How to Get Your First Job in Data Science?

How can you get your first job as a Data Scientist? This is the most asked question online nowadays. So, in this article, we are providing a walkthrough of how you can get your first job in data science or an entry-level job as a data scientist. Below, you will find all the major questions and answers related to important tools, technical and non-technical skills, and at last steps required to land your first data science job.

How to get your first Data Science Job?

Every sector worldwide uses data somehow or other. But what does it take to become a data scientist? How can one get his/her first job as a data scientist? Find answers to all these questions and secure your first data science job.

1. What are the Important Data Science skills?

To to get into the field of data science, you must have the following technical skills:

1.1 Python

Python is one of the most popular and easy to learn programming language. It is a top-level object-oriented programming language that is used not just for data science but for web-application and GUI development. Python offers support for a large number of machine learning and deep learning libraries like Tensorflow, Keras, sci-kit-learn etc. To begin your journey in the field of data science, Python is an ideal programming language.

1.2 R

R is a statistical modelling language that is very prominent among data-scientists. R provides a deep understanding curve that makes it difficult for the first time customers. However, excluding this, R is the first choice for several statisticians. It also gives support for various data science operations with its abundant collections.

1.3 SQL

SQL is the backbone for Data Science. It is utilized as the first step in every data science operation. SQL is used for extracting data. It is developed for taking care of data that is stored in a relational database. SQL is mostly used for managing structured data. However, to become a data scientist, you should know how to manage unstructured data, which is dealt with NoSQL.

1.4 Big Data

Big Data is another vital technology that is a sub-part of Data Science. Big Data technologies like Hadoop, Flicker, Apache Flink have taken the globe by tornado due to their substantial data storage and handling capacities. Considering that a data scientist has to manage huge quantities of data, knowledge of Big Data is necessary.

1.5 Java

In more specific computing terminology, Python is known as a scripting language and Java is called a programming language. Many industries call for expertise in both scripting and programming languages. Knowledge of Java will certainly facilitate you to tune and maintain big data platforms like Hadoop which are written in the very same language.

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

You can acquire the understanding of some of these tools via following publications–.

2.1 Learning Python

This book is a primer for any individual who wants to venture into the world of Python programming. With this publication, you will certainly be able to get a full grasp over Python. This is an extensive publication that will give you an extensive understanding of Python.

2.2 Hands-On Programming with R

This publication is especially for the outright novices in R. With this publication, you will certainly learn about several basic concepts of R like objects, packages, notations and environment.

2.3 Understanding SQL

This publication offers the best introduction for those who wish to learn SQL. With this book, you will learn several standard operations in SQL like retrieve, manipulate, develop databases and tables. You will also find out some intermediate principles like grouping, signing up with tables etc.

2.4 Hadoop: The Clear-cut Overview

Big Data is a substantial area that incorporates various tools and technologies. For beginners, Hadoop is an optimal tool. With this book, you can create and keep reliable Hadoop Clusters for saving a substantial amount of data. This book certainly instruct you on various Hadoop packages like Hive, Pig, HBase etc

3. What are the Non-technical Skills Required?

One more important area where you need to be skilful in is the non-technological area. This comprises of Stats, Math and Analytical Thinking.

3.1 Stats

Data develops the core foundation of data science. To excel in Data Science, you should have thorough knowledge about different topics of Data like Descriptive Stats and Inferential Statistics. Getting ready for data science interviews will certainly require you to be well versed in numerous analytical procedures.

3.2 Mathematics

Mathematical concepts like linear algebra, calculus and probability are some of the most important topics in Data Science. Consequently, understanding of these topics is necessary for your first job as a Data Scientist.

3.3 Analytical Thinking

Analytical thinking and problem solving, are both crucial demands for any data science position. As a part of your everyday responsibility, you will be needed to solve complicated data science problems. Therefore, you have to have the appropriate knowledge and creative thinking to formulate a service and use different tools to implement it.

4. What are the steps that one needs to land in their first data science job?

After understanding these skills, you have to apply them to create different interactive projects and engage in an energetic data science community.

Step 1— The first step towards obtaining any type of data science job is to develop a resume or a portfolio that all the data science projects that you have built or contributed to. To do so, you have to have the analytical knowledge and shows abilities to join such jobs.

Step 2— The best method of participating in data science projects is via Kaggle. There are competitors and challenges on Kaggle that interest data science enthusiasts of all levels. As you work your way up through numerous competitions on Kaggle, you are making a reputation of your own in the data science globe. Also, you are adding projects to your data science portfolio.

Step 3— Working with independent tasks is another way of obtaining experience in the field of Data Scientific research. There are different publicly available datasets online.

Step 4– Besides project development, your interaction, and contribution to the data science community also matter. You must involve in writing blogs, tutorials, and even walkthrough of the options of your Kaggle challenges. This will certainly help you to develop a solid presence online. Besides, engaging in StackOverflow and responding to queries will provide you with the training to answer questions of varying degrees.

Step 5– Building connections is another important requirement to secure your first interview. You should be active on Linkedin, share your projects, and take part in community discussions. You should have the ability to influence your potential employers to secure a meeting with them.

Summary

In this post, we described a walkthrough towards becoming a competent data scientist. We went over various essential steps that include skills required, tools used, portfolio curation and building connections to land your first job at data science firm. We wish that with the help of this post, you will be able to land in your first job as a data scientist!

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