5 Sections
161 Lessons
10 Weeks
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Advance Excel
18
1.1
Introduction to Excel for Analytics Overview
30 Minutes
1.2
Importance of Excel in Data Analytics
10 Minutes
1.3
Excel interface and basic navigation
10 Minutes
1.4
Essential Excel Functions for Data Analytics
1.5
Understanding and using basic functions (SUM, AVERAGE, COUNT)
1.6
Working with mathematical and statistical functions
1.7
Text functions for Data Cleaning and manipulation
1.8
Logical Functions (IF, AND, OR)
1.9
Data Import and Cleaning in Excel
1.10
Importing Data from Various sources (CVS, Excel, Databases)
1.11
Data Cleaning techniques and best practices Handling missing data
1.12
Data Visualization in Excel Creating basic charts (bar charts, line charts, Pie charts)
1.13
Introduction to PivotTables
1.14
PivotTables and PivotCharts
1.15
Creating PivotTables for Data Summarization
1.16
Building PivotCharts for visual analysis
1.17
Data Analysis with Excel tables
1.18
Introduction to Excel Tables
SQL
35
2.1
Introduction to Databases and SQL
2.2
Understanding Databases
45 Minutes
2.3
Definition and types of databases
45 Minutes
2.4
Relational Databases and Non-relational Databases
2.5
Introduction to SQL and What is SQL ?
2.6
SQL History and Evaluation
2.7
Importance of SQL in the industry
2.8
Basic SQL Commands
2.9
Filtering data using WHERE clause
2.10
Retrieving data from a single table
2.11
Advanced Querying
2.12
Ata Modification
2.13
COMMIT and ROLLBACK Statement
2.14
Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)
2.15
Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
2.16
Stored Procedures and Functions
2.17
Creation Stored Procedures
2.18
Input and Output parameters
2.19
User-Defined Functions
2.20
Database Security
2.21
User Roles and Permissions
2.22
GRANT and REVOKE Statements
2.23
Securing Sensitive Data
2.24
Introduction to NoSQL Databases
2.25
Overview of NoSQL Databases
2.26
Best Practices and Optimization
2.27
Writing Efficient Queries
2.28
Query optimization techniques
2.29
Case Studies and Real-World Applications
2.30
Analyzing real-world Senarios
2.31
Building practical solutions with SQL
2.32
Views and Indexes
2.33
Creating and managing views
2.34
Indexes and their impact on performance
2.35
Data Filtering and Aggregation
Python
50
3.1
Introduction to Programming
10 Minutes
3.2
Basics of Programming Logic
10 Minutes
3.3
Understanding algorithms and flowcharts
3.4
Overview of Python as a programming language
3.5
Setting Up Python Environment
3.6
Installing Python
3.7
Working with Python IDEs
3.8
(Integrated Development Environments)
3.9
Writing and executing the first Python script
3.10
Python Basics
3.11
Variables and Data Types
3.12
Basic operations (arithmetic, comparison, logical)
3.13
Input and output (print, input)
3.14
Control Flow
3.15
Conditional statements (if, elseif, else)
3.16
Loops (for, while)
3.17
Break and continue statements
3.18
Functions in Python
3.19
Defining functions
3.20
Parameters and return values
3.21
Scope and lifetime of variables
3.22
Lists and Tuples
3.23
Creating and manipulating lists
3.24
Slicing and indexing
3.25
Working with tuples
3.26
Dictionaries and Sets
3.27
Understanding dictionaries
3.28
Operations on sets
3.29
Use cases for dictionaries and sets
3.30
Reading and Writing Files
3.31
Opening and closing files
3.32
Reading from and writing to files
3.33
Working with different file formats (text, CSV)
3.34
Error Handling
3.35
Introduction to exceptions
3.36
Try, except, finally blocks
3.37
Handling different types of errors
3.38
Python Libraries for Data Analytics
3.39
NumPy for numerical operations
3.40
Pandas for data manipulation and analysis
3.41
Matplotlib and Seaborn for data visualization
3.42
Data Cleaning and Preprocessing
3.43
Handling missing data
3.44
Data imputation techniques
3.45
Data normalization and standardization
3.46
Exploratory Data Analysis (EDA)
3.47
Descriptive Statistics
3.48
Measures of central tendency and dispersion
3.49
Skewness and kurtosis
3.50
Correlation and covariance
Statistics
38
4.1
Overview of Statistics Definition and scope of statistics
10 Minutes
4.2
Descriptive vs. inferential statistics
15 Minutes
4.3
Data Types and Measurement Scales
4.4
Categorical vs. numerical data
4.5
Nominal, ordinal, interval, and ratio scales
4.6
Descriptive Statistics
4.7
Measures of central tendency (mean, median, mode)
4.8
Measures of variability (range, variance, standard deviation)
4.9
Introduction to Probability
4.10
Basic probability concepts
4.11
Probability rules and laws
4.12
Probability Distributions
4.13
Discrete and continuous distributions
4.14
Normal distribution and its properties
4.15
Sampling Distributions
4.16
Central Limit Theorem
4.17
Standard error and confidence intervals
4.18
Hypothesis Testing
4.19
Formulating hypotheses
4.20
Type I and Type II errors
4.21
Parametric Tests
4.22
t-tests for means
4.23
Analysis of variance (ANOVA)
4.24
Non-parametric Tests
4.25
Mann-Whitney U test
4.26
Kruskal-Wallis test
4.27
Correlation Analysis
4.28
Pearson correlation coefficient
4.29
Spearman rank correlation
4.30
Regression Analysis
4.31
Simple linear regression
4.32
Multiple linear regression
4.33
Bayesian Concepts
4.34
Bayes’ Theorem
4.35
Prior, likelihood, and posterior probabilities
4.36
Bayesian Inference
4.37
Bayesian hypothesis testing
4.38
Bayesian modeling
Data Visualization
20
5.1
Overview of Tableau
5.2
Understanding the Tableau interface
5.3
Connecting to data sources
5.4
Data source considerations and best practices
5.5
Importing and cleaning data
5.6
Managing metadata
5.7
Joins and relationships
5.8
Data blending
5.9
Creating basic charts (bar charts, line charts, scatter plots)
5.10
Building maps and geographic visualizations
5.11
Using size and color in visualizations
5.12
Dual-axis charts and combo charts
5.13
Working with calculated fields
5.14
Building hierarchies
5.15
Creating sets and groups
5.16
Trend lines and reference lines
5.17
Advanced chart types (tree maps, heat maps, box plots)
5.18
Dashboard design principles
5.19
Storytelling with data
5.20
Interactive dashboards and actions
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