Build Your Career In Data Science

1.  Excel Basics

Introduction to Excel Environment

• Navigating through the Excel interface, including ribbons, toolbars, and workbooks.
• Understanding cell references: absolute, relative, and mixed references.

Efficient Worksheet Management

•       Creating, renaming, and formatting worksheets.
•       Grouping, freezing panes, and using shortcuts for navigation.

2.  Core Excel Formulas and Functions

Basic Functions

•       Master functions like SUM, AVERAGE, MIN, MAX, COUNT, and COUNTA for data aggregation and summarization.
•       Using ROUND, ROUNDUP, and ROUNDDOWN for precision control.

Logical Functions

•       Implement decision-making functions such as IF, AND, OR, and NOT.
•       Crafting nested IF statements for complex conditions.

Date and Time Functions

•       Automating date-based calculations with TODAY, NOW, DATE, YEAR, MONTH, and DAY.
•       Analyzing work durations with NETWORKDAYS and WORKDAY functions.

Text Functions

•       Manipulate text data using CONCAT, TEXTJOIN, LEFT, RIGHT, MID, LEN, TRIM, and PROPER.
•       Formatting and extracting meaningful data from unstructured text.

Lookup and Reference Functions

•       Implement advanced search techniques with VLOOKUP, HLOOKUP, XLOOKUP, INDEX, and MATCH.
•       Dynamic range lookups for data modeling and reconciliation.

3.  Data Visualization and Reporting

PivotTables and PivotCharts

•       Learn to create dynamic reports for summarizing large datasets.

Charting

•       Master visualizations like bar charts, line graphs, and scatter plots.

Conditional Formatting

•       Highlight data patterns with heatmaps and data bars.

4.  Advanced Excel Techniques for MIS

What-If Analysis

•       Use Goal Seek, Data Tables, and Scenario Manager to analyze hypothetical business scenarios.

Data Consolidation

•       Aggregate and merge data from multiple workbooks and sheets.

Forecasting and Trend Analysis

•       Automate trend identification with Forecast Sheets and TREND functions.

5.  Automation and Macros

Introduction to Macros

•       Record and execute simple macros for repetitive tasks. 

6.  Project

HR Analytics

•       Using interactive dashboard find out the possible key factors influencing Attrition.

Super Store Daily Reporting

•       Create Dashboard to observe sales and profit on daily , weekly and monthly basis .

1.  Getting Started with Power BI

Overview of Power BI ecosystem

• Understanding Power BI Desktop, Service, and Mobile applications.
• Connecting to diverse data sources: Excel, SQL Server, and APIs.

2.  Data Transformation in Power Query

Cleaning and transforming datasets

• Removing duplicates, splitting columns, and merging tables.
• Handling missing data and reshaping datasets.

Understanding M-code

• Basics of M-code for advanced transformations.

3.  Data Modeling

Establishing relationships

• Understanding one-to-one and one-to-many relationships.
•       Implementing star schema for analytical efficiency.

Optimizing models

• Setting up calculated columns and measures.

4.  Data Visualization in Power BI

Creating impactful visuals

• Using bar, pie, line, and scatter plots for effective communication.
• Designing custom visuals with Power BI Marketplace.

Interactivity features

• Implementing slicers, drill-throughs, and report filters.

5.  Introduction to DAX (Data Analysis Expressions)

Basic DAX measures

• Using SUM, COUNT, and AVERAGE for aggregations.

Logical functions in DAX

• Applying IF, SWITCH, and conditional calculations.

Time intelligence

• Performing YTD, MTD, and QTD analysis.

6.  Publishing and Collaboration

Publishing reports to Power BI Service

• Sharing dashboards and managing permissions.

Collaboration features

• Setting up workspaces and embedding reports.

7.  Advanced Features

Row-Level Security (RLS)

• Implementing RLS for data access control.

Power BI and Python/R integration

• Enhancing analytics with Python scripts.

Module 1: Introduction to Tableau

–  Overview of Tableau

–  What is Tableau?

–  Importance of Tableau for Data Analysts

–  Tableau Product Suite: Tableau Desktop, Tableau Public, Tableau Online, Tableau Server

–  Getting Started with Tableau

–  Installing Tableau Desktop or Tableau Public

–  Understanding Tableau Interface

–  Connecting to Data Sources (Excel, CSV, SQL, etc.)

Module 2: Data Preparation and Connections

–  Connecting to Data

–  Connecting to different file types (Excel, CSV, SQL databases, etc.)

