, Finance, Healthcare, Agriculture, Gaming, Entertainment, Space Exploration, Self-Driven vehicles, you just name it.

 

What should we Focus on?

So, when it comes to learning Data Science, I believe that there are four major subjects or four major branches of Data Science Curriculum, that an individual should work on.

So, the first branch is programming tools, where you need to cover what is Python? How to program in Python? How to program in R? based on the language that you choose, then you learn about how to use notebooks, various libraries like NumPy, Pandas, TensorFlow, Keras, etc...

The second subject is Data Engineering, where you learn about how to engineer the data? how to extract it? learn about writing SQL queries, exploratory analysis, data wrangling, databases, and API.

The third subject is mathematics and statistics, where you learn about linear algebra, stats, probability, hypothesis testing, A/B testing, how to design your experiments.

The fourth and final subject is called Algorithms and Systems, where you learn about machine learning and deep learning algorithms, how to build recommender systems, and other concepts. You have to practice all these concepts in the form of projects, solving real-world problems.

So, now we have understood, that there is a lot of interest in pursuing Data Science, and for good reasons, that is... high job satisfaction and high demand, high salaries and high impact.

So how should you get started?  A quick Google search for Data Science will give you a lot of resources to learn from. There are podcasts, forums, blogs, articles, online courses, self-directed curriculum, boot camp, etc... so there are a lot of resources to learn from.

Definitely, you won’t get all of them in a single resource. So, to cover the entire data science space, and the four branches of the data science curriculum, and all the concepts mentioned in the data science pyramid, you need to search in google for them individually, and learn from data scientists working at Google, Microsoft, Amazon, and such big companies. Search for which companies are looking forward to Data Science, and what they look for in a Data Scientists.

Conclusion

Start by learning Python programming, and then continue to learn from various resources and have command over them. With this, I would like to wrap-up this article. I hope, I have covered all the details that you require to begin your journey in the Data Science Field. Thank you for reading this article and I would request you to also check my article on the Difference between Data Scientist and DataAnalyst.