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How to Make a Bright Career in Data

Last updated:
26th Jun, 2018
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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.

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  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.

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  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.

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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.

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Career in Data

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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.


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.

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