NASSCOM Futureskills Prime Certified Advanced Data Science with Gen AI including Free Internship & 100% Placement Assistance

Introduction to Python Programming

• What is Python programming language?
• Why is 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 Statements

• 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 a 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
• Built-in Methods
• Basic Examples Using Lists to Print Elements at Even Index, etc.

Tuple & Set

• What is a Tuple?
• How to Create a Tuple?
• Accessing Tuple Elements Using Indexing and Slicing
• Differences Between Lists and Tuples
• What is a Set?
• How to Create a Set?
• Built-in Methods
• Set Operations
• Manipulating and Accessing Sets

Dictionary

• What is a Dictionary?
• How to Create a Dictionary?
• Adding, Modifying and Retrieving Values from a Dictionary
• Getting All Methods
• Built-in Methods
• Differences Between Sets and Dictionaries
• Mutable vs Immutable Data Types

Strings

• What is a String?
• Create a String
• Accessing a String Using Indexing and Slicing
• Built-in Methods
• format() Method
• User Input / Dynamic Entry
• Type Casting of Strings

For Loop, For-Else & Comprehension

• Introduction to for Loop, range(), enumerate()
• For-Else
• List Comprehension & Dictionary Comprehension
• Problem Solving with String Methods Using for Loops & Conditional Statements
• Using chr() and ord()
• Problem Solving with Lists and Dictionaries
• Comprehension

Functions

• What is a 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 Packages
• Using Conditional Statements
• Using Loops
• Using Functions

Modules & Packages

• Modules and Packages
• Datetime
• Random
• math, os
• Creating Custom Modules

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?
• Types of Variables, Types of Methods and 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 & Exception Handling

• What is a File?
• How to Create, Open and Close a File?
• Writing to a File
• Appending to a File
• Reading from a File
• Testing File Existence
• What is an Exception?
• Raising Exceptions
• try and 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-Server Architecture
• Introduction to FastAPI
• Request Methods
• Hands-on FastAPI
• Application Building
• Integration of FastAPI with Streamlit
• Case Study

GenAI: Enhanced Coding Fundamentals

• Introduction to AI-Assisted Coding
• ChatGPT for Code Generation and Explanation
• AI-Assisted Debugging and Code Optimization
• Generating Python Solutions with ChatGPT
• Hands-on AI-Assisted Coding for Data Science Tasks

Introduction to EDA

• How is EDA different from Python programming?
• EDA vs Python with Case Study
• Types of Data (Numerical, Categorical)
• Types of Analysis (Non-Visual 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 (Univariate Analysis)
• Measures of Correlation (Bivariate Analysis)

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, 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

• Array Manipulations – 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 and Swapping Columns
• Adding New Columns / Dropping Existing Columns
• Vectorized Arithmetic Operations
• Data Type Conversions
• Math and Statistical Functions on Columns

GroupBy and Pivot on DataFrame

• 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
• Case Study – Descriptive Statistics

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, Boxplot, Violinplot
• Univariate Plots for Categorical Data – Barplot, Countplot

Bivariate and Multivariate Analysis with Matplotlib / Seaborn

• Plots Between Numerical Variables – Scatterplot, Heatmap, Lineplot
• Plots Between Categorical Variables – KDE Plot, Boxplot, Histogram, Boxenplot, Violinplot
• Plots Between a Numeric and a Categorical Variable – Countplot, Stacked Barplot
• Plots for Multivariate Analysis – Pairplot, Heatmap

Interactive Data Visualization with Plotly

• Interactive Plots for Numeric Data – Histogram, Boxplot, Violinplot, Density Plot
• Interactive Plots for Categorical Data – Bar Chart, Grouped Bar Chart, Pie Chart, Funnel Chart
• Interactive Plots for Bivariate Analysis – Scatterplot, Line Chart, Bubble Chart, Area Chart
• Interactive Plots for Multivariate Analysis – Heatmap, Scatter Matrix, Parallel Coordinates
• Advanced Plotly Visualizations – Subplots, Animations, Hover Interactions, Filters

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
• Bernoulli, Binomial, and Poisson

Continuous Data Distribution

• Uniform, Normal
• Exponential
• LogNormal
• 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, Systematic 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 Multiple Samples

Confidence Interval Estimate

• Confidence Interval Estimates Using 1 Sample (Population Std 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 – Chi-Square Test, ANOVA

Simplifying Statistical Analysis with AI Tools

• Introduction to AI-Assisted Statistical Analysis – Role of GenAI in Statistics
• AI-Assisted Descriptive Statistics – Mean, Median, Mode, Variance and Standard Deviation
• AI-Assisted Statistical Analysis – Correlation, Distribution and Hypothesis Testing
• Interpreting Statistical Results with GenAI – Insights and Explanations
• Hands-on with AI Tools – Statistical Analysis and Interpretation

