Digital Upskilling Training for Banks

• Introduction to Data Science
• Data Science vs Business Analytics vs Big Data 
• Classification of Business Analytics
• Data Science Project Workflow
• Various Roles in Data Science
• Application of Data Science in various industries

• Introduction to Statistics
• Harnessing Data
• Exploratory Analysis
• Distributions
• Hypothesis & Computational Techniques 
• Correlation & Regression

• Install SQL packages and Connect to DB
• RDBMS (Relational Database Management) Basics 
• Basics of SQL DB, Primary key, Foreign Key
• SELECT SQL command, WHERE Condition
• Retrieving Data with SELECT SQL command and WHERE Condition to Pandas DataFrame
• SQL JOINs
• Left Join, Right Joins, Multiple Joins

• Advanced Machine Learning Concepts 
• Tuning with Hyperparameters
• Random Forest – Ensemble
• Ensemble Theory, Random Forest Tuning 
• Support Vector Machine (SVM)
• Simple and Multiple Linear Regression, KNN
• Natural Language Processing (NLP)
• Text Processing with Vectorization, Sentiment Analysis with Text Blob, Twitter Sentiment Analysis
• Naïve Bayes Classifier
• Naïve Bayes for Text Classification, New Articles Tagging 
• Artificial Neural Network (ANN)
• Basic ANN network for Regression and Classification 
• TensorFlow Overview
• Deep Learning Intro

• Basics of Application Program Interface (API)
• API basics, loosely Coupled Architecture
• Installing Flask
• Installation and configuring Flask and cross-domain authentication
• End to End ML project with API Deployment
• Complete Project Flow with API Deployment and assess through the website

Finance Overview

• Introduction
• Key roles and responsibilities 
• Key Processes and Metrics

Data Science Workflow

• Data Science in Finance
• Data Science professionals’ roles
• Descriptive Analytics to derive insights from data
• Workflow Optimization and Process Improvements

• Introduction to Data Science with Python
• Python Basics: Basic Syntax, Data Structures
• Data objects, Math, Comparison Operators, Condition 
• Statements, loops, lists, tuples, dicts, functions 
• Numpy Package
• Pandas Package
• Python Advanced: Data Mugging with Pandas 
• Python Advanced: Visualization with Matplotlib 
• Exploratory Data Analysis: Data Cleaning, Data Wrangling 
• Exploratory Data Analysis: Case Study

• Visual Analytics Basics 
• Basic Charts, Plots

• Machine Learning Introduction
• What is ML? ML vs AI. ML Workflow, Statistical Modelling of ML
• Application of ML
• Machine Learning Algorithms
• Popular ML algorithms, Clustering, Classification, and Regression, Supervised vs Unsupervised.
• Choice of ML 
• Supervised Learning
• Simple and Multiple Linear Regression, KNN, and more 
• Linear Regression and Logistic Regression
• Theory of Linear regression, hands-on with use cases 
• K-Nearest Neighbour (KNN)
• Decision Tree
• Naïve Bayes Classifier
• Unsupervised Learning: K-Means Clustering

• What is a Time-Series?
• Trend, Seasonality, Cyclical and Random 
• White Noise
• Auto Regressive Model (AR)
• Moving Average Model (MA)
• ARMA Model
• Stationarity of Time Series
• ARIMA Model – Prediction Concepts 
• ARIMA Model Hands-on with Python 
• Case Study Assignment on ARIMA

• Introduction to Deep learning
• What is Deep Learning?
• Various Deep Learning models in practice and applications 
• Convolutional Neural Network CNN Intro
• Case Study: Keras–TensorFlow Image Classification
• CNN hands-on application for classification of images of Cats and Dogs

Application In Finance

• Predictive Analytics core concepts 
• Machine Learning application in Finance

Challenges Of Data Science In Finance

• Limitations of Data Science adaption in Finance 
• Challenges in transition 

Data Science Use Cases In Practice In Finance

• Data Science Industry use a case in the Finance domain