How to Kickstart your Data Science Career?

How to get into data science

As we have already discussed, what is data science and why is data science important in our previous blog. In this blog, we will be talking about starting a career in data science. Since data science is a growing and in-demand field, many people, especially students, are interested in getting into data science.

How to start a career in data science?

If you are a professional or a student looking to get into data science, you must keep in mind some of the prerequisites before applying to any jobs or internships.

It would be best if you first did your homework and research about the field, what’s in and what’s not. You should carefully learn what data science is and the skills required to become a successful data scientist, especially if you do not have any experience.

Some of the important steps to keep in mind if you want to know how to get into data science are:

1. Brush up your math skills

If one is from a STEM background, data science would not be that difficult for them but if you are from a different background, mathematics is necessary for you to master. One needs to understand the basics of data analysis first before jumping off to data science, which begins with plotting graphs with different X and Y values and then finding correlations and trends between them. Some of the important concepts to master are:

  • Statistical methods and probability theory
  • Probability distributions
  • Multivariable calculus
  • Linear algebra
  • Hypothesis testing
  • Statistical modeling and fitting
  • Data summaries and descriptive statistics
  • Regression analysis
  • Bayesian thinking and modeling
  • Markov chains

2. Learn Programming

Data scientists are expected to know coding languages such as Python, R, SQL and SAS as their roles and responsibilities will revolve around these languages. In the interviews as well, they can be asked to write down a code or two. Machine learning is also another major aspect that one should master when thinking of getting into this field.

3. Do internships and projects

Companies will eventually look for some professional experience in your resume and not just your skills while hiring. So, you must take up some side projects or internships, even if it is unpaid, that you can find through social media or job boards. Doing these projects and internships will not only increase your skill set, but they will also increase your chances of landing a job in the data science field.

4. Begin you journey as a data analyst

A data analysts may not be the same as data scientists, but both these fields are booming right now. Data analysts will manage the data collected from various sources and look for the latest trends, and a data scientist will go one step ahead by applying his coding and mathematical skills to interpret the data. Hence, data analyst jobs will be slightly easier to land and can act as a thrust in your data science career.

5. Networking

When you are a newbie in the field, you may find it easy to break into smaller companies but to get into bigger organizations as an entry-level data science professional, you can start networking and discover a few opportunities in the desired team given you have the right skills for it. As more prominent companies have a lot of programs for freshers to get training and learn about the field. You can also look for opportunities in data science in your current company can network internally to pivot into that position.

Data Science can be a talk of the town for many data science engineers and aspirants. Adaptation of Data Science as a career has given rise to plenty of misconceptions and myths. There are a lot of Data Science myths that one should avoid misapprehending Data Science as a career.

Lastly, it all depends on how skilled you are and how much are you willing to learn as there will be a lot of learning and exploration in this field.

WRITTEN BY

Himanshu Mishra

Technology Head
Himanshu is an entrepreneur with 17+ years of overall experience in strategic and advisory roles in Senior Management, IT program management, Quality Assurance, and ERP implementations. He has invested in companies focused on Digital Technologies and Healthcare industries. He has previously worked in domains like: Technology, Finance, Marketing… Read more

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