AI Powered Data Analytics Training Program

NA

• Introduction To Python
• Installation and Working with Python
• Understanding Python variables
• Python basic Operators
• Understanding the Python blocks.

• Python Keyword and Identiers
• Python Comments, Multiline Comments.
• Python Indentation
• Understating the concepts of Operators

• Introduction To Variables
• Variables, expression condition and function
• Global and Local Variables in Python
• Packing and Unpacking Arguments
• Type Casting in Python
• Byte objects vs. string in Python
• Variable Scope

• Python Data Type
• Declaring and using Numeric data types
• Using string data type and string operations
• Understanding Non-numeric data types
• Understanding the concept of Casting and Boolean.
• Strings
• List
• Tuples
• Dictionary
• Sets

• Control Structure & Flow
• Statements if, else, elif
• How to use nested IF and Else in Python
• Loops
• Loops and Control Statements.
• Jumping Statements Break, Continue, pass
• Looping techniques in Python
• How to use Range function in Loop
• Programs for printing Patterns in Python
• How to use if and else with Loop
• Use of Switch Function in Loop
• Elegant way of Python Iteration
• Generator in Python
• How to use nested Loop in Python
• Use If and Else in for and While Loop
• Examples of Looping with Break and Continue Statement
• How to use IN or NOT IN keyword in Python Loop.

• Python Function, Modules and Packages
• Python Syntax
• Function Call
• Return Statement
• Arguments in a function Required, Default, Positional, Variable-length
• Write an Empty Function in Python pass statement.
• Lamda/ Anonymous Function
• *args and **kwargs
• Help function in Python
• Scope and Life Time of Variable in Python Function
• Nested Loop in Python Function
• Recursive Function and Its Advantage and Disadvantage
• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Random functions in python
• Programming using functions, modules & external packages
• Map, Filter and Reduce function with Lambda Function
• More example of Python Function

• List
• What is List.
• List Creation
• List Length
• List Append
• List Insert
• List Remove
• List Append & Extend using + and Keyword
• List Delete
• List related Keyword in Python
• List Revers
• List Sorting
• List having Multiple Reference
• String Split to create a List
• List Indexing
• List Slicing
• List count and Looping
• List Comprehension and Nested Comprehension

• Tuple
• What is Tuple
• Tuple Creation
• Accessing Elements in Tuple
• Changing a Tuple
• Tuple Deletion
• Tuple Count
• Tuple Index
• Tuple Membership
• TupleBuilt in Function (Length, Sort)

• Dictionary
• Dict Creation
• Dict Access (Accessing Dict Values)
• Dict Get Method
• Dict Add or Modify Elements
• Dict Copy
• Dict From Keys.
• Dict Items
• Dict Keys (Updating, Removing and Iterating)
• Dict Values
• Dict Comprehension
• Default Dictionaries
• Ordered Dictionaries
• Looping Dictionaries
• Dict useful methods (Pop, Pop Item, Str , Update etc.)

• Sets
• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations
• Frozen Sets

• Strings
• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations

• Python Exception Handling
• Python Errors and Built-in-Exceptions
• Exception handing Try, Except and Finally
• Catching Exceptions in Python
• Catching Specic Exception in Python
• Raising Exception
• Try and Finally

• Python File Handling
• Opening a File
• Python File Modes
• Closing File
• Writing to a File
• Reading from a File
• Renaming and Deleting Files in Python
• Python Directory and File Management
• List Directories and Files
• Making New Directory
• Changing Directory

• Python Database Interaction
• SQL Database connection using
• Creating and searching tables
• Reading and Storing cong information on database
• Programming using database connections

• Reading an excel
• Working With Excel
• Reading an excel le using Python
• Writing to an excel sheet using Python
• Python| Reading an excel le
• Python | Writing an excel le
• Adjusting Rows and Column using Python
• ArithmeticOperation in Excel le.
• Play with Workbook, Sheets and Cells in Excel using Python
• Creating and Removing Sheets
• Formatting the Excel File Data
• More example of Python Function

• Complete Understanding of OS Module of Python
• Check Dirs. (exist or not)
• How to split path and extension
• How to get user prole detail
• Get the path of Desktop, Documents, Downloads etc.
• Handle the File System Organization using OS
• How to get any les and folders details using OS

• AI and LLM Integration in Python:
• PandasAI: Natural language queries on DataFrames
• OpenAI API (GPT) to generate code, EDA, and reports
• LangChain for building chat-based data apps
• Autogen/Code Interpreter tools for automating insights

