5 Reasons to Build a Career in Data Science


Data Science is one of the major disruptive technologies which has huge scope as a career. However, aspiring data scientists have many queries regarding the field. To answer their queries and give them a clear idea, TalentSprint organized a webinar on “5 reasons to build a career in Data Science” on 22nd May 2020. The webinar was addressed by Mr. Rahul Kumar, Associate Director, TalentSprint.

The participants got valuable insights into the field of Data Science. They also learned in detail about the expertise that an aspiring Data Scientist must have. Here is an excerpt from the insightful webinar.

Highlights of the webinar

  • Data Science Processes- Tool Mapping
  • 5 Reasons to Build a Career in Data Science
  • Industry 4.0-The Future is Here
  • How to become a Data Scientist

Data Science Processes- Tool Mapping

Data Science is an interdisciplinary field which is an intersection of programming, mathematics and statistics along with the business expertise. Though the term Data Science is relatively new, this field has effectively evolved over a period of time and it has lots of processes and tools mapped to it.

Data Science follows several processes like sourcing and storing data. All the processes in Data Science effectively require many tools. Here are all the processes and the respective tools 

  • Sourcing and Storing data- Sourcing and storing data effectively is a crucial process in Data Science. The tools that help in this process are MongoDB, Cassandra etc.
  • Convert and ETL – For the conversion and extraction of data one of the good tools is Apache Sqoop.
  • For the transformation of data the tool that is required is Apache Hive
  • Exploratory Analysis- Elastic Search and KNIME can help us explore the data
  • Model Build and general insights- Python, Julia,SaS etc. can help in building the model and generating the insights 
  • Visualization- Gephi, SAP business objects, Tableau etc. can help in the visualization of data.
  • Model execution in production- Java, Hadoop, C#, Srom, Scala etc. help in the execution of the Data Science models in production environments.

Five Reasons to build a career in Data Science

Reason 1: Data is the fuel for all businesses

All business decisions and strategies are data driven because of the availability of huge data. We have been using lots of apps which in turn are generating loads of data. There are different kinds of data coming at different speeds from multiple sources which is why the data volume keeps increasing. 

Below are 3 characteristics of Big Data: 

  • Data Variety  
  • Data Velocity
  • Data Volume

Because of this  data explosion, a Big Data ecosystem has been created. And different companies are providing services in different parts of this ecosystem.

  • Data Centers and Hardware- Some of the companies that are offering services in these area of the ecosystem are Dell, IBM, HP, Oracle, EMC, VMware
  • Infrastructure and Network-  Some of the companies that are offering services in these area of the ecosystem are IBM, Oracle, Cisco, Dell, HP
  • Storage- Some of the companies that are offering services in these area of the ecosystem are NetApp, Dell, EMC, IBM, 
  • Databases-  Some of the companies that are offering services in these area of the ecosystem are IBM, Oracle, SAP, HP
  • Big Four Tech Giants- Facebook, Google, Apple, Amazon
  • Specialized ISVs–  Some of the companies that are offering services in these area of the ecosystem are Microsoft, SalesForce, EveryAngle
  • Large/Analytics ISVs-  Some of the companies that are offering services in these area of the ecosystem are SAP, SAS, Oracle, IBM, Microstrategy, 
  • Integration- Some of the companies that are offering services in these area of the ecosystem are Atos ,Capgemini, TCS
  • Services-  Some of the companies that are offering services in these area of the ecosystem are KPMG, Deloitte, Accenture, IBM

If you aspire to be a part of any one of these companies then learning Data Science is the best option you can look for. 

Reason 2: Data Science is an ‘in-demand’ skills with higher salaries 

  • Currently Data Science is an in-demand skill. According to a study from NASSCOM, industry needs 140k Data Science Professionals as of the year 2020. 
  • Number of  entry-level jobs is increasing and freshers are being hired in huge numbers by companies. 
  • People who are already there in the industry and looking for a transition to Data Science have a bright future in this field as employers are giving priority to such candidates while hiring.
  • However there is a demand-supply gap of talent with a thorough knowledge in Data Science. Lots of certification courses can be done by students. 
  • Since  there is a demand-supply gap, applicants will get better salaries. The demand-supply gap is creating huge opportunities for students to build a successful career in Data Science. 

