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Comparing Business Analytics and Data Science in PGDM

05 February 2024

Business Analytics and Data Science are two closely related but distinct fields that have gained significant traction in the business world. As organisations strive to harness data for strategic decision-making, the demand for professionals skilled in Business Analytics and Data Science has increased rapidly.

Business Analytics systematically explores an organisation's data, emphasising statistical analysis, predictive modelling, and data mining. It uses statistical and quantitative methods to derive insights, make informed decisions, and solve complex business problems.

On the other hand, Data Science is a broader field encompassing a range of techniques for handling and analysing data. It involves collecting, cleaning, and analysing structured and unstructured data to extract valuable insights and make informed decisions.

Course duration
PGDM programmes specialising in Business Analytics typically have a duration of two years. The coursework focuses on applying analytics to business problems, emphasising decision-making based on data-driven insights. The shorter duration allows for a focused exploration of business analytics principles, preparing students for roles that address the gap between data analysis and business strategy.

PGDM programmes with a Data Science focus may vary in duration but generally follow the standard two years. These programmes delve into a broader range of data-related topics, covering statistical methods, machine learning, and Big Data technologies. The course duration allows students to get an in-depth understanding of the technical skills required for roles in Data Science.

In a PGDM programme in Business Analytics, the curriculum typically emphasises the application of analytical tools to solve business problems. It covers statistical methods, predictive modelling, and tools like SQL and business intelligence software. Students develop data interpretation, critical thinking and decision-making skills, business economics, operations research, programming for analytics, predictive analytics, blockchain technology, and the ability to use analytics tools to solve business challenges.

Core subjects include:

  1. Introduction to Business Concepts
  2. Statistical Analysis
  3. Quantitative methods
  4. Data Management
  5. Database Systems
  6. Data Visualisation with tools like Tableau or Power BI
  7. Predictive Modelling
  8. Business Intelligence

Conversely, a Data Science concentration in a PGDM programme delves deeper into the technical aspects of data processing. This concentration often attracts individuals with a more robust technical background. Core subjects include:

  1. Programming languages
  2. Algorithmic techniques
  3. Programming and Software Development
  4. Machine Learning and Predictive Modeling
  5. Big Data technologies like Hadoop and Spark
  6. Data Engineering
  7. Deep Learning and Neural Networks
  8. Experimental design and A/B Testing
  9. Ethics and responsible AI

Career opportunities
Analysing career opportunities within the two disciplines can help future aspirants make informed decisions based on their interests, learning styles, and career goals. MarTech estimates that there are 2.7 Zettabytes of data in the digital universe; if most of this data is unstructured, many data analysts will be needed to analyse it all. This implies there are a lot of employment chances in Business Analytics now more than ever. Some of the career opportunities that you can get after a Business Analytics degree are:

  1. Data analyst, analyses business data to provide actionable insights
  2. Business Intelligence analyst, utilises data to enhance business decision-making
  3. Operations analyst, improves efficiency through data-driven strategies
  4. Financial analyst, uses data to assess economic trends, evaluate investment opportunities, and make informed financial decisions

While Business Analytics and Data Science have distinct career paths, there is a growing convergence, especially in roles that demand business acumen and technical proficiency. A degree in Data Science can open doors to specialised roles with a strong technical orientation.

  1. Data scientist, applies statistical analysis and Machine Learning principles to extract insights
  2. Machine Learning engineer, develops algorithms for predictive modelling
  3. Big Data analyst, handles large datasets using different tools and technologies
  4. Data engineer, focuses on designing, constructing, and maintaining the hardware and software required for processing and storing data

Business Analytics and Data Science play integral roles in shaping the future of business. A PGDM programme focusing on either discipline equips graduates with the skills to navigate the increasingly data-driven landscape, making them valuable assets in various industries. The choice between a Business Analytics and a Data Science concentration in a PGDM programme ultimately depends on their individual career goals, background, and the level of technical proficiency they seek.

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