NA

• Introduction to Statistics
• Categorical Data
• Numerical Data
• Mean
• Median
• Mode
• Outliers
• Range
• Interquartile range
• Correlation
• Standard Deviation
• Variance
• Box plot

• Understanding Statistics
• Descriptive Statistics
• Sample vs Population Statistics
• Random variables
• Probability distribution functions
• Expected value
• Normal distribution
• Gaussian distribution
• Z-score
• Spread and Dispersion
• Correlation and Co-variance

• Data Pre-Processing & Data Mining
• Data Preparation
• Feature Engineering
• Feature Scaling
• Datasets
• Dimensionality Reduction
• Anomaly Detection
• Parameter Estimation
• Data and Knowledge
• Selected Applications in Data Mining

• EDA (Exploratory Data Analysis)
• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Imbalanced Data Techniques

NA

• Data Analysis and Visualization using Pandas.
• Statistics
• Pandas

• Data Analysis and Visualization using NumPy and MatPlotLib
• NumPy
• MatPlotLib

• Introduction to Data Visualization with Seaborn
• Introduction to Seaborn
• Visualizing Two Quantitative Variables
• Visualizing a Categorical and a Quantitative Variable
• Customizing Seaborn Plots

NA

• SQL Server Fundamentals
• SQL Server 2019 Installation
• Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
• SQL Server Features & Purpose
• Using Management Studio (SSMS)
• Conguration Tools & SQLCMD
• Conventions & Collation

• SQL Server 2019 Database Design
• SQL Database Architecture
• Database Creation using GUI
• Database Creation using T-SQL scripts
• DB Design using Files and File Groups
• File locations and Size parameters
• Database Structure modications

• SQL Tables in MS SQL Server
• SQL Server Database Tables
• Table creation using T-SQL Scripts
• Naming Conventions for Columns
• Single Row and Multi-Row Inserts
• Table Aliases
• Column Aliases & Usage
• Table creation using Schemas
• Basic INSERT
• UPDATE
• DELETE
• SELECT queries and Schemas
• Use of WHERE, IN and BETWEEN
• Variants of SELECT statement
• ORDER BY
• GROUPING
• HAVING
• ROWCOUNT and CUBE Functions

• Data Validation and Constraints
• Table creation using Constraints
• NULL and IDENTITY properties
• UNIQUE KEY Constraint and NOT NULL
• PRIMARY KEY Constraint & Usage
• CHECK and DEFAULT Constraints
• Naming Composite Primary Keys
• Disabling Constraints & Other Options

• Views and Row Data Security
• Benets of Views in SQL Database
• Views on Tables and Views
• SCHEMA BINDING and ENCRYPTION
• Issues with Views and ALTER TABLE
• Common System Views and Metadata
• Common Dynamic Management views
• Working with JOINS inside views

• Indexes and Query tuning
• Need for Indexes & Usage
• Indexing Table & View Columns
• Index SCAN and SEEK
• INCLUDED Indexes & Usage
• Materializing Views (storage level)
• Composite Indexed Columns & Keys
• Indexes and Table Constraints
• Primary Keys & Non-Clustered Indexes

• Stored Procedures and Benets
• Why to use Stored Procedures
• Types of Stored Procedures
• Use of Variables and parameters
• SCHEMABINDING and ENCRYPTION
• INPUT and OUTPUT parameters
• System level Stored Procedures
• Dynamic SQL and parameterization

• System functions and Usage
• Scalar Valued Functions
• Types of Table Valued Functions
• SCHEMABINDING and ENCRYPTION
• System Functions and usage
• Date Functions
• Time Functions
• String and Operational Functions
• ROW_COUNT
• GROUPING Functions

• Triggers, cursors, memory limitations
• Why to use Triggers
• DML Triggers and Performance impact
• INSERTED and DELETED memory tables
• Data Audit operations & Sampling
• Database Triggers and Server Triggers
• Bulk Operations with Triggers

• Cursors and Memory Limitations
• Cursor declaration and Life cycle
• STATIC
• DYNAMIC
• SCROLL Cursors
• FORWARD_ONLY and LOCAL Cursors
• KEYSET Cursors with Complex SPs

• Transactions Management
• ACID Properties and Scope
• EXPLICIT Transaction types
• IMPLICIT Transactions and options
• AUTOCOMMIT Transaction and usage

• AI Integration in SQL:
• AI Tools:
• Copilot in Azure Data Studio or GitHub for SQL suggestions