Course Overview

Python Programming

MODULE 1 PYTHON BASICS 

• Introduction of python
• Installation of Python and IDE
• Python objects
• Tokens in Python and Variables

MODULE 2 PYTHON DATA TYPES

• Basic data types in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

MODULE 3 PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 4 PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

MACHINE LEARNING ASSOCIATE

MODULE 1 MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI Vs DL
• Types of ML Learnings
• Supervised Vs Unsupervised Vs Reinforcement

MODULE 2 PYTHON NUMPY PACKAGE

• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays

MODULE 3 PYTHON PANDAS PACKAGE

• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Operations performed in Data Frame

MODULE 4 VISUALIZATION WITH PYTHON - Matplotlib

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter, Histogram etc.

MODULE 5 PYTHON VISUALIZATION PACKAGE - SEABORN

• Seaborn: Basic Plot
• Advanced Python Data Visualizations

MODULE 6EVALUATION METRICS

• Types of Evaluation metrics
• Coding wise – Evaluation metrics

MODULE 7 ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python

MODULE 8 ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python

MODULE 9 ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works: K Means theory

MODULE 10 ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python

MACHINE LEARNING 

MODULE 1 ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 2 PRINCIPAL COMPONENT ANALYSIS (PCA)

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python

MODULE 3 ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python

MODULE 4 ENSEMBLE TECHNIQUES - BAGGING

• Introduction to Ensemble technique and Bagging
• Modeling and Evaluation in Python

MODULE 5 ML ALGO: NAÏVE BAYES

• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python

MODULE 6 GRADIENT BOOSTING, XGBOOST

• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python

MODULE 7 ADVANCED ML CONCEPTS

• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set : Smote Technique
• Feature Selection Techniques

MACHINE LEARNING EXPERT

MODULE 1 Statistics

• Population Vs Sample
• Central Tendencies
• Correlation vs Co variance
• CLT - theorem
• Measures of dispersion
• Hypothesis Testing
• Z statistic, t- statistic , p- value , Significance level

MODULE 2 TIME SERIES FORECASTING - ARIMA

• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA and SARIMA model
• Autocorrelation and AIC
• Time Series Analysis in Python

MODULE 3 FEATURE ENGINEERING

• Introduction to Feature Selection & Methods
• Wrapper method: Forward selection, Backward Elimination, Exhaustive Selection
• Filter method & Types
• Filter method: Variance threshold, Correlation coefficient, Chi-Square
• Embedded method: Regularization, Treebased method

MODULE 4 REGULAR EXPRESSIONS WITH PYTHON

• Regex Introduction
• Regex codes
• Text extraction with Python Regex

MODULE 5 ML MODEL DEPLOYMENT WITH FLASK

• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment of a Project

MODULE 6 ADVANCED DATA ANALYSIS WITH MS EXCEL

• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL

MODULE 7 AWS CLOUD FOR DATA SCIENCE

• Introduction of cloud computing
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)

MODULE 8 INTRODUCTION TO DEEP LEARNING

• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python

MODULE 9 Deep Learning model with images

• Introduction to Image processing
• Pytorch Vs Tensorflow
• Dense Net, Vgg16,  YOLO models  in python

MODULE 10 Artificial Intelligence

• Introduction to Artificial Intelligence
• LLms model and OpenAi
• Langchain Applications
• RAG application , Text Summarization and Chatbot creation

DATABASE: SQL AND MONGODB

MODULE 1 DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• Relational Database Management System

MODULE 2 SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation

MODULE 3 DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4 DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5 SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
• Windows functions: Over, Partition , Rank

MODULE 6 SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7 DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods

VERSION CONTROL WITH GIT

MODULE 1 GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2 GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub

MODULE 3 COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4 TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 5 GIT WITH GITHUB AND BITBUCKET

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6 GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers

BIG DATA FOUNDATION

MODULE 1 BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2 HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort

MODULE 3 PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4 SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive

CERTIFIED BI ANALYST

MODULE 1 POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 2 DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 3 CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model