5 Sections
171 Lessons
10 Weeks
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Advance Excel
18
1.1
Introduction to Excel for Analytics Overview
20 Minutes
1.2
Importance of Excel in Data Analysis
60 Minutes
1.3
Excel Interface and Basic Navigation
1.4
Essential Excel Functions For Data Analysis
1.5
Understanding and using basic functions (SUM, AVERAGE, COUNT)
1.6
Working with Mathematical and Statistical Function
1.7
Text Functions for Data Cleaning and Manipulation
1.8
Logical Functions (IF, AND, OR)
1.9
Data Import and Cleaning in Excel Importing Data from Various
1.10
Importing Data From Various Sources (CSV, 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 Pivot Tables
1.14
Pivot Tables and Pivot Charts
1.15
Creating Pivot Tables for Data Summarization
1.16
Building Pivot Charts for Visual Analysis
1.17
Data Analysis with Excel Tables
1.18
Introduction to Excel Tables
SQL
34
2.1
Introduction to Databases and SQL
60 Minutes
2.2
Understanding Databases
60 Minutes
2.3
Definition and Types of Databases
2.4
Relational Databases and Non-Relational Databases
2.5
Introduction to SQL and What is SQL?
2.6
SQL history and evolution
2.7
Importance of SQL in the industry
2.8
SQL Basic 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 ROLL BACK Statements
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
Creating 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
Best Practices and Optimization
2.26
Writing Efficient Queries
2.27
Query Optimization Techniques
2.28
Case Studies and Real-World Applications
2.29
Analyzing Real-World Scenario
2.30
Building Practical Solutions with SQL
2.31
Views and Indexes
2.32
Creating and Managing Views
2.33
Indexes and their impact on performance
2.34
Data Filtering and Aggregation
Power BI
37
3.1
Overview
30 Minutes
3.2
Power BI ecosystem and components
20 Minutes
3.3
Getting Started with Power BI Desktop
30 Minutes
3.4
Power BI service overview
25 Minutes
3.5
Connecting to different data sources (Excel, Databases, Cloud Services)
3.6
Loading and transforming data using Power Query Editor
3.7
Data Modeling and Relationship
3.8
Building and Designing Reports
3.9
Creating Visuals (Tables, Charts, Maps, etc.)
3.10
Formatting and customizing visuals
3.11
Using Themes and Templates
3.12
DAX (Data Analysis Expression) Fundamentals
3.13
Creating Calculated columns and measures
3.14
The intelligence Function
3.15
Hierarchies and drill-downs
3.16
Publishing reports to Power BI Services
3.17
Creating and sharing dashboards
3.18
Setting up and managing workspaces
3.19
Creating and using Quick Insights
3.20
Analyzing data trends and patterns
3.21
Building advanced analysis with AI visuals
3.22
Exploring and Interpreting data insights
3.23
Integrating Power BI with Excel
3.24
Embedding Power BI reports in other applications
3.25
Using Power BI REST API for automations
3.26
Power BI and Azure integration
3.27
Power BI Gateway setup and configuration
3.28
Managing data refresh schedules
3.29
Implementing Row-level security
3.30
Monitoring and managing Power BI workspaces
3.31
Performance Optimization techniques
3.32
Designing efficient data models
3.33
Report and Dashboard Optimization
3.34
Best practices for Data visualization
3.35
Overview of Power BI Certification
3.36
Exam Preparation tips and resources
3.37
Continuing learning paths and resources
Python
64
4.1
Overview
4.2
The Python Ecosystem
4.3
Why python over R/SAS
4.4
What do expect after you learn Python
4.5
Understanding and choosing between different Python Versions
4.6
Setting up python on any machine (Windows/Linux/Mac)
4.7
Using Anaconda, the Python distribution
4.8
Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime)
4.9
Setting up a suitable Workspace
4.10
Running the first Python program
4.11
Python Syntax
4.12
Interactive Mode/Script Mode Programming
4.13
Identifiers and Keywords
4.14
Single and Multi-line Comments
4.15
Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary)
4.16
Implicit and Explicit Conversions
4.17
Understanding Operators in Python
4.18
Working with various Date and Time formats
4.19
Working with Numeric data types – int, long, float, complex
4.20
String Handling, Escape Characters, String Operations
4.21
Working with Unicode Strings
4.22
Local and Global Variables
4.23
Flow Control and Decision Making in Python
4.24
Understanding if else conditional statement
4.25
Nested Condition
4.26
Working in Iterations
4.27
Understanding the for and while Loop
4.28
Nested Loops
4.29
Loops Control Statements – breaks, continue, Pass
4.30
Understanding Dictionary – The Key value pairs
4.31
List Comprehensions and Dictionary Comprehensions
4.32
Functions, Arguments, Returns Statements
4.33
Packages, Libraries and Modules
4.34
Error Handling in Python
4.35
Reading data from files (TXT, CSV, Excel, JSON, KML, etc.)
4.36
Writing data to desired file format
4.37
Creating connections to Databases
4.38
Importing/Exporting data from/to NoSQL databases (MongoDB)
4.39
Importing/Exporting data from/to RDBMS (PostgreSQL)
4.40
Getting data from website
4.41
Manipulating Configuration files
4.42
Introduction to Data Wrangling Techniques
4.43
Why is transformation so important
4.44
Understanding Database Architecture – (RDBMS, NoSQL Databases)
4.45
Understanding the strength/limitations of each complex data containers
4.46
Understanding Sorting, Filtering, Redundancy, Cardinality
4.47
Sampling, Aggregations
4.48
Converting from one Data Type to another
4.49
Introduction to Numpy and its superior capabilities
4.50
Understanding differences between Lists and Arrays
4.51
Understanding Vectors and Matrices, Dot Products and Matrix Products
4.52
Universal Array Functions
4.53
Understanding Pandas and its architecture
4.54
Getting to know Series and Data Frames, Columns and Indexes
4.55
Getting Summary Statistics of the Data
4.56
Data Alignment, Ranking and Sorting
4.57
Combining/Splitting Data Frames, Reshaping, Grouping
4.58
Identifying Outliers and performing Binning Tasks
4.59
Cross Tabulation, Permutations, the apply() functions
4.60
Introduction to Data Visualization
4.61
Line Chart, Scatter plots, Box plots, Violin plots
4.62
Understanding Probability Distribution
4.63
Histograms, Heat maps and Clustered Matrices
4.64
Plotting Kernel Density Estimate plots
Deep Leaning
18
5.1
Logistic Regressions
5.2
K-Nearest Neighbours (K-NN)
5.3
Support Vector Machines
5.4
Kernel SVM
5.5
Naive Bayes Classifier
5.6
Decision Tree Classification
5.7
Random Forest Classification
5.8
Clustering – Intuition
5.9
K-Means Clustering
5.10
Hierarchical Clustering
5.11
Principal Component Analysis (PCA)
5.12
Linear Discriminant Analysis (LDA)
5.13
Understanding Kernel PCA
5.14
Understanding the need for model selection
5.15
What is Overlifting
5.16
Understanding Bias Variance Trade-Off
5.17
K-fold cross validation
5.18
Understanding and applying Grid Search
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