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Introduction to Python Programming

• What is Python programming language ?
• Why Python programming language required for Data Science ?
• What is Anaconda ?
• Installation of Anaconda
• Understanding Jupyter notebook
• Basic commands in Jupyter notebook
• Understanding Python Syntax
• Data types in python broadly discussed

Literals, Keywords and Data Types

• What is an Identifier?
• Rules for Identifier Naming
• Inbuilt Keywords
• Variables and Data Types
• print() and input()

Python Operators

• Arithmetic operators
• Assignment operators
• Comparison operators (relational)
• Logical operators
• Identity operators

Conditional Statement

• What are conditional statements ?
• if Statement
• if else Syntax
• if elif else
• nested if and if else
• elif ladder

While Loops & While-Else

• What is loop ?
• Why loops are used ?
• while loop syntax
• Control Flow
• Solving Pattern Problems

Lists

• What is a list ?
• How to define a list ?
• Accessing list elements using indexing and slicing
• Builtin methods
• Basic examples using lists to print elements at even index, etc

Tuple & Set

• What is tuple ?
• How to create a tuple ?
• Accessing tuple elements using indexing and slicing
• Differences between lists and tuples
• What is Set ?
• How to create a set ?
• Builtin methods
• Set operations
• Manipulating and accessing sets

Dictionary

• What is Dictionary ?
• How to create a dictionary ?
• Adding, Modifying and Retrieving values from a dictionary
• Getting all methods
• Builtin methods
• Differences between sets and dictionaries
• Mutable vs Immutable Data Types

Strings

• What is string ?
• Create a string
• Accessing a string using indexing and slicing
• Built-in methods
• format() method
• User input / Dynamical entry
• Type-casting of string

For Loop, For-else & Comprehension

• Introduction to for loop, range(), enumarate(),
• For-else
• List Comprehension & Dictionary Comprehension
• Problem Solving wrt to String Methods utilizing for loops & conditional Statements makingf use
• of chr() and ord()
• Problem Solving with List and Dictionary
• Comprehension

Functions

• What is function ?
• Why functions are used ?
• Terminologies used in functions ?
• How to define a function ?
• How to call a function ?
• Positional and Keyword arguments

Higher Order Functions

• Functions with/without return values
• lambda functions
• Map, filter & reduce
• Namespaces
• Modules and Package
• Using conditional statements
• Using Loops
• Using Functions

Module & Packages

• Modules and Package
• Datetime
• Random
• math,os
• Creating custom module

OOPS – Classes and Objects

• What is a Class ?
• How to create a class ?
• What are the properties and methods of a class ? What is an Object ?
• How to create an Object ?
• How to access the properties and methods of a class using an Object ?
• What is a Constructor ?
• What are the Types of variables ? What are the Types of methods ? Access modifiers

Inheritance & Abstraction

• Inheritance:
• Single Inheritance
• Multilevel Inheritance
• Multiple Inheritance
• Hierarchical Inheritance
• Hybrid Inheritance and MRO Abstraction

Polymorphism and Encapsulation

• Polymorphism:
• Dynamically Typed or Duck Typing
• Operator, Method and Constructor Overloading
• Method and Constructor Overriding Encapsulation
• Setter and Getter

File Handling & Exceptional Handling

• What is a file ?
• How to create a file ? How to open a file ? How to close a file ? Writing to a file Appending to a
• file Reading from a file Testing file’s existence
• What is an exception ? Raising exceptions try finally
• Custom exceptions

VS Code & Streamlit Deployment

• What is streamlit
• Components of Streamlit
• Creating an application with Streamlit
• VS Code, Setting Environment
• Executing programs in VS Code.

