Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconData Sciencebreadcumb forward arrow iconHow to Make a Bright Career in Data

How to Make a Bright Career in Data

Last updated:
26th Jun, 2018
Views
Read Time
5 Mins
share image icon
In this article
Chevron in toc
View All
How to Make a Bright Career in Data

Behind every successful organization, there is Data. It is the latest buzzword in the industry now. Over the past few years, organizations across the globe have started leveraging data to uncover useful insights to drive business growth and power innovation. As a result, there have emerged plenty of career opportunities in data.

However, to kickstart a successful career in data, you must first build the necessary skill set that the field demands, that is, a high level of proficiency with data. Individuals who have extensive knowledge about market dynamics and who know how to convert data into actionable insights using a combination of technical agility and analytical skills are the ones who have a chance to have a bright career in data.

  1. Understand The Basics

The foremost requirement of beginning a career in data is to develop a compatibility quotient with key data concepts such as machine learning, probability theory, statistics, and enterprise analytics, to name a few. As a data professional you must be able to handle databases with flair and also be well-versed with programming languages such as Python, Scala, R, Java, JavaScript, Perl, and SQL.
Career in Data
Today, many online platforms offer specialized Data Science courses for aspiring individuals seeking to start a career in data. With a little dedication and commitment, you can master Data Science with these online courses.

What is Text Mining: Techniques and Applications
  1. Be Thorough With Market Dynamics

The technological landscape is dynamic, with innovations being made every day. To emerge as a skilled data professional, you must keep yourself constantly updated with the latest market development dynamics. The better you understand the market dynamics, the higher will be your efficiency in leveraging advanced data tools to power innovation. Apart from following industry-related news and events, make sure to attend technological conferences. You get to connect with the best of talent in the industry and expand your network.

  1. Strengthen Your Statistics and ML Foundations

Statistics and Machine Learning are two fundamental pillars of Data Science. Thus, to build a career in data, you need to strengthen your statistics and ML skills. Start off small with simple concepts such as hypothesis testing, central limit theorem, normal distribution, and make your way up towards more complex concepts like classification, probability estimation, logistics regression, linear regression, cluster analysis, decision trees, etc.

What Is Data Science? Who is a Data Scientist? What is Analytics?
  1. Develop The Right Communication Skills

To have a bright career in data, it is not enough to be technologically sound. A data professional cannot stop at extracting meaningful information. He/she must be able to translate the information into actionable insights and convey the data-driven solutions to the business management team in terms they can understand. Not only do you have to continually mine data to discover valuable insights but you also have to act as a communication link between the tech team and the business management team.

Explore our Popular Data Science Online Certifications

Our learners also read: Learn Python Online for Free

Having covered all your bases, you will be all set to explore the emerging exciting and challenging positions in data jobs like that of a data scientist, data analyst, business analyst, data architect, machine learning engineer, etc.

Top Data Science Skills You Should Learn


Career in Data

“How to Become a Data Scientist” Answered!

upGrad’s Exclusive Data Science Webinar for you –

Watch our Webinar on How to Build Digital & Data Mindset?

 

Read our popular Data Science Articles

Today, demand for data professionals has spread across various industries such as banking and finance, entertainment, advertising, online shopping platforms, crime departments, agri-tech, and many more.

Profile

Supriya VS

Blog Author
Supriya is managing Product marketing for UpGrad's Data Vertical. She's an engineer by education; loves learning new things, interacting with people and understanding different perspectives. She has a comprehensive experience in Marketing & Sales, Business Development and team building in a Startup venture.

Frequently Asked Questions (FAQs)

1What are different career opportunities for Data Analytics in India?

According to LinkedIn’s 2020 Emerging Job Report India’s traditional relatedness with jobs using hard technological skills is going to get stronger as more and more companies are looking for such talent to grow their business. Data Analytics offers several generalist and specialist roles such as

1. Data Architect - As per LinkedIn the base salary of a Data Architect starts from Rs. 19,00,000 p.a. and scales up to Rs. 33,00,000 eventually with experience.
2. Business Intelligence manager –A BI manager can easily make Rs. 15,00,000 on an average and as the position grows it can scale up to Rs. 25,00,000.
3. Data Scientist – In India a Data Scientist can earn Rs. 9,00,000 yearly on an average and eventually with work experience the salary can rise up to Rs. 20,00,000 yearly. A great demand for data scientists is observed in sectors like finance, retail, travel, consumer goods, etc. offering a base salary of Rs. 13,00,000 to 14,00,000 per annum.
4. Data Manager –The basic salary offered to a Data Manager is about Rs. 7,00,000 and can rise up to Rs. 25,00,000 with time.
5. Data Analyst – Apart from Data Analysis a data analyst should master technical skills, data modeling, data organizing, automation, etc. In India the salary of a Data Analyst ranges between Rs. 4,00,000 to 9,00,000.

2What is the difference between Data Science and Data Analytics?

When you enter the world of ’Data’, you are most likely to encounter two terms ‘Data Science' and ‘Data Analytics' for sure. Though both the terms are interrelated, they refer to different things when applied in business.

1. Data Science: Data science is concerned with the creation, cleaning, and organization of datasets. To collect and transform raw data into something that can be more easily comprehended, data scientists use algorithms, statistical models, and their own specialized analysis. Data Scientist performs key functions such as Data Wrangling, Statistical Modeling and Programming.
2. Data Analytics: The technique and practice of analysing data to answer questions, extract insights, and discover trends is known as data analytics. This is accomplished through the use of a variety of tools, approaches, and frameworks that differ based on the type of analysis being performed. Different types of analytics included in Data Analytics are Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics.

Explore Free Courses

Suggested Blogs

Data Science for Beginners: A Comprehensive Guide
5015
Data science is an important part of many industries today. Having worked as a data scientist for several years, I have witnessed the massive amounts
Read More

by Harish K

28 Feb 2024

6 Best Data Science Institutes in 2024 (Detailed Guide)
5020
Data science training is one of the most hyped skills in today’s world. Based on my experience as a data scientist, it’s evident that we are in
Read More

by Harish K

28 Feb 2024

Data Science Course Fees: The Roadmap to Your Analytics Career
5036
A data science course syllabus covers several basic and advanced concepts of statistics, data analytics, machine learning, and programming languages.
Read More

by Harish K

28 Feb 2024

Inheritance in Python | Python Inheritance [With Example]
17105
Python is one of the most popular programming languages. Despite a transition full of ups and downs from the Python 2 version to Python 3, the Object-
Read More

by Rohan Vats

27 Feb 2024

Data Mining Architecture: Components, Types & Techniques
10586
Introduction Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a
Read More

by Rohit Sharma

27 Feb 2024

6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About
79399
What is a Data Analytics Lifecycle? Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes
Read More

by Rohit Sharma

19 Feb 2024

Sorting in Data Structure: Categories & Types [With Examples]
137484
The arrangement of data in a preferred order is called sorting in the data structure. By sorting data, it is easier to search through it quickly and e
Read More

by Rohit Sharma

19 Feb 2024

Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics
67769
Summary: In this article, you will learn, Difference between Data Science and Data Analytics Job roles Skills Career perspectives Which one is right
Read More

by Rohit Sharma

19 Feb 2024

13 Exciting Python Projects on Github You Should Try Today [2023]
44751
Python is one of the top choices in programming languages among professionals worldwide. Its straightforward syntax allows software developers and dat
Read More

by Hemant

19 Feb 2024

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon