What all topics we will cover in Data Analytics Course in Palin Analytics:

Module 1: Introduction to Excel for Analytics

Overview of Excel as an analytics tool

Importance of Excel in data analysis

Excel interface and basic navigation

Module 2: Essential Excel Functions for Data Analysis

Understanding and using basic functions (SUM, AVERAGE, COUNT)

Working with mathematical and statistical functions

Text functions for data cleaning and manipulation

Logical functions (IF, AND, OR)

Module 3: Data Import and Cleaning in Excel

Importing data from various sources (CSV, Excel, databases)

Data cleaning techniques and best practices

Handling missing data

Module 4: Data Visualization in Excel

Creating basic charts (bar charts, line charts, pie charts)

Formatting and customizing charts

Using sparklines for trend analysis

Module 5: PivotTables and PivotCharts

Introduction to PivotTables

Creating PivotTables for data summarization

Building PivotCharts for visual analysis

Module 6: Advanced Excel Functions for Analytics

VLOOKUP and HLOOKUP for data retrieval

INDEX and MATCH functions

Advanced IF statements and nested functions

Using array formulas

Module 7: Data Analysis with Excel Tables

Introduction to Excel Tables

Sorting and filtering data in tables

Using structured references

Module 1: Introduction to Databases and SQL

Understanding Databases

Definition and types of databases

Relational databases

Non-relational databases

Introduction to SQL

What is SQL?

SQL history and evolution

Importance of SQL in the industry

Module 2: Basic SQL Commands

SELECT statement

Retrieving data from a single table

Filtering data using WHERE clause

Sorting data using ORDER BY clause

INSERT, UPDATE, DELETE statements

Adding, modifying, and deleting data

Data integrity considerations

Module 3: Data Types and Operators

Common SQL data types

Working with text, numeric, and date data

Arithmetic and comparison operators

Module 4: Advanced Querying

JOIN operations

INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

Cross join and self-join

Subqueries

Nested queries

Correlated subqueries

Set operations

UNION, INTERSECT, EXCEPT

Module 5: Data Filtering and Aggregation

GROUP BY clause

HAVING clause

Aggregate functions (SUM, AVG, COUNT, MIN, MAX)

Module 6: Data Modification

Transactions

COMMIT and ROLLBACK statements

Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)

Module 7: Views and Indexes

Creating and managing views

Indexes and their impact on performance

Module 8: Stored Procedures and Functions

Creating stored procedures

Input and output parameters

User-defined functions

Module 9: Database Security

User roles and permissions

GRANT and REVOKE statements

Securing sensitive data

Module 10: Best Practices and Optimization

Writing efficient queries

Indexing strategies

Query optimization techniques

Module 11: Introduction to NoSQL Databases

Overview of NoSQL databases

Contrasting SQL and NoSQL

Module 12: Case Studies and Real-world Applications

Analyzing real-world scenarios

Building practical solutions with SQL

Introduction to Programming

Basics of programming logic

Understanding algorithms and flowcharts

Overview of Python as a programming language

Setting Up Python Environment

Installing Python

Working with Python IDEs 

(Integrated Development Environments)

Writing and executing the first Python script

Python Basics

Variables and data types

Basic operations (arithmetic, comparison, logical)

Input and output (print, input)

Control Flow

Conditional statements (if, elif, else)

Loops (for, while)

Break and continue statements

Functions in Python

Defining functions

Parameters and return values

Scope and lifetime of variables

Lists and Tuples

Creating and manipulating lists

Slicing and indexing

Working with tuples

Dictionaries and Sets

Understanding dictionaries

Operations on sets

Use cases for dictionaries and sets

Module 4: File Handling

Reading and Writing Files

Opening and closing files

Reading from and writing to files

Working with different file formats (text, CSV)

Module 5: Error Handling and Modules

Error Handling

Introduction to exceptions

Try, except, finally blocks

Handling different types of errors

Python Libraries for Data Analytics

NumPy for numerical operations

Pandas for data manipulation and analysis

Matplotlib and Seaborn for data visualization

Data Cleaning and Preprocessing

Handling missing data

Data imputation techniques

Data normalization and standardization

Exploratory Data Analysis (EDA)

Descriptive Statistics

Measures of central tendency and dispersion

Skewness and kurtosis

Correlation and covariance

Module 1: Foundations of Statistics

Overview of StatisticsDefinition and scope of statistics

Descriptive vs. inferential statistics

Data Types and Measurement Scales

Categorical vs. numerical data

Nominal, ordinal, interval, and ratio scales

Descriptive Statistics

Measures of central tendency (mean, median, mode)

Measures of variability (range, variance, standard deviation)

Module 2: Probability Theory

Introduction to Probability

Basic probability concepts

Probability rules and laws

Probability Distributions

Discrete and continuous distributions

Normal distribution and its properties

Sampling Distributions

Central Limit Theorem

Standard error and confidence intervals

Module 3: Inferential Statistics

Hypothesis Testing

Formulating hypotheses

Type I and Type II errors

Parametric Tests

t-tests for means

Analysis of variance (ANOVA)

Non-parametric Tests

Mann-Whitney U test

Kruskal-Wallis test

Module 4: Correlation and Regression

Correlation Analysis

Pearson correlation coefficient

Spearman rank correlation

Regression Analysis

Simple linear regression

Multiple linear regression

Module 5: Bayesian Statistics

Bayesian Concepts

Bayes’ Theorem

Prior, likelihood, and posterior probabilities

Bayesian Inference

Bayesian hypothesis testing

Bayesian modeling

Module 1: Introduction to Tableau

Overview of Tableau

Understanding the Tableau interface

Connecting to data sources

Data source considerations and best practices

Module 2: Data Preparation and Cleaning in Tableau

Importing and cleaning data

Managing metadata

Joins and relationships

Data blending

Module 3: Basic Visualization Techniques

Creating basic charts (bar charts, line charts, scatter plots)

Building maps and geographic visualizations

Using size and color in visualizations

Dual-axis charts and combo charts

Module 4: Intermediate Visualization Techniques

Working with calculated fields

Building hierarchies

Creating sets and groups

Trend lines and reference lines

Module 5: Advanced Visualization Techniques

Advanced chart types (treemaps, heat maps, box plots)

Dashboard design principles

Storytelling with data

Interactive dashboards and actions