Reason 3: You(Anyone) can acquire Data Science expertise

Data Scientists can emerge from any field. Anyone can build Data Science expertise regardless of their backgrounds. Students from fields like Mathematics, Computer Science, Engineering, Social Sciences, Commerce, Business Administration and Physical Sciences can also build a career in Data Science. They don’t need to be from IT or computer science background to become a Data Scientist. They just need to have the basic expertise and a good knowledge in Maths and Statistics. 

5 ReasonsReason 4: A wide range of opportunities await your expertise

You have a wide variety of opportunities waiting for you to develop the expertise in Data Science. Data Science is multidisciplinary. It has disciplines like Databases, Data Mining, Machine Learning, Neurocomputing and Visualizations. Apart from the technical skills there are wider opportunities in the business domain as well. 

Few Job Roles listed below

  • Data Creator: Researchers with domain expertise who produce data. These people may have a high level of expertise in handling, manipulating and using data.
  • Data Scientist: People who work where the research is carried out-or in close collaboration with the creators of the data- and may be involved in creative enquiry and analysis, enabling others to work with digital data, and developments in database technologies
  • Data Manager: Computer Scientists, information technologists or information scientists and who take responsibility for computing facilities, storage, continuing access and preservation of data.
  • Data Librarian: People originating from the library community, trained and specialising in the curation, preservation and archiving of data. 

There are wide range of job titles in Data Science

It has huge applications across industries like Healthcare, Social Media, Sales, Marketing, Credit Insurance, Travels and all. Lots of MNCs have started hiring huge numbers of data scientists to meet their demands. Below are few job titles that are there in the field of Data Science. 

  • Business Intelligence Specialists
  • Data Modeler
  • Data Curation Specialists
  • Metadata Librarian
  • Data Mining Specialist
  • Market Research Analytics
  • SAS Programmer
  • Data Mining Specialist
  • Data Analytics Engineer
  • Data Visualization Specialist
  • Data Curation Librarian
  • CMR Analyst
  • Data Manager
  • Data Journalist

Reason 5: It is not just a job, it is a ‘career’ builder

Don’t consider Data Science as just a job, it is a prominent career. It has a huge scope of growth in future for a good 30-40 years. If you have the required expertise and you are willing to learn continuously then you can keep progressing in the same field for a longer period of time. To build your expertise you can take up online certification courses that are widely available. As a data scientist you will improve your problem solving skills along the way. Since most roles in Data Science bridge IT and management, you can experience both the domains and plan your career move.

Industry 4.0- the future is here

With industry 4.0 the world is moving in the direction of major technological changes. The adoption of the latest technology like Artificial Intelligence, Internet of Things, Robotics and Automation, and other tools and technologies have become inevitable. These technological advancements have contributed to making Smart Factories and work-places. It enables people to operate machines and continue most of the manufacturing process through an app, without being physically present at the Unit. 

Also read: Excerpts from the webinar on In-demand IT jobs post COVID-19 by TalentSprint https://www.nationalskillsnetwork.in/excerpts-from-the-webinar-on-in-demand-it-jobs-post-covid-19/

This will generate a massive amount of data which needs to be handled properly. The demand for Data Scientists is going to be high in near future. You just need the right mindset and the basic expertise to build a career in this field which has huge scope. 

How to become a Data Scientist

You need to have few basic expertise to start building a career in Data Science. You must have a good knowledge of technology and programming languages. The most in-demand technologies for Data Science are Python, R, SQL, Spark, Hadoop, Java, Tableau, AWS, SAS, Hive, Scala, C++, Excel and Azure.

Apart from the technical knowledge, there are some nontechnical skills that a person must have to become a successful Data Scientist.

  • You must have analytical mindset
  • You Should be creative in problem solving
  • You must be curious about the data
  • You must think practically. Being pragmatic is a must have skill., 
  • Business thinking 

The Data Scientist’s skill-set as a student

  • Programing Languages
  • Data Extraction and Processing
  • Data Wrangling and exploration
  • Statistics
  • Data Visualization
  • Big Data Processing Framework
  • Deep learning
  • Machine Learning

The webinar was enlightening and answered most of the queries of the participants regarding a career in Data Science. As one of the major disruptive technologies Data Science is going to change the job scenario in the near future. Those who aspire to build a career in Data Science should have all the required expertise and should be open for consistent and continuous learning.

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