PG Certificate in Data Analytics in Finance Overview

Introduction (Online Live Classes / 6 Months)

The PG Certificate in Data Analytics in Finance is designed to equip finance professionals, data analysts, and aspiring fintech specialists with the analytical skills required to make data-driven decisions in the financial industry. This program blends finance, statistics, and data science, providing hands-on exposure to financial datasets, machine learning applications in finance, and advanced quantitative techniques.

With a growing reliance on data for investment strategies, risk management, and financial decision-making, this course prepares learners to tackle real-world financial problems using data analytics tools like Python, R, SQL, and AI-driven techniques.

Benefits of the Course

1. Gain industry-relevant expertise in financial data analytics.
2. Learn Python, R, and SQL for data analysis and financial modeling.
3. Develop skills in predictive analytics, machine learning, and AI applications in finance.
4. Work on real-world datasets and case studies from banking, fintech, and investment firms.
5. Learn from experienced industry professionals and academicians.

Course Modules

Module 1: Introduction to Financial Data Analytics

1. Fundamentals of Financial Data Analytics
2. Importance of Data in Financial Decision-Making
3. Overview of Financial Markets and Instruments
4. Introduction to Python, R, and SQL for Finance
5. Exploratory Data Analysis (EDA) for Financial Datasets

Module 2: Data Handling with Python & SQL

1. Financial Time Series Analysis
2. Data Wrangling & Cleaning (Handling Missing Data, Outliers)
3. Data Visualization with Matplotlib, Seaborn, and Plotly
4. SQL for Financial Data Extraction & Analysis

Module 3: Applied Statistics and Financial Modelling

1. Regression Models for Forecasting Financial Trends
2. Descriptive & Inferential Statistics, Hypothesis Testing and Confidence Intervals

Module 4: Machine Learning for Financial Applications

1. Supervised vs. unsupervised learning
2. Models: Decision Trees, Random Forests, XGBoost, SVM

Module 5: Capstone Project

1. Hands-on project in financial data analytics
2. Real-world datasets from banking, investments, or fintech firms
3. Implementing an end-to-end data analytics pipeline in finance
4. Presentation & business insights derived from financial analytics

Who Should Enroll?

1. Finance professionals looking to upskill in data analytics.
2. Data analysts aspiring to work in financial services.
3. FinTech professionals and startup enthusiasts.
4. Students or graduates interested in finance, investment, or risk analytics.
5. IT professionals transitioning to financial data science roles.

Have questions? Reach out to us at contacts@Lsbcaglobe.com