How to Get Your First Job in Data Science? Take a Leap in your Career

How can you get your first job as a Data Scientist? This is the most asked question on the internet these days. So, in this article, we are providing you a walkthrough of how you can get your first job in data science or first entry-level job as a Data Scientist.

Here, you will find all the major questions with answers related to important tools, technical and non-technical skills, and at last steps which you should take to land your first data science job.

Before moving on, get a quick overview of – What is Data Science?

How to Achieve Your First Data Science Job?

Every industry in the world makes use of data in some way or another. But what does it take to become a data scientist? How can one get his/her first interview and work as a data scientist? Explore answers to all these questions and secure your first data science profession.

1. What are the Important Data Science Technical Skills?

In order to journey into the field of Data Science, you must possess some of the following key skills:

1.1 Python

Python is the most popular and easy to learn programming language. It is a high-level object-oriented programming language that is used not just for data science but also for web-application and GUI development.

Python 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, Python is an ideal programming language.

Want to learn Python from Scratch?

1.2 R

R is a statistical modelling 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.

Learn more about R in detail

1.3 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.

1.4 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.

Before it’s too late, start your Big Data learning and make a bright career.

1.5 Java

In more specific computing terminology, Python is known as a scripting language and Java is known as a programming language. Many industries require knowledge of both scripting as well as a programming language.

Knowledge of Java will facilitate you to tune and maintain big data platforms like Hadoop which are written in the same language.

2. What are the Best Books for Learning Data Science Skills?

You can acquire the knowledge of some of these tools through the following books –

2.1 Learning Python

This book is a primer for anyone who wants to venture into the world of Python programming. With this book, you will be able to get a full grasp over Python. This is a comprehensive book that will give you an in-depth understanding of Python.

2.2 Hands-On Programming with R

This book is specifically for the absolute beginners in R. With this book, you will learn about some of the basic concepts of R like objects, packages, notations and environment.

2.3 Learning SQL

This book provides a perfect introduction for those who want to learn SQL. With this book, you will learn some of the basic operations in SQL like retrieve, manipulate, creating databases and tables. You will also learn some intermediate concepts like grouping, joining tables etc.

2.4 Hadoop: The Definitive Guide

Big Data is a vast field that incorporates various tools and technologies. For starters, Hadoop is an ideal tool. With this book, you can design and maintain reliable Hadoop Clusters for storing massive amount of data. This book will also teach you various Hadoop packages like Hive, Pig, HBase etc.

Check out more Data Science Books

3. What are the Non-technical Skills Required?

Another important area where you need to be proficient in is the non-technological area. This area comprises of Statistics, Math and Analytical Thinking.

3.1 Statistics

Statistics forms the core backbone of data science. In order to be proficient 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.

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.

4. What are the Steps to Get your First Job in Data Science?

After having knowledge of these skills, you must apply them to create various interactive projects and engage in an active data science community.

Step 1 – The first step towards getting any data science job is to 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.

Top Programming Languages required for Data Scientist Job

Step 2 – The best way of taking part in data science projects is through Kaggle. There are competitions and challenges on Kaggle that appeal to data science enthusiasts of all levels.

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

Step 3 – Working on independent projects is another way of getting experience in the field of Data Science. There are various publicly available datasets online. 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 – Apart from project developments, your engagement and contribution to the data science community also matter. You should engage yourself in writing blogs, tutorials and even walkthroughs of the solutions of your Kaggle problems.

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 5 – 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.

Learn with infographic – How to become a data scientist quickly? 

Summary

In this article, we described a walkthrough towards becoming a proficient data scientist. We discussed various key steps that include skills required, tools utilized, portfolio curation and connection building to land your first job at a data science firm.

We hope that with the help of this article, you will be able to land your first job as a data scientist!

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