How to jumpstart your data analytics career

By NIIT Editorial

Published on 29/10/2021

7 minutes

The generation of data around the world doesn’t seem to be slowing down anytime soon. Fast-growing mobile data traffic, cloud computing traffic, and the speedy development of technologies such as AI and IoT are contributing to the increasing complexity and volume. Data analytics involves extracting insights from big datasets. Data science is about how can we model the world— and use models to make predictions. From the speed at which technology is progressing, data analytics will be taking centre-stage in our daily life. Its increased presence will also boost the career opportunities associated with it.

Why make a career in data analytics?

Data analytics has become an important part of global development

  • 2.5 quintillion bytes of data are being generated each day
  • Most of all data has been created in the last few years
  • Unstructured data poses a problem for most businesses
  • In a few years, the big data industry could be valued at an estimated $77 billion

Formulating actionable answers out of data has become a key business skill. Different organisations collect big data and look towards using it for making or improving decisions. Firms in fields as varied as B2B and B2C commerce, manufacturing, health care, and marketing use data analytics for improving processes and enhancing profits.


What are the roles and opportunities to pursue in this field?

The role of a data analyst is a popular one in India. But there is a variety of other roles one could develop into. 

Here are some in-demand global data-oriented roles you can expect to fill in after when you learn data analysis:

  • Big Data Engineer
  • Business Intelligence Analyst
  • Database Manager
  • Data Warehouse Analyst
  • Database Developer
  • Data Scientist
  • Database Administrator
  • Data Modeler
  • Data Architect
  • Data Analyst/Report Writer


What skills should you develop to jumpstart your data analytics career?

Making a career in data analytics can involve the following steps:

1.  Getting your analytical skills up to date

Varying levels of technical ability are involved in data analytics. Statistics is one area that is worth being knowledgeable about.

  • To be a data analyst, you must know how to accurately use statistical techniques
  • You should like and be good at working with numbers
  •  You also have to be curious, inquisitive, and enjoy wanting to know how to solve a problem, not knowing the solution, and working through the problem
  •  You should be comfortable working with datasets, using statistical models and techniques, and creating insights that can be interpreted by others

To carry out modelling, which is at the core of machine learning models, you should have an understanding of linear algebra, probability, statistics, and calculus. The better you connect with math, the better you can understand the underlying behaviour of algorithms. It will also help you consume information much quicker during your time spent completing data analysis courses.

Math skills are something you must have from the start. They are the basis to understand and learn everything else in data analytics. Study basic math and basic algorithms behind the instructions in Python or other tools. Courses for data analytics using R may also come with special modules for mathematics and related subjects.


2.  Digging deep into your field of interest

Analytics isn’t a stand-alone field. It can be applied to any sector where data is collected. Analytical roles can vary based on the context of the department and industry.

  •  Understand your current or desired field and become more aware of how data can be used in it
  •  For insights and access to reports or datasets, look up public datasets and see what metrics are commonly recorded
  • Try to find out the common challenge or problems being faced in this field. Understand the metrics that would come into play to diagnose and resolve it
  • Get to know the companies or organisations you admire. Check out their blogs, social media accounts, hiring policies, and mission statements. Try to understand the role of data and analytics in those companies.

By digging deep, you’ll be better equipped to understand how data can influence the future positions you plan to pursue.


3.  Honing your skills

One of the main functions of a data analyst is to visually analyse exploratory data and communicate the findings using innovative visualisation tools. For example, in a data scientist must be able to weave a story out of the data at hand and innovatively communicate the findings via striking and engaging visuals. So, along with, say, an advanced excel course for data analytics, also look out for a visualisation course to complement it.


4.  Learning how to code

One of the most important skills for working effectively in an analytical role is the reading, understanding, writing, and analysing of data and code. Some of the most in-demand analytical tools and languages are Excel, R, SQL, and Python. For example, a Data Analytics course with R Certification training could help you master some important foundations of the field.

By becoming proficient in one tool, you will be well-equipped with the skills to learn the next tool more easily. Also, know that the most important skill in analytics is the ability to adapt to new technologies.


5.  Sharpening your business acumen

In the world of data analytics, business skills like negotiation, creativity, persuasion, and leadership are important for roles that are higher up in the hierarchy. Such roles will need you to be acutely in tune with the pulse of the business, understand terminologies, have good organisation skills and be capable of driving and influencing change.


6.  Expanding your network

Networking is a skill that’ll be an important part of your career advancement strategies. Expanding your network is important.

  • Get the word out about your interest in analytics
  •  Find out about friends and peers in similar fields or roles and ask for an introduction
  • Get in touch with top course creators
  •  Enrol for relevant courses such as a Data Analytics and Visualisation course with Excel certification
  •  Build important connections with their alumni, industry partners, and other professionals
  •  Practice for your interviews to make the best of opportunities

Career transitions don’t happen overnight. While there is no shortcut to becoming a data analyst, the above steps will help you transition well into the role you desire. Remember to have fun solving data problems, learning new tools, and building connections while you pave the way for a lucrative career in data science.

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