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

GenAI: Powered Data Wrangling with PandasAI

• Introduction to Data Wrangling – Data Cleaning, Transformation, Integration
• Introduction to PandasAI – AI-Assisted Data Manipulation
• AI-Powered Data Cleaning – Missing Values, Duplicates, Data Types
• AI-Assisted Data Transformation – Filtering, Sorting, Aggregation
• Natural Language Data Analysis – Querying and Exploring Data with PandasAI

Introduction to SQL

• Data
• What is Database?
• DBMS
• RDBMS
• SQL vs MySQL
• SQL vs NoSQL
• CRUD Operations
• Pandas vs SQL
• Client-Server Architecture
• Workbench Introduction

SQL Fundamentals

• Types of SQL Commands
• Data Types
• Constraints (PRIMARY KEY / AUTO_INCREMENT, NOT NULL, UNIQUE, DEFAULT, CHECK)
• Creating Tables with Constraints
• DDL (CREATE, ALTER, DROP, TRUNCATE)
• DML (INSERT, UPDATE, DELETE)

Multiple Tables

• Multiple Tables – Primary Key, Composite Key, Foreign Key
• Types of Relationships in SQL
• ER Diagram
• Case Study: On Relational Database

Data Exploration and Data Filtering (DQL and Operators)

• SELECT (Retrieve)
• Data Exploration – Selecting Columns; Performing LIMIT, DISTINCT, Aggregation Values, Indexing and Slicing Using OFFSET
• Data Filtering – Filtering Data Based on Conditions Using Operators (LIKE, REGEXP, BETWEEN)
• GROUP BY (Aggregate Functions)
• HAVING (Correlating with get_group in Pandas)
• ORDER BY
• CASE
• Order of Execution
• Case Study: A Case Study of Clauses

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 Database Objects

• Views
• Stored Procedures
• Functions

Advanced Topics

• Transaction Control Language – ACID Properties, COMMIT, ROLLBACK, SAVEPOINT
• Triggers
• Type Conversion, Working with Date, Time & String Functions
• Connectivity of MySQL with Python (Pandas)

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

GenAI Powered SQL: Query Generation & Optimization

• Introduction to AI-Powered SQL – AI-Assisted SQL Concepts and Workflows
• AI-Assisted Query Generation – Natural Language to SQL
• AI-Assisted Query Optimization – Query Performance and Optimization
• SQL Debugging with GenAI – Error Identification and Correction
• Hands-on with AI-Powered SQL – Query Generation and Optimization

Excel Essentials for Data Analysis

• Introduction to Excel for Data Analysis
• Data Exploration Using Excel Functions
• Data Cleaning and Formatting in Excel
• Sorting, Filtering and Data Validation
• Conditional Formatting and Data Highlighting
• Lookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCH
• Pivot Tables and Pivot Charts for Data Analysis
• Advanced Excel Functions for Data Analysis
• Case Study: Dashboarding in Excel

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 Visualization

Power Query

• Power Query Introduction
• Data Transformation – Its Benefits
• Introducing Ribbons
• Queries Panel
• M Language Briefing
• Power BI Data Types
• Changing Data Types of Columns
• Filtering
• Inbuilt Column Transformations
• Inbuilt Row Transformations
• Combine Queries
• Merge Queries

Power Pivot and Introduction to DAX

• Power Pivot
• Introduction 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 Functions
• DAX Filter Functions
• DAX Time Intelligence Functions
• 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
• Drill Down
• 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

GenAI: Enhanced Presentations with ChatGPT

• Introduction to AI-Enhanced Presentations – Role of GenAI in Data Storytelling
• AI-Assisted Content Creation – Insights, Summaries and Key Takeaways
• AI-Assisted Presentation Design – Structure, Visuals and Slide Content
• Data Storytelling with ChatGPT – Converting Analysis into Narratives
• Hands-on with ChatGPT – Creating Data-Driven Presentations

Machine Learning Foundations

• Why Machine Learning?
• AI vs ML vs Deep Learning
• Supervised vs Unsupervised Learning
• Regression vs Classification
• Machine Learning Workflow / Lifecycle

Problem Definition & Objective

• Business Problem Statement
• Defining ML Objectives
• Identifying Features and Target
• Identifying Regression or Classification Tasks

Feature Engineering

• Feature Transformation
• Feature Selection
• Feature Importance

Train-Test Split

• Training and Testing Data
• Importance of Data Splitting
• Avoiding Data Leakage

Numerical Data Preprocessing

• Numerical Feature Preprocessing
• Feature Scaling
• Types of Feature Scaling
• Standardization, Normalization

Categorical Data Preprocessing

• Categorical Feature Preprocessing
• Encoding Techniques
• One-Hot Encoding
• Ordinal Encoding

Supervised Learning – Classification Algorithms

• Introduction to Classification
• K-Nearest Neighbors (KNN)
• Logistic Regression
• Naive Bayes
• Decision Tree

