How To Start A Career In Data Science

A data scientist is someone who helps an organisation to make critical decisions through data analysis, modelling, visualisation, among others. According to the survey reports, Data Science and analytics ecosystem has been witnessing an overall growth in the number of jobs with India contributing to 6% of open job openings worldwide. 

Currently, the total number of analytics and data science job positions available are more than 90,000 and compared to the worldwide estimates, India contributes 6% of open job openings. In this evolving talent market, aspirants have been developing a blend of skills in order to strive ahead in the role of data science.

While taking this journey, there are a few crucial questions that keep roaming inside the head. For instance, what are the most essential tools? which path to follow? What new can you learn? among others.   

Today, this article attempts to evaluate all these crucial questions and will take a more in-depth look at how to choose the right career in Data Science. Here, we list down 7 points following which an aspirant will inevitably move in the right direction.

Mastering Tools

In order to become a good data scientist, an aspirant must have blended knowledge of BI tools, cloud solutions, visualisation tools, programming languages, data management tools, among others. Languages like Python, Java, R, SQL continue to dominate the market as the tools of choice among the data analysts and data scientists. According to reports, almost 17% of all advertised analytics jobs in India demand for Python as a core skill whereas 16% demand Java. R skills come third in the most critical skills required with 10% of all analytics jobs looking for R professionals.

Find Your Area of Interest

The Data Science domain is huge and one needs to be clear in the head as which role to choose and start preparing accordingly. There are a number of job roles in data science which are currently available among organisations such as machine learning engineer, data architect, quantitative analyst, big data engineer, data scientist, adat visualisation expert and several others. One must get clear what these job roles do and then decide what you should become.  

Learn To Implement Big Data Technologies

With the numerous amount of data gathered every day in the organisations, Big Data has gone through significant disruptions in these few years. According to reports, the analytics industry has grown to $3.03 billion in size last year and is expected to double by 2025. One must learn to implement Big Data techniques using tools such as Tableau, SQL, NoSQL, Hadoop, Pig, Hive, among others. This will not only help you to understand the technologies but also make you adequate to deploy enterprise information management and solve business problems. 

Hands-On Projects

Undergoing courses is only halfway to the destination. The journey will complete when you start focusing on the practical applications of the topics which you have been learning. Getting your hands dirty with projects will not only help you understand the concept better but also enables you to master the languages and techniques. 

Master Data Visualisation

Data Science profile is all about playing with an enormous amount of data and gain meaningful insights for decision-making in an organisation. Data visualisation is the simplest way to represent and understand data. An aspirant must learn necessary visualisation tools such as Tableau, Qlik, among others.  

Contribute To Open Source Data Science Projects

To create a strong profile in the field of data science, one must contribute to the open-source projects available in GitHub. This will help aspirants to gain the necessary skills that are needed to upgrade the skill-set. Mentioning these projects while in an interview or resume will help an aspirant to accelerate in the career.

Join The Community

Data science community is one of the vast communities around the tech space. For a data science aspirant, it is ideal if one joins the community and broadens the network within the community. This will help in several ways, such as it will help to keep updated as the technology keeps evolving, participate in meet-ups to share as well as gain knowledge.   

Jigsaw Academy provides one of the most in-depth courses in data science and analytics. The most exciting and robust point about the PG course provided by Jigsaw Academy is that all of the points mentioned above can be genuinely related to the Postgraduate Diploma In Data Science course. 

Postgraduate Diploma in Data Science program offered by Jigsaw Academy in association with Equifax is designed to get the aspirants employed by providing them with a broad understanding of the basic and advanced concepts of Data Science. The course is appropriate for fresh candidates as well as working professionals which will enable aspirants to implement Big Data techniques using tools using R, NoSQL, Hadoop, Pig, Hive, etc., learn emerging data science of unstructured data analysis and robotic process automation & more by industry experts, and much more.

The Data Science course is spread over 11 months duration that includes an 8-month classroom program in Bangalore and Hyderabad, followed by a 3-month internship/ project. 

After successful completion of the course, an aspirant will be able to 

  • Understand and use Big Data technologies as enablers to deploy enterprise information management and solve business problems.
  • Learn artificial intelligence and a neural network that emphasises the creation of intelligent machines.
  • Communicate analytics problems, methods, and findings effectively orally, visually, and in writing.
  • Help Companies make critical decisions through analysis, modelling, visualisation, etc.
  • Learn the emerging data science of unstructured data analysis and robotic process automation by choosing elective based on your area of interest.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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