–  Live vs. Extract Connections

–  Data Preparation

–  Data Cleaning and Transformation in Tableau Prep

–  Joins, Blends, and Relationships

–  Pivoting and Splitting Columns

–  Understanding Metadata

–  Data Types and Roles

–  Managing Hierarchies

–  Creating Calculated Fields and Sets

Module 3: Building Basic Visualizations

–  Introduction to Charts

–  Bar Charts

–  Line Charts

–  Pie Charts

–  Scatter Plots

–  Tables and Text Visuals

–  Cross-tabs

–  Highlight Tables

–  Heat Maps

–  Using Filters and Sorting

–  Dimension and Measure Filters

–  Interactive Filters

–  Sorting and Grouping

Module 4: Advanced Visualizations

–  Advanced Chart Types

–  Dual-Axis Charts

–  Tree Maps

–  Bubble Charts

–  Waterfall Charts

–  Gantt Charts

–  Geographical Analysis

–  Maps and Spatial Analysis

–  Using Map Layers

–  Dashboards and Stories

–  Building Interactive Dashboards

–  Adding Filters and Actions

–  Creating Stories for Presentations

Module 5: Advanced Analytics

–  Calculated Fields and Table Calculations

–  String, Date, and Logical Functions

–  Aggregate and Level of Detail (LOD) Expressions

–  Forecasting and Trend Analysis

–  Adding Trend Lines

–  Forecasting

–  Creating and Using Parameters

–  Dynamic Filtering and Highlighting

Module 6: Collaboration and Sharing

–  Publishing and Sharing

–  Publishing to Tableau Server or Tableau Online

–  Sharing Workbooks and Dashboards

Module 8: Case Studies and Projects

–  Industry-Specific Projects

–  Sales and Marketing Dashboards

Introduction to SQL

–  Introduction to SQL

–  What is SQL?

–  SQL vs NoSQL

–  SQL Syntax and Standards

–  Introduction to MySQL

–  What is MySQL?

–  Installing MySQL and MySQL Workbench

–  MySQL Workbench Overview

–  Database Basics

–  Database vs Table

–  Keys: Primary Key, Foreign Key, Unique Key

Module 2: Data Definition Language (DDL)

–  Creating Databases and Tables

–  CREATE DATABASE Syntax

–  CREATE TABLE Syntax with Examples

–  Data Types in MySQL (Numeric, String, Date/Time)

–  Altering Databases and Tables

–  ALTER TABLE: Adding, Modifying, and Dropping Columns

–  Renaming Tables

–  Dropping Databases and Tables

–  DROP DATABASE

–  DROP TABLE

–  Constraints

–  Primary Key, Foreign Key

–  NOT NULL, UNIQUE, CHECK, DEFAULT

Module 3: Data Manipulation Language (DML)

–  Inserting Data

–  INSERT INTO Syntax

–  Bulk Inserts

–  Retrieving Data

–  SELECT Statement

–  Filtering Data with WHERE

–  Sorting with ORDER BY

–  Pagination with LIMIT

–  Updating Data

–  UPDATE Syntax

–  Updating with Conditions

–  Deleting Data

–  DELETE Syntax

–  Truncate Table (TRUNCATE)

Module 4: Data Control Language (DCL)

–  User Management

–  Creating Users: CREATE USER

–  Modifying Users: ALTER USER

–  Deleting Users: DROP USER

–  Granting and Revoking Permissions

–  GRANT: Assigning Privileges

–  REVOKE: Removing Privileges

–  Viewing Privileges: SHOW GRANTS

–  Role Management

–  Creating and Assigning Roles

–  Managing Role Privileges

Module 5: Advanced Queries

–  Joins

–  Inner Join

–  Left (Outer) Join

–  Right (Outer) Join

–  Full (Outer) Join (using UNION)

–  Cross Join

–  Self Join

–  Subqueries

–  Single-Row Subqueries

–  Multi-Row Subqueries

–  Correlated Subqueries

–  Aggregate Functions

–  SUM, COUNT, AVG, MIN, MAX

–  Grouping with GROUP BY

–  Filtering Groups with HAVING

–  Set Operations

–  UNION, UNION ALL

–  INTERSECT (Emulated in MySQL)

–  EXCEPT (Emulated in MySQL)

Module 6: Views

–  Introduction to Views

–  What are Views?

–  Advantages and Limitations

–  Creating Views

–  CREATE VIEW Syntax

–  Views with Joins

–  Modifying and Dropping Views

–  ALTER VIEW

–  DROP VIEW

Module 7: Stored Procedures

–  Introduction to Stored Procedures

–  Benefits of Stored Procedures

–  Use Cases

–  Creating Stored Procedures

–  CREATE PROCEDURE Syntax

–  IN, OUT, and INOUT Parameters

–  Executing Stored Procedures

–  Calling Procedures with CALL

–  Passing Parameters

–  Modifying and Dropping Procedures

–  ALTER PROCEDURE

–  DROP PROCEDURE

Module 8: Functions

–  Introduction to Functions

–  Differences Between Functions and Procedures

–  Use Cases

–  Creating Functions

–  CREATE FUNCTION Syntax

–  Deterministic vs Non-Deterministic Functions

–  Using Functions

–  Calling Functions in Queries

–  Example: String, Date, and Numeric Functions

–  Modifying and Dropping Functions

–  ALTER FUNCTION

–  DROP FUNCTION

Module 9: Triggers

–  Introduction to Triggers

–  What are Triggers?

