• Overview of Data Analytics
  • Importance and Applications
  • Data Analytics Process
• Tools and Technologies
  • Setting Up Development Environment
  • Version Control with Git and GitHub

1. Introduction to Python

• Basics: High-level, interpreted language.
• Data Types: Integers, Floats, Strings, Lists, Tuples, Sets, Dictionaries.
• Variables: Assignment and naming conventions.
• Operators: Arithmetic, Comparison, Logical, Assignment.
• Input/Output: input() for input, print() for output.

2. Conditional Statements

• If-Else: if, elif, else for decision-making.

3. Loops

• While: Repeats while a condition is true.
• For: Iterates over sequences (lists, strings).
• Nested Loops: Loops inside loops.

4. Collections

• List: Ordered, mutable.
• Tuple: Ordered, immutable.
• Set: Unordered, unique.
• Dictionary: Key-value pairs.

5. Functions

• Basics: def to define functions, return to return values.
• Lambda: Anonymous functions.
• Built-ins: map(), filter(), reduce(), zip().

6. OOP (Object-Oriented Programming)

• Class & Objects: Class defines objects with attributes/methods.
• Inheritance: Extending classes.
• Constructor: __init__() initializes objects.

7. File Handling

• Read/Write: Open files, read/write data.
• With: Automatic file closing using with.

8. Exception Handling

• Try-Except: Handling errors, finally for cleanup.

9. Advanced Python

• • Libraries: Numpy (arrays), Pandas (dataframes), Matplotlib/Seaborn (visualization), EDA (data analysis).

Introduction to Databases and MySQL

1.1 Data and Databases

• Data Types: Structured, semi-structured, unstructured data.
• Databases: Organized collection of data in tables; benefits include efficient management, integrity, and centralized access.
• RDBMS: Relational databases store data in tables with relationships (using keys); provide integrity, easy querying, and structure.

2. MySQL Commands and Data Types

2.1 DDL & DML Commands

• Database & Table Management:
  • CREATE DATABASE, DROP DATABASE, CREATE TABLE, DROP TABLE, ALTER TABLE (modify structure).
  • SHOW DATABASES, SHOW TABLES (list databases and tables).
• Data Manipulation:
  • INSERT, UPDATE, DELETE to manage data in tables.
  • TRUNCATE TABLE (remove all rows).

2.2 Data Types and Constraints

• Data Types: Numeric (INT, FLOAT), Date/Time (DATE, DATETIME), String (VARCHAR, TEXT).
• Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, DEFAULT, AUTO_INCREMENT, CHECK.

3. Data Retrieval and Advanced Queries

3.1 Querying Data

• SELECT: Retrieve data from tables, with WHERE, ORDER BY, GROUP BY, HAVING, DISTINCT clauses.
• Joins: Combine data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, CROSS JOIN.
• Aggregates: Functions like SUM(), COUNT(), MIN(), AVG().

3.2 Subqueries, Views & Window Functions

• Subqueries: Nested queries within SELECT, INSERT, UPDATE, DELETE.
• Views: Virtual tables for simplified complex queries (CREATE VIEW).
• Window Functions: ROW_NUMBER(), RANK(), DENSE_RANK() for ranking results.

4. Procedures, Triggers, and Data Control

4.1 Procedures and Triggers

• Stored Procedures: Store reusable queries with input parameters (CREATE PROCEDURE).
• Triggers: Automatically execute queries in response to data changes (CREATE TRIGGER).

4.2 Transaction Control & Data Security

• TCL: Manage transactions with COMMIT, ROLLBACK, SAVEPOINT.
• DCL: Manage user access with GRANT and REVOKE.

5. Importing/Exporting Data

• CSV Operations:
  • Import: LOAD DATA INFILE to load CSV data.
  • Export: SELECT INTO OUTFILE to export query results to CSV.

• Descriptive Statistics
  • Measures of Central Tendency (Mean, Median, Mode)
  • Measures of Dispersion (Variance, Standard Deviation)
  • Data Distribution (Histograms, Boxplots)
• Inferential Statistics
  • Probability Distributions (Normal, Binomial)
  • Hypothesis Testing (t-tests, chi-square tests)
  • Confidence Intervals
• Regression Analysis
  • Linear Regression
  • Multiple Regression
  • Logistic Regression

1. Getting Started with Excel

• Overview: Explore Excel’s capabilities.
• Navigation: Key elements of spreadsheets and managing workbooks.

2. Data Entry & Manipulation

• Data Entry: Efficient input of text, numbers, and formulas.
• Editing: Modify content, undo/redo, and use Find & Replace.
• Copy/Paste: Basic and advanced techniques.

3. Formatting for Clarity

• Formatting: Adjust fonts, alignment, and cell styles.
• Conditional Formatting: Highlight data based on rules.
• Customizing: Personalize views and freeze panes.

4. Formulas & Calculations

• Basic Operations: Addition, subtraction, multiplication, division.
• Formula Writing: Create simple formulas, apply BODMAS rules.

5. Advanced Data Manipulation

• Fill & Flash Fill: Auto-fill patterns and data.
• Logical Functions: Use IF statements and nested functions.

6. Working with Functions

• Statistical Functions: SUMIF, COUNTIF, AVERAGEIF.
• Text Functions: TRIM, PROPER, UPPER, LOWER.
• Date/Time Functions: TODAY, NOW, DAY, MONTH, YEAR.
• Lookup Functions: VLOOKUP, INDEX/MATCH.

7. Sorting & Filtering

• Sorting: Organize data alphabetically or numerically.
• Filtering: Focus on specific data subsets.

8. PivotTables

• Introduction: Use PivotTables for data summarization and analysis.

9. Data Visualization

• • Charts: Create and customize basic charts (column, pie, bar).
  • Customizing: Enhance charts with titles, labels, and legends.