Course Overview
Advanced Excel
Introduction to MS Excel
Uses and Importance of Excel
Exploring Excel Interface
Basic Excel Operations
Formatting
Format Painter
Number & Symbols
Inserting in Excel
Transforming Data
Working with Formulas
Functions in Excel
Logical Functions
Math Functions
Lookup Functions
Formula Auditing
Data Sorting and Filtering
Slicer & Timeline
Flash Fill
Consolidation
Data Validation
Conditional Formatting
Chart and Graphs
Pivot Table
Pivot Charts
Table and Table Styles
Link
Page Setup & Arrangement in Excel
Macro
Protection of Worsksheet
PowerBI
Introduction to Power BI
What is Power BI?
Why do we use Power BI?
Where do we see dashboards in real life?
Getting Started with Power BI
Installing Power BI Desktop
Understanding the Power BI Interface
How to Import Data (Excel, CSV)
Working with Data
Viewing Data in Table Form
Basic Cleaning (Removing Empty Columns or Rows)
Renaming Columns and Rows
Sorting and Filtering Data
Visualizing Data
What is a Chart?
Types of Charts
Making a Dashboard
Adding Multiple Charts
Changing Colors and Labels
Using Text Boxes, Shapes, and Images
Making the Dashboard Look Beautiful
Storytelling with Data
How to Explain a Chart
Adding Titles and Notes
Explaining Trends and Patterns
Mini Project
Create a Personal Dashboard (e.g. Favorite Food Survey, Daily Routine, or
School Subjects Scoreboard)
Exporting and Sharing
Advance python
Introduction to python
Exploring Anaconda and other IDEs
Identifiers
Comments
What are Keywords?
Datatype and its types
Strings and its Operations
Lists and its Operations
Tuples and its Operations
Sets and its Operations
Dictionaries and its Operations
Operators
Types of Operators
Dealing with Binary Numbers
Decision Control Statement
Nested If
Loop Statement
Nested Loop
Jump Control Statement
Introduction to Functions
Types of Functions
Arguments and its types
Ways to define a Function
Built-In Functions
Recursion
Lambda function
Introduction to Modules
Types of Modules
Creating and Importing of a module
Introduction to Exception Handling
Error vs Exception
Raising an exception
Handling an exception
Components of Exception Handling
Types of Exception
Nested Try
What is a File?
Types of Files
Introduction to File Handling
Steps of File Handling
Working with File Paths
Modes in File Handling
Methods in File Handling
Introduction to OS module
OS module functions and methods
Introduction to OOPS
Class & Object
Methods
Principles of OOPS
Inheritance
Types of Inheritance
Encapsulation
Polymorphism
Abstraction
Getters
Setters
Deleters
Multithreading
Introduction to Numpy
Understanding Arrays
Dimensions in Arrays
Datatypes in Numpy
Functions and Methods used in Numpy
Introduction to Pandas
Series & Dataframes in Pandas
Reading CSV & JSON using pandas
Cleaning Data using Pandas
Introduction to Tkinter
Creating GUI using Tkinter
Introduction to Matplotlib
Plotting & Marking in Matplotlib
Graphs & Charts in Matplotlib
Multiple Python Based Projects
SQL
Understanding Database
Types Of Databases
RDBMS vs NoSQL
Introduction to Relational Databases
What is SQL?
SQL Syntax Basics
Setting up SQL Environment
Creating a Database
Viewing Existing Database
Dropping a Database
Using a Database
Creating Table
Viewing and Modifying Table
What is a Datatype?
Types pf Datatypes in SQL
Inserting and Retrieving Data
Working and Updating Data
Functions in SQL
JOINS in SQL
Grouping and Filtering Data
Subqueries
Set Operations
Constraints in SQL
Procedures in SQL
Statistics
Introduction to Statistics
Importance and Applications of Statistics
types of Statistics
Qualitative vs Quantitative