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How can I make my career in Data Science? Is there a roadmap I can follow? What technical skills do I need? What books should I read? Or what Non-technical skills should I learn?

Does any of these or all of these questions bother you? Then, you have touched down the right place.

It doesn’t matter if you have a graduation degree or not. All you need is Business Acumen, Computer Science Skills and some mathematics and statistics (It is not too easy, but there is no such thing as a free lunch :P).

Since you have landed here you are in search of a Data Science job (Smart :D). You can achieve your first job in data science by scrolling the article below.

Talking about Data Science, it is a Science which is all about having the right skill set which your college will not teach. (Exceptions are always there).

Life is not a bed of roses, but career in Data science can disprove the saying!  

How to make a Career in Data Science?

Explore the below 9 steps that will help you to make a career in data science and to achieve your dream job in it –

How to make a career in data science

Step 1 – Practice! Practice!  and Practice

Practice and get real-life experience.

Work under someone or pick up a project that you are interested in or teach someone(this will help you to improvise yourself and see where you are lacking).

Step 2 – Find a Mentor

Disclaimer- This is not mandatory. But, it’s always good to have proper guidance.

Try to network and find yourself a mentor. Working in someone’s guidance helps you to learn a lot. This will also ensure that you work in the right direction.

Step 3 – Take Projects

Taking projects, help you a lot in making your career in data science. The best way of taking part in data science projects is through Kaggle. There are competitions and challenges on it that appeal to data science enthusiasts of all levels.

As you work your way up through various competitions, you are earning a reputation for yourself in the Data Science world. Furthermore, you are adding projects to your data science portfolio.

Also, working on independent projects is another way of getting experience in the field of Data Science. Using your creativity, you can craft your own data product that makes use of open datasets.

If the dataset is not available online, or you are interested to have real-time streams of data, you can utilize web-scraping tools that are provided with Python.

Step 4 – Make a strong online presence

Make yourself visible online and show how effective and dedicated you are towards the work. Make your work history and experience (if you have) impressive and align it with a job that you desire.

For example, you can make your Linkedin profile very compelling. Or you can have your own blog at Word press and write about the ideas or technology. You can also have your own website and upload your portfolio.

This will help your resume to not get buried under the mountain among other applicants. In order to do so, you must have the statistical knowledge and programming skills to participate in such projects.

Step 5 – Engage in the Data Science community

Engagement and contribution to the data science community matters. You should engage yourself in writing blogs, tutorials, and even walkthroughs. This will help you to build a strong presence online.

Furthermore, engaging in StackOverflow and answering queries will provide you with the training to answer questions of varying degrees.

Step 6 – Networking is your Networth

Building connections is another important requirement for securing your first interview. You must be active on Linkedin, share your projects and engage in community discussions. You should be able to influence your potential employers in order to secure an interview with them.

Step 7 – Make your CV strong

Your resume should reflect your essence. This can be done by adding the right personal details and your best attributes. Develop a resume or a portfolio that lists all of the relevant data science projects that you have built or contributed to.

In order to do so, you must have the statistical knowledge and programming skills to participate in such projects. Don’t forget to keep CV up to date. It will show your punctuality and discipline for work.

Step 8 – Take your first job in a startup, mid-level organization or a higher level company(The important thing is to learn)

It is true that people at a Multinational company are already overburdened with work. They will neither have the time, nor the motivation to teach you or guide you( I am talking about the general scenario).

In mid-level, there are chances that you don’t have much to work and in a startup, it is possible that you might be asked for multitasking(so you might find it difficult to master the science).

All I want to say is that you should focus on the environment where you can learn, so if you think that startup is the best option for you or likewise go for that. The right choice can help you shape your career.

Wait! At this point, you need to check the different job roles of data science

Step 9 – Last but not least.. Money is secondary in the initial stage, learning is more important

You will earn a lot since Data science is highly in demand. Also, because it is one of the best paying jobs. But, you should focus on learning and improving your skills in the start. If you get a good hold of the skills and if you master the art of how to learn then your career will shoot up like a Bamboo tree.

This is because the unseen foundation you will build will help you grow. So, patiently toil towards worthwhile dreams and goals, build strong character while overcoming adversity and challenge and grow the strong internal foundation to handle success.

Finally!!! After exploring these steps, you must apply them or read the below skills that will take you a step ahead for your next Data Science Job. 

How to Achieve Your First Data Science Job?

For making your data science career bright and achieving a job in it, you must possess several skills that are essential fir every data scientist –

1. Become Tech-Savvy and Learn the Right Tools:

These tools are the key essential factor for becoming a Data Scientist. They are:

1.1 Python

Python is the most popular language in Data Science. It is a highly productive language and it is very easy to read and simple to implement.

It provides support for a large number of machine learning and deep learning libraries like Tensorflow, Keras, scikit-learn, etc. In order to start your journey into the world of Data Science, start with the most ideal programming language.

1.2 R

R is a statistical modeling language that is highly popular among data-scientists. R provides a steep learning curve that makes it difficult for the first time users.

However, regardless of this, R is the first choice for many hard-core statisticians. It also provides support for various data science operations through its abundant libraries.

1.3 Big Data

Big Data is an important technology that is a sub-part of Data Science.

Big Data technologies like Hadoop, Spark, Apache Flink have taken the world by storm due to their massive data storage and processing capabilities. Since a data scientist has to deal with large volumes of data, knowledge of Big Data is essential.

1.4 SQL

SQL is the bread and butter of Data Science. It is used as the first step in every data science operation. SQL is used for extracting and retrieving the data. It is designed for managing data that is stored in a relational database.

SQL is primarily used only for handling structured data. However, in order to become a data scientist, you must also know how to handle unstructured data, which is handled through NoSQL.

After having an insight of the tools that you need, the next question that you can have is From where should I learn? You have two cost effective and time efficient methods to learn from.

2. Where to learn Data Science?

The first option is to learn through books. Increase your brain RAM and knowledge and read books.

Or the second place to learn these skills is through an online platform like DataFlair. The advantages of learning online are that it presents the topic in a very logical order and supplements your learning.

Here are FREE 370+ Data Science Tutorials where you will find every topic that is important for mastering data science.

3. Don’t underestimate the non-technical areas.

Statistics, Math and Analytical Thinking help you to become a considerable Data Scientist:

3.1 Statistics

Statistics form the core backbone of data science. In order to get your first job in Data Science, you must have in-depth knowledge about various topics of Statistics like Descriptive Statistics and Inferential Statistics.

Preparing for data science interviews will require you to be well versed in various statistical procedures.

Maths and statistics for data science

3.2 Math

Mathematical concepts like linear algebra, calculus, and probability are the most important concepts in Data Science. Therefore, knowledge of these concepts is a necessity for securing your first job as a Data Scientist.

3.3 Analytical Thinking

Analytical Thinking and problem-solving are the two most important requirements for any data science position. As a part of your everyday responsibility, you will be required to solve complex data science problems.

Therefore, you must possess the right knowledge and creative thinking to formulate a solution and use various tools to implement it.


In this article, we discussed the steps to get the first Data Science Job. It explained how by following these steps one can make the most and become industry competent.

Also, you should keep this in mind that different companies have different requirements and the usage varies according to the industry. Therefore, learning the above skills will prepare you in advance for your job and will also give you an edge over others.

Adding to that you should also keep in mind that no knowledge ever goes to waste so give your 100% in each and everything you do. I hope that you land your first job as a data scientist soon!

If you have any other questions regarding the Data Science career, you can freely ask through comments.

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