–  Use Cases

–  Creating Triggers

–  CREATE TRIGGER Syntax

–  BEFORE and AFTER Triggers

–  Triggers for INSERT, UPDATE, DELETE

–  Managing Triggers

–  Viewing Triggers: SHOW TRIGGERS

–  Dropping Triggers: DROP TRIGGER

Module 10: Performance Optimization

–  Indexing

–  What are Indexes?

–  Types of Indexes: Primary, Unique, Full-Text

–  Creating and Dropping Indexes

–  Query Optimization

–  Using EXPLAIN to Analyze Queries

–  Common Query Optimization Techniques

–  Database Normalization

–  1NF, 2NF, 3NF, and Beyond

–  Denormalization for Performance

–  Partitioning

–  Horizontal and Vertical Partitioning

–  Managing Partitions in MySQL

–  Healthcare Analytics

–  Supply Chain Dashboard

1.  Introduction to Python

Overview of Python

•       Importance of Python in data and business analytics.
•       Installation and setup of Python and Jupyter Notebook.

Python Basics

•       Syntax, indentation, and writing your first Python script.
•       Data types: integers, floats, strings, booleans.
•       Variables and type casting.

2.  Data Structures in Python

Lists

•       Creation, indexing, slicing, and common list operations.
•       List comprehensions for concise operations.

Tuples

•       Immutable sequences and use cases in analytics.

Dictionaries

•       Key-value pairs for mapping and lookup operations.
•       Dictionary comprehensions and nested dictionaries.

Sets

•       Unique elements, set operations (union, intersection, difference).

3.  Conditional Statements and Loops

Conditional Statements

•       if, elif, else: Handling decision-making scenarios.
•       Nested conditions for complex logic.

Loops

•       for and while loops for repetitive tasks.
•       Using break, continue, and pass statements.

Practical Examples

•       Iterating through datasets and extracting information.

4.  Exception Handling

Understanding Errors

•       SyntaxError, TypeError, ValueError, and their causes.

Try-Except Blocks

•       Handling runtime errors gracefully.
•       Using finally for cleanup operations.

Raising Exceptions

•       Custom error messages for debugging.

5.  Functions and Modules

Functions

•       Defining and calling functions with def.
•       Using parameters, arguments, and return statements.

Lambda Functions

•       Creating anonymous functions for one-time use.
•       Use cases in data filtering and mapping.

Modules and Packages

•       Importing built-in modules like math and random.
•       Creating and using custom modules.

6.  Object-Oriented Programming (OOP)

Classes and Objects

•       Defining classes and creating objects.
•       Attributes and methods.

OOP Principles

•       Inheritance, polymorphism, encapsulation, and abstraction.
•       Practical examples in analytics projects.

7.  Data Manipulation with Numpy

Introduction to Numpy

•       Arrays vs. lists: Benefits and performance.
•       Creating arrays: zeros, ones, arange, and linspace.

Array Operations

•       Element-wise operations, indexing, and slicing.
•       Aggregations: sum, mean, std, and min/max.

Broadcasting and Reshaping

•       Reshaping arrays for analytical tasks.
•       Stacking and splitting arrays.

8.  Data Analysis with Pandas

Introduction to Pandas

•       Series and DataFrames: Creation and manipulation.

Data Cleaning

•       Handling missing data with fillna, dropna.
•       Detecting and removing duplicates.

Data Operations

•       Filtering, sorting, and grouping data.
•       Merging, joining, and concatenating DataFrames.

Exploratory Data Analysis (EDA)

•       Descriptive statistics with Pandas: describe(), info().
•       Data visualization integration with Pandas.

9.  Data Visualization

Matplotlib

•       Creating basic plots: line, scatter, bar, and histogram.
•       Customizing plots: titles, labels, legends, and styles.
•       Subplots for comparative analysis.

Seaborn

•       Advanced visualizations: heatmaps, pairplots, and boxplots.
•       Customizing aesthetics for professional reporting.
•       Statistical plots: regression plots, violin plots.

Plotly

•       Interactive visualizations: line and scatter plots.
•       Building dashboards with Plotly Dash.
•       Enhancing business presentations with interactivity.

10.  Exploratory Data Analysis (EDA)

Understanding the Dataset

•       Overview of columns, data types, and null values.
•       Identifying outliers and inconsistencies.

Univariate Analysis

•       Analyzing individual features with histograms and boxplots.

Bivariate and Multivariate Analysis

•       Correlation analysis and heatmaps.
•       Pairwise relationships using pairplots.

Feature Engineering

•       Creating new features from existing ones.
•       Encoding categorical variables for modeling.

11.  Statistical Analysis

Descriptive Statistics

•       Measures of central tendency: mean, median, mode.
•       Measures of dispersion: variance, standard deviation, range.

Probability Basics

•       Introduction to distributions: normal, binomial, and uniform.
•       Understanding probability density functions (PDFs).

Hypothesis Testing

•       t-tests, chi-square tests, and ANOVA.
•       p-values and statistical significance.

12.  Real-World Projects

Project 1: Sales Data Analysis

•       Analyzing regional sales performance.
•       Visualizing trends and seasonality.