Backend with FastAPI

• Client Sever Architecture
• Introduction to Fast API
• Request Methods
• Hands on FastAPI
• Application building
• Integration of Fast API with streamlit
• Case Study

Introduction to EDA

• How is EDA different from python programming?
• EDA vs Python with case study.
• Types of data (Numerical, Categorical)
• Types of Analysis (Nonvisual and visual, Univariate and Bivariate, Descriptive, Inferential & Probabilistic)

Descriptive Statistics

• What and why Statistics?
• Data and its Measures
• Measures of central tendency (Univariate Analysis)
• Measures of dispersion (Bi Variate)

Core Numpy Operations

• Generating Random Numbers – np.random.rand, np.random.randn, np.random.randint, np.random.uniform
• Indexing and Slicing of Arrays (1D & 2D)
• Indexing with Boolean Arrays
• Updating Numpy Array
• Insert, Append and Delete
• Array Shape Manipulations
  – reshape
  – ravel and flatten
  – transpose
• Problem Solving

Mathematical & Statistical Functions

• Iterating over a 1D and 2D numpy array
• Iteration using np.nditer()
• Python Operators on Numpy Array
• Numpy Maths – sqrt(), exp(), sin(), cos(), add(), subtract(), multiply(), divide(), dot(), @operator
• Numpy Statistics – sum(), min(), max(), mean(), median(), var(), std(), corrcoef()

Vectorization and Advanced Numpy Functions

• Concatenate, vstack, hstack, column_stack, hsplit, vsplit
• Sorting the Data in an array
  – sort
  – argsort
• Broadcasting Arrays
• Numpy where, any

Introduction to Pandas

• Introduction to Pandas
• Pandas Series
• Pandas DataFrame
• Difference between Series and Dataframe
• Creating Series and Dataframes

Data Exploration and Understanding

• Loading data from csv and excel files
• Understanding the dataframe structure
• Dataframe non indexing attributes
• Dataframe utility methods
• Dataframe iteration methods
• Mathematical functions on whole dataframe
• Getting column index/label
• Changing column labels
• Selecting, swapping columns
• Adding new columns/dropping existing columns
• Vectorized arithmetic operations
• Datatype conversions
• Math and statistical functions on columns

Groupby and Pivot on Data Frame

• Summarizing data using groupby()
• Multidimensional aggregation using pivot table()
• Categorical analysis using crosstab

Working With Multiple Tables Using Joins (Merge Operation)

• Concatenation – vertical and horizontal stacking
• Merging (merge) – inner, outer, left and right joins
• Index based merging

Handling Missing Values, String and Datetime Manipulations

• Types of missing data
• Identifying and visualizing missing data
• Missing value imputation
• String operations
• Working with datetime”

Advanced Data Transformations

• apply() and map()
• WIndow functions (rolling(), expanding())
• Handling JSON files
• JSON record_path_normalization

Univariate and Bivariate Statistical Analysis (Non Visual)

• Measures of shape
• Measure of relationship
• With a Case Study Descriptive stats

Introduction to Data Visualization

• Understanding visualization
• Basic structure of a plot – figure, axis, title, legends, ticks, labels, subplots
• Creating basic plots with raw data
• Customizing the plots – colormaps, axis limits, markers, linestyles, colors

Univariate analysis with Matplotlib/Seaborn

• Univariate plots for numeric data
  – Histogram, kde, box plot, violin plot
• Univariate plots for categorical data
  – bar plot, count plot

Bivariate and Multivariate analysis with Matplotlib/Seaborn

• Plots between Numerical variables
  – Scatter plot, heat map, line plot
• Plots between Categorical variables
  – kde plot, box plot, histogram, boxen plot, violin plot
• Plots between a numeric and a categorical variable
  – count plot, stacked bar plot
• Plots for multivariate analysis
  – pairplot, heatmap

Introduction to Probability

• Random Experiment
• Sample Space
• Event
• Axioms of Probability
• Basic Probability Examples

Frequency Tables

• Introduction to Frequency Tables
• Joint Probability
• Marginal Probability
• Conditional Probability

Introduction to Data Distributions, Discrete Data Distributions

• What is a Data Distribution?
• Why Study Data Distribution?
• Key Characteristics
• Types of Distributions
• Introduction to Scipy Library
• Bernaulli, Binomial, and Poisson

Continuous Data Distribution

• Uniform, Normal
• Exponential
• Log Normal
• Pareto Distribution
• 68-95-99.7% Rule
• Verify the Distribution using QQ Plot”

Feature Engineering

• Feature Scaling Normalization and Standardization
• Data Transformation
• Log Transformation and Box-Cox

