How to Become a Data Scientist

Introduction to How to Become a Data Scientist

Have you ever thought of a mathematician or statistician sitting in an IT company, doing software work or vice versa? Well, the Data scientist’s job asks for it. It needs people to know math, statistics, domain expertise and programming knowledge. One who is very much interested in chunks of data and what they are going to do in this world can also be surprised by data science. In fact, anyone with a basic undergraduate degree can become a data scientist. Many people are into the lookout for how to become a data scientist. I think that it’s the most searched topic on the internet.

What is Data Scientist?

Let us look into the details of what is data scientist, whether it’s domain expertise or programming background or mathematics.

1. Basic Mathematics

Many of us might have hated math in our childhood days, that we didn’t even like the tutor who taught math. I am here to reveal a well-known secret. Math, including algebra, matrices and some calculus, is very much needed in the field of data science. While exploring huge data, we will be in awe as to how these ‘good for nothing’ matrices or calculus could do it. Math in itself is fascinating if one takes an interest in the subject. Develop a genuine interest in math, and you will do it right. Now folks, who love math like me, give the nod to you and go ahead.

2. Statistics

While learning probability and statistics during my childhood, I never thought that probability would follow me lifelong. However, the importance of statistics in data science is inevitable. We use many theorems and formulae of statistics to understand the data and to predict the future of data. Even if you get lost in the vast data, statistics can help you take the right path. Theories and formulae proven by great scientists will not fail, will they? Distribution and exploration of data can be done easily with the help of statistics.

3. Programming Skills

After getting an idea of data with the help of mathematics, it is really nice to visualize it. What if some coding helps us to do this easily! Python and R are well-known programming languages that help data scientists do their work easily. Statistics easily works with both languages that distribution and exploration of huge data can be seen easily with two or three steps of coding.

It’s not necessary to know both the hand of the language in hand. However, expertise in one language helps you reach in great heights in your data science career. If you are new to Python or R, take a deep breath and pull yourself up. Both languages are easy to learn and understand. Nothing can stop you from becoming a data scientist.

4. Data Visualization

Data visualization is very important in data science as you should know how your data behaves after your analysis. If you could foresee it well, you are halfway done at the beginning of data exploration. While analyzing data, visualize where data can take you if you take it the right way. Or what happens if you take the opposite side of the road? People may laugh at me if I say that creativity is an important part of data visualization. But this is true. Graphs and plots can help you do the work without doing all the calculations and coding parts. Some data visualization tools include Excel, Tableau, Google charts and so on.

5. Machine Learning

Data science is about analyzing the data; machine learning is building a model out of the data. Machine learning helps you understand labeled and unlabeled data, gives you a clear picture of various types of regression and predicts how future data can be. With the advent of new technologies and various ways through which a new pile of data is being created, it is important to keep the data in our hands to be well known and helps us predict our future. Machine learning helps in doing this. Traditional machine learning approaches can be dethroned by deep learning. Neural networks think like human brains, and bit AI will make our life easy with data. Basic knowledge of deep learning is important to be an efficient data scientist.

6. Data Knowledge

This should be the first topic on this page. Knowing your data is very important. The domain to which the data belong to, whether any relevant columns are missing, the shape and size of data, and data behaviour is necessary to derive proper conclusions. Missing data should be replaced or removed based on the relevance of the column. Proper care should be given to find out labeled and unlabeled data. The method of regression to be followed must be considered after proper study of data.

7. Communication Skills

Once data cleaning, exploration, and analysis are over, it is crucial to inform the developments to the concerned team members and also to the management. Communication skills come in handy over here. It is important to showcase your work with utmost patience in layman terms so that whoever in the presentation should get a gist of the message you are trying to convey. Speak with the genuinely interested people in your work, get information from people who have been working for long years, and make everyone understand the importance of data analysis. Good communication helps in doing all these things in a methodical manner.

Conclusion

You should be updated about the market and develop your data analysis accordingly. Work hard for your data and do a perfect analysis as a small mistake means screwing up your organization. No one wants to do that. The data scientist can specialize in any field because huge data is present in every field of science in the world. Knowledge of all the above-mentioned topics in itself cannot make you a skilled data scientist. You should be hardworking and open to new ideas always. As the world changes, so do the field of data.

Recommended Articles

This is a guide to How to Become a Data Scientist. Here we discuss the introduction and what is data scientist respectively. You can also go through our other related articles to learn more –

1. Introduction To Data Science
2. Data Science Languages
3. Data Science Algorithms
4. Python Libraries For Data Science