PG Diploma in Business Analytics

Python is a dynamic programming language widely utilized in business analyst classes for data analysis tasks. Its powerful libraries, such as Pandas, NumPy, Matplotlib, and Seaborn, simplify data processing, visualization, and statistical modeling, making it ideal for BA analyst courses and business analytics classes.

Expertise attained: 

• Python Proficiency
• Data Visualization
• Data Structures
• NumPy arrays and functions

Statistics forms the core of business analytics by providing techniques for data collection, interpretation, and decision-making. It teaches you how to perform statistical analysis using python and helps in identifying patterns, making informed predictions, and understanding data variability, which is crucial in business analytics in business applications.

Expertise attained: 

• Logistic Regression

• Data Interpretation

• Statistical Techniques

• Decision-Making Skills

Advanced Analytics equips learners with the skills to build predictive models using R and Python, enabling data-driven forecasting and decision-making. It covers advanced analytics techniques, including regression analysis, classification, and clustering, applied to real-world business scenarios. By mastering these tools, learners can uncover patterns and trends to solve complex business challenges effectively.

This module covers techniques to interpret financial statements and analyze key performance metrics. It provides insights into evaluating a company’s financial health, profitability, and sustainability. Financial analytics is the field that provides high level views of a company’s financial data. Helping the company to improve its business performance. In this statement Analysis module, students will be exposed to different tools and techniques that can be used to extract meaningful information from the raw financial data of a company.

Expertise attained: 

• Understanding of Financial Statements.
• Trend and Variance Analysis.
• Financial Performance and Decision-Making.
• Financial modelling and analysis: Time series models.

Learn the fundamentals of SAS programming for data manipulation, statistical analysis, and report generation, enabling efficient data handling.

Expertise attained:

• Writing and debugging SAS programs.
• Data cleaning and transformation using SAS.
• Skills to automate workflows to generate dynamic reports.

This module emphasizes creating impactful data visualizations to communicate insights effectively using Tableau’s interactive features. Through visually appealing charts, and graphs, data visualisation allows analysts and decision-makers to grasp the fundamental characteristics of the data, identify outliers, discern patterns, and draw preliminary conclusions. This module provides actionable insights for improved business

Expertise attained: 

• Proficiency in creating dashboards and storyboards.
• Ability to design interactive and user-friendly visualizations.
•  Visualisation with tableau
• Skills to interpret data trends and present business insights. strategies and problem-solving.

Explore machine learning algorithms and their applications for predictive analytics, pattern recognition, and data-driven decisions. Students will get expertise in supervised and unsupervised learning. Unsupervised machine learning involves deriving patterns, structures, or insights from unlabelled data. The particular model is focusing to uncover hidden patterns, group similar data points, or reduce the dimensionality of the data without predefined target values. This module will also cover supervised machine learning to train a model on a labelled dataset.

Expertise Attained:

• Understanding supervised and unsupervised learning techniques.
• Evaluating machine learning models.
• Bagging and boosting techniques
• Apriori algorithm

Data mining module will provide understanding of data mining tools. Learn these data mining techniques to uncover patterns, relationships, and insights from large datasets for strategic decision-making.

Expertise attained: 

• Understanding data mining tools.
• Patterns and trends extraction.
• Proficiency in association rule mining and anomaly detection.
• Data mining for business problem-solving.

Option 1: Marketing Analytics 

Option 2: HR Analytics

Option 3: Fraud Analytics

Choose an optional paper from the above options and students need to choose specialized topics to gain in-depth knowledge and skills aligned with your career goals and industry requirements. Marketing analytics involves tracking and analysing data from various sources to increase sales and meet marketing targets. With the advent of e-commerce and social media, marketing analytics serves as a critical tool for digital marketing.

Expertise attained: 

• Advanced analytical techniques.
• Skills tailored to industry-specific challenges.
• Enhanced problem-solving abilities in a focused domain.

Apply theoretical and practical knowledge to solve real-world business problems in a comprehensive project. Win In Life’s Capstone project explained from scratch to advanced level, thereby making the students industry ready.

Expertise attained:

• Hands-on experience in data-driven problem-solving.
• Ability to work on end-to-end business analytics projects.
• Skills in presenting findings and actionable recommendations.
• Proficiency in leveraging tools and techniques for practical application.