Introduction to Inferential Statistics

• Inferential Statistics
• Population vs Sample
• Why Inferential Statistics?
• Sampling Techniques
  – Convenience Sampling
  – Systematics Sampling
  – Simple Random Sampling
  – Stratified Sampling
  – Cluster Sampling
• Sampling in Pandas DataFrame

Central Limit Theorem and Point Estimate

• Performing Estimations
• Point Estimate using single sample
• Central Limit Theorem
• Point Estimate using m samples

Confidence Interval Estimate

• Confidence Interval Estimates using 1 Sample (Population Std is known and unknown)
• Confidence Level
• Significance Level
• Critical Value – z score and t -score

Hypothesis Testing and Advanced Statistical Tests

• What is Hypothesis Testing?
• What is p-value?
• Important Hypothesis Tests
• Type I and Type II Errors
• Parametric and Non Parametric Tests
  – chisquare test
  – ANOVA

Regular Expressions

• Pattern Matching Basics
• Email, Phone Number & URL Extraction
• Text Cleaning & Data Validation
• Removing Special Characters
• Regex with Python (re module)

Ethical Web Scraping

• Requests Library
• BeautifulSoup
• HTML Parsing
• CSS Selectors & Inspect Element
• Extracting Tables, Text & Links
• Pagination Handling

Data Cleaning & Preprocessing

• Handling Missing Values
• Removing Duplicates
• String Cleaning
• Formatting & Standardization
• Data Transformation using Pandas

Exploratory Data Analysis (EDA)

• Data Understanding & Profiling
• Statistical Summary
• Data Visualization using Matplotlib & Seaborn
• Correlation & Trend Analysis
• Business Insights Generation

Project on EDA

• Collect real-time data from websites ethically
• Clean and preprocess raw datasets
• Apply Regex for data extraction & validation
• Perform EDA and generate insights
• Build industry-oriented projects

Introduction to SQL

• Data
• What is Database
• DBMS
• RDBMS
• SQL vs MYSQL
• SQL vs NoSQL
• CRUD operations
• Pandas vs SQL

Data Exploration and Data Filtering (DQL and OPERATORS)

• Client Server Architecture
• Workbench introduction
• Select (retrieve)
• Data Exploration
• Selecting columns
• Performing (limit, distinct, aggregation values, indexing and slicing using offset)
• Data Filtering
• Filtering data based of conditions (with all operators(Like,Regexp,Between))

Clauses

• GROUP BY (Aggregate Function)
• HAVING (correlating with get_group in pandas)
• ORDER BY
• CASE
• Order of execution
• Case Study: A Case Study of Clauses

Multiple Tables

• Multiple Tables
• Primary key
• Composite key
• Foreign key
• Types of relationships in SQL
• ER diagram

Joins, Unions And Subquery

• Types of Joins
• Inner join
• Outer join
• Left
• Right
• Cross Join
• Self join
• Set operations
• UNION
• UNION ALL
• Subquery
• Scalar Subquery
• Multiple Subquery
• Correlated Subquery

Temporary Tables

• Derived table
• CTE
• Inbuilt Functions
• Window Functions
• Case Study: A Case Study of Temporary Tables

SQL Fundamentals

• Types of SQL Commands
• Data types
• Constrains (PRIMARY KEY/auto_increment, NOT NULL, UNIQUE, DEFAULT, CHECK)
• Creating table with constraints
• DDL (CREATE, ALTER, DROP, TRUNCATE)
• DML (Insert, Update, Delete)

SQL Database Objects

• Views
• Stored Procedure
• Functions

Advance Topics

• Transaction Control Language
• ACID properties
• (COMMIT, ROLLBACK, Savepoint)
• Triggers

Project on MySQL

• Analyze normalized relational databases and ER diagrams based on real-world business requirements.
• Import and manage CSV datasets in SQL databases.
• Write SQL queries using filters, aggregations, sorting, and joins.
• Develop advanced SQL solutions using CTEs, Views, Stored Procedures, and Window Functions.
• Solve domain-specific business problems and generate actionable insights.

