Mastering Data Analytics: A Comprehensive Guide through PG Diploma

In the modern dynamic business landscape, the demand for skilled data scientists and analysts is soaring. Aspiring individuals looking to make their mark in the data analytics domain can embark on a transformative journey through a well-structured post graduate in data science. This article will delve into the comprehensive syllabus structure across four terms, designed to equip participants with the necessary skills to navigate the complexities of data analytics.

Term 1: Foundation and Exploration

• Orientation, Industry Landscape, and How to Succeed

The first term of post graduate in data science kicks off with an insightful induction module, providing an overview of the current market scenario and the essential qualities needed to thrive in the data analytics field. Emphasis is placed on understanding how analytics drives global business decisions and impacts revenue. The module focuses on evaluating core skills, such as simplifying complex ideas, curiosity in business, AI and machine learning proficiency, and a holistic view of organizational architecture.

• Building Blocks (Basics of Mathematics & Statistics, Fundamentals of Programming)

Candidates who choose to do post graduate in data science delve into the basics of statistics and programming, essential for those without prior knowledge. The module aims to demystify the misconception that computer science is solely about programming, encompassing graphics, logical functions, and algorithm design. Participants also recapitulate calculus, linear algebra, and statistics, emphasizing the importance of mathematics in advancing a data science career.

• Data Analytics and Visualization using EXCEL and POWER BI

This module focuses on analyzing consumer behavior through data analytics, a critical aspect of personalized business strategies. Candidates learn data analytics components, and types (qualitative and quantitative), and gain hands-on experience with EXCEL. Additionally, the module equips participants with visualization skills, covering types, techniques, and tools such as Tableau, Google Data Studio, and Power BI.

• RDBMS + ETL – SQL for Data Science – Introduction to Cloud Computing

Understanding the relational database management system (RDBMS) and Extract, Transform, and Load (ETL) processes becomes crucial in this module. Participants learn how ETL tools extract and transform data in SQL interfaces. Simultaneously, the module introduces cloud computing and its connection to RDBMS, highlighting its role in data analysis through shared resources.

Term 2: Advanced Data Modeling and Machine Learning

• Business Problem Solving: Predictive Modeling using Python

This term of post graduate in data science delves into predictive modeling using historical data and programming languages like Python. Participants learn to read data patterns and trends, enabling them to build predictive models for informed decision-making.

• Machine Learning using Python (Supervised and Forecasting Methods)

Candidates explore practical applications of machine learning across industries, gaining proficiency in supervised learning and forecasting methods. The focus is on minimizing loss functions and improving forecasting accuracy.

• Unsupervised Learning using Python and MLOps (Clustering, PCA, and Recommendation System)

This module of post graduate in data science introduces unsupervised machine learning, covering clustering, Principal Component Analysis (PCA), and Recommendation Systems. Participants understand data interpretation methodologies and AI implementation for accurate product recommendations.

Term 3: Specialized Applications and Functions

• Text Mining and NLP using Python

This module introduces Natural Language Processing (NLP) and text-mining concepts using Python. Participants learn machine learning algorithms for text mining and NLP, developing skills to analyze and extract information from structured and unstructured content.

• Value Proposition of Analytics in different functions (Marketing, Risk, and Operation)

Candidates studying post graduate in data science explore data analytics applications in marketing, risk management, and operations. Marketing analytics covers descriptive, predictive, and prescriptive analysis. Risk analytics equips participants to measure, assess, and manage risks. Operations analytics focuses on data-driven business decisions for improved efficiency.

• AI & Deep Learning using Python – Computer Vision, Text Mining – Elective (Option I)

An elective module offers insights into AI, deep learning, computer vision, and text mining. Participants understand the distinctions between AI and deep learning, delving into computer vision’s role in deriving meaning from digital images and text-mining techniques using NLP.

• Big Data Engineering using Hadoop Ecosystem & Spark/ PySpark – Elective (Option II)

The second elective specialization covers big data engineering, emphasizing frameworks like Hadoop, Cassandra, Apache Storm, and Spark. Participants learn to handle large datasets, analyze them, and derive meaningful insights.

Term 4: Culmination and Practical Application

• Industry Capstone Project work, Dissertation & Final Viva

The final term of post graduate in data science focuses on the practical application of knowledge through a capstone project. Participants choose from diverse project options, such as sports event analysis, consumer electronics pricing analysis, telecom churn prediction, credit card spending prediction, and more.

• Problem Solving (Frameworks, Approaches)

Participants enhance problem-solving skills, breaking down complex problems into logical steps. Mentors guide them to adopt a data-driven approach for robust business outcomes.

• Placement Preparation

The program concludes with career development support, resume reviews, and interview preparations. Participants receive one-on-one counseling and mock interviews, ensuring they are well-prepared for their dream roles.