Supervised Learning – Regression Algorithms

• Introduction to Regression
• Linear Regression
• KNN for Regression
• Decision Tree for Regression

Data Collection

• Data Sources
• Data Collection Methods
• Loading and Understanding Datasets

Exploratory Data Analysis (EDA)

• Univariate, Bivariate and Multivariate Analysis
• Visual and Non-Visual Statistics
• Understanding Data Distributions and Relationships

Data Quality Assessment

• Identifying Duplicates
• Handling Null and Missing Values
• Detecting Outliers
• Data Type and Consistency Checks

Model Building with Scikit-learn

• Model Training and Prediction
• Applying Preprocessing and Models
• Building ML Pipelines
• Hands-on Model Implementation

Regression Evaluation Metrics

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score
• Adjusted R² Score

Classification Evaluation Metrics

• Confusion Matrix
• Accuracy, Precision and Recall
• F1 Score
• Classification Report

Hyperparameter Tuning

• Parameters vs Hyperparameters
• Introduction to Cross-Validation
• GridSearchCV
• RandomizedSearchCV
• Model Selection and Tuning

GenAI: Enhanced Machine Learning Case Study

• Problem Definition & Data Understanding – Problem Statement, Objectives and AI-Assisted Data Exploration
• EDA & Data Preparation – AI-Assisted EDA, Data Cleaning and Feature Engineering
• Model Building & Evaluation – AI-Assisted Model Selection, Training and Evaluation
• Model Optimization & Interpretation – AI-Assisted Hyperparameter Tuning and Result Interpretation
• Business Insights & Deployment – AI-Assisted Insights and Reporting

GenAI Fundamentals for Data Analysts

• Introduction to Generative AI
• Generative AI vs Traditional AI
• Applications of GenAI in Data Analytics
• Overview of GenAI Tools – ChatGPT, Gemini, Gamma

Understanding LLMs

• What are Large Language Models?
• Context, Prompts and Responses
• Data Relevance and Hallucinations
• Limitations of LLMs

Prompt Engineering Essentials for Data Analytics

• Introduction to Prompt Engineering
• Effective Prompt Structure
• Context, Role and Constraints
• Iterative Prompting and Refinement

Prompting for Data Tasks

• Prompts for Data Understanding
• Prompts for Data Cleaning and EDA
• Prompts for Data Interpretation

GenAI for Data Cleaning & EDA

• AI-Assisted Data Understanding
• Identifying Missing Values, Duplicates and Outliers

GenAI for Code & SQL

• AI-Assisted Python Code Generation
• Code Explanation and Debugging
• AI-Assisted SQL Query Generation
• Query Optimization and Validation

GenAI for Analytics Reporting

• Converting Analysis into Insights
• AI-Assisted Report, PPT and PDF Generation Using ChatGPT
• Data Storytelling with GenAI
• Creating Presentations with Gamma

Multimodal GenAI for Data Analytics

• Understanding Text, Images and Tables with GenAI
• Extracting Information from Documents and Images
• Image and Chart Interpretation
• Multimodal Analysis with ChatGPT and Gemini

Automating Data Analytics Workflows

• Introduction to AI-Assisted Automation
• Automating Repetitive Analytics Tasks
• Connecting Data and GenAI Tools
• Workflow Design with GenAI

No-Code AI Automation with n8n

• Introduction to n8n
• Connecting AI Models with Data Sources
• Building AI-Assisted Analytics Workflows
• Automating Reports and Notifications

Responsible GenAI for Data Analytics

• AI Limitations and Hallucinations
• Data Privacy and Security
• Bias and Responsible AI Usage
• Human Validation and Oversight

GenAI: Enhanced Data Analytics Case Study

• Problem Definition and Data Understanding
• AI-Assisted EDA and Data Preparation
• AI-Assisted Analysis, Reporting and Visualization
• Workflow Automation Using GenAI Tools
• Validating AI Outputs and Generating Business Insights

GenAI Tools & Future of Data Analytics

• Comparing GenAI Tools for Data Tasks
• Selecting the Right Tool for the Task
• Building Personal GenAI Workflows
• Staying Current with Emerging GenAI Tools

Ethics, Responsibility & Staying Current with GenAI

• AI Ethics, Bias and Responsible AI Usage
• Data Privacy, Security and Intellectual Property
• Hallucinations, Human Validation and AI Reliability
• Staying Current with Emerging GenAI Tools and Technologies

Synthetic Data & Image Generation

• Introduction to Synthetic Data Generation
• Generating Synthetic Data for Testing and Analysis
• Introduction to AI-Based Image Generation and Use Cases

• Retail Sales Analysis
• Student Performance Analysis
• People Ops Analytics
• HR Analytics – Employee Attrition & Performance Analysis
• Pricing Strategy Analysis
• AI-Augmented Business Intelligence