Introduction To Power BI

• What is Business Intelligence?
• Power BI Introduction
• Quadrant report
• Comparison with other BI tools
• Power BI Desktop overview
• Power BI workflow

Data Import And Data Visualizations

• Data import options in Power BI
• Import from Web (hands on)
• Why Visualization?
• Visualization types
• Categorical data visualization
• Trend Data viz

Power Queries

• Power Query Introduction
• Data Transformation – its benefits
• Introducing ribbons
• Queries panel
• M Language briefing
• Power BI Datatypes
• Changing Datatypes of columns
• Filtering
• Inbuilt column Transformations
• Inbuilt row Transformations
• Combine Queries
• Merge Queries

Power Pivot And Introduction To Dax

• Power Pivot
• Intro to Data Modeling
• Relationship and Cardinality
• Relationship view
• Calculated Columns vs Measures
• DAX Introduction and Syntax

Data Analysis Expressions

• DAX logical functions
• DAX text functions
• DAX math and statistical Functions
• DAX aggregation function
• DAX filter function
• DAX time intelligent function
• Creating a Date Dimension table
• Related aspects with tables

Login, Publish To Web And RLS

• Power BI services
• Dashboard creation
• Web Content, Image, Text Box
• Dashboard formatting
• Sharing your dashboard
• RLS introduction

Miscellaneous Topics

• Visual Interactions
• Drill Through
• Drilldown
• Conditional Formatting
• Creating buttons in Power BI reports
• Creating Python Script Visuals

Project on POWER BI

• Understand business problems and define reporting objectives.
• Load data from multiple sources into Power BI.
• Clean and transform data using Power Query.
• Build data models and establish relationships.
• Create DAX measures and calculated columns.
• Design interactive dashboards and reports.
• Generate insights and business recommendations across multiple industry domains.
  

ML vs DL vs AI, Reg vs Class

• Why learn AI/ML?
• AI vs ML vs DL
• Supervised vs Unsupervised Learning
• Various Machine Algorithms
• Classification Task
• Regression Task

Model Building Pipeline

• Understanding the Model Building Phase,Splitting data into train and test
• Data Preprocessing Pipelines
• Intro to Numerical Data Preprocessing
• Intro to Categorical Data Preprocessing

Data Preprocessing – Numerical & Categorical Features

• Introducing sklearn module
• Preprocessing Numerical Data
• Preprocessing Categorical Data
• Nominal Encoding
• Ordinal Encoding

Case Study

• Building Models
• Making Prediction and Evaluation using sklearn
• Case Study

Text Preprocessing

• Text Data – Introduction to NLP
• Why Text data is hard to work with
• Cleaning Text Data
• Tokenisation
• Stop Words
• Lemmatization
• Stemming

Text Transformation

• Bag of Word
• Term Frequency Inverse Document
• Frequency,Spam Ham Detection

Working with Image Data

• Image Data – Understanding Images
• RGB channels
• Images as 3D numpy arrays
• Image Data – Understanding Images
• RGB channels
• Images as 3D numpy arrays

Image Case Study

Mathematical Foundations

• Introduction and why Linear Algebra
• Fundamentals of Vectors and Matrices
• Unit Vector
• Matrices operations
• Dot Product of vectors
• Angle between two vectors
• Projection of a vector onto another vector
• Length of projection
• Distances and Dot Products

kNN for Classification & Regression

• Intuition behind KNN
• Developing the algorithm for KNN
• Solving Classification Problems with KNN
• Code Implementation for KNN

Classification Evaluation Metrics

• Accuracy
• Confusion matrix
• Precision and Recall
• F1 Score
• ROC AUC
• Log Loss

kNN for Regression & Regression Evaluation Metrics

• KNN for Regression
• Regression Metrics
  – MAE
  – MSE
  – R square
  -Adjusted R square

Introduction to Model Selection

• Occam’s Razor
• What is model complexity?
• Complex vs Simple Models

Train and Test Errors

• Overtting and Undertting
• Bias and Variance
• Model Complexity vs Train Test Error
• Parametric vs Non Parametric Models
• Hyperparameter vs Parameters

Hyperparameters and Cross Validation

• Hyperparameter for ML Algorithms
• Regularization
• Hyper Parameter Tuning
• Hyperparameter for ML Algorithms
• Regularization
• Hyper Parameter Tuning

Hyperparameter Tuning with Lab

• GridSearch CV
• RandomizedSearch CV
• Hands on

Naive Bayes Derivation, Solving an Example with NB

• Derivation of Naive Bayes Algorithm
• Solving an example
• Code implementation,NB Example

Decision Tree(DT) Algorithm

• Introduction to rule based approach to solve classification and regression problem
• Building Decision Trees
• ID3, C4.5, C5.0 algorithms to build decision trees
• Concept of Entropy, Information Gain and Gini Impurity
• Python Code Sample

DT for Classification and Regression

• Working of DT with numerical input data
• DT for Regression
• Reduction of Variance

Solving an Example with DT

Equation of Line and Linear Regression, Multiple Linear Regression

• Understanding equation of a Line and Hyperplane
• Intuition behind Linear Regression
• Building a cost function for Regression and Classification Problem
• Mathematical Formulation of Linear Regression
• Calculating the errors
• Python Implementation,Simple Linear Regression vs Multiple Linear Regression
• Issues with Multiple Linear Regression
• Python Implementation
• Issue of Multicollinearity
• Detecting Multicollinearity with VIF

Linear Regression Edge Cases

• Linear Regression with Polynomial Features
• Assumptions of Linear Regression
• Hands on

Gradient Descent

• Understanding Differentiation of a Function
• Slope of Tangent
• Computing Derivative
• Computing Maxima and Minima
• Iterative algorithm to solve the problem of Maxima and Minima
• Update function of Gradient Descent

Logistic Regression

• Geometric Intuition behind Logistic Regression
• Mathematical Formulation of Logistic Regression
• Signed Distance Formulation
• Sigmoid Function
• Numerical Instability Issue
• Assumptions of Logistic Regression
• Code Sample

Support Vector Machines

• Intro to SVM
• Solving classification problem using SVM
• Logistic Regression vs SVC
• Margin Maximization
• Hard Margin Support Vector Classier,Soft Margin Support Vector Classier
• How kernel trick works?
• Pros and Cons of SVC

Parallel Ensembles

• Introducing Model Ensembles
• Voting Ensembles
• Stacking
• Bagging
  – Random Forest-case study

Sequential Ensembles

• Cascading
• Boosting
• ADABoost
• GBDT
• XGBoost
  -case study

Feature Engineering – Transformation, Feature Engineering – Selection

• Revision on Feature Transformation
• Feature Selection Introduction
• Variance Thresholding,Variance Ignition Factor
• Recursive Feature Elimination
• Lasso Regularization
• Decision Tree for Feature Importance
• Implementation using Python

Introduction to Unsupervised Learning and KMeans, K Means ++

• Understanding Unsupervised Learning
• Clustering
• Applications of Clustering
• K-Means Algorithm
• Python Code Sample,K Means++ initialisation

Hierarchical Clustering, Customer Segmentation

• Intro to Hierarchical Clustering
• Merging
• Linkage – Single, Centroid, Complete
• Dendrogram
• Cut Tree, Solving Customer Segmentation using Clustering

PCA and Use Case

• Introduction to Dimensionality Reduction
• Introduction to Principle Component Analysis
• Eigenvalues and Eigen Vectors
• Orthogonality
• Transforming Eigenvalues
• Proportion of Variance Explained in PCA
• Code Implementation

Project on Machine Learning

• Understand business problems and dene machine learning objectives.
• Collect datasets from multiple sources or work with publicly available datasets.
• Perform data cleaning, preprocessing, and feature engineering.
• Conduct Exploratory Data Analysis (EDA) to identify trends and patterns.
• Split data into training, validation, and testing datasets.
• Build and train supervised and unsupervised machine learning models.
• Evaluate model performance using appropriate metrics.
• Optimize models using hyperparameter tuning and cross-validation.
• Implement MLOps workflows using MLow for experiment tracking and model management.
• Track model metrics, parameters, versions, and training workflows.
• Interpret model outputs and generate actionable business insights.
• Deploy machine learning applications using Streamlit.
• Create end-to-end ML pipelines for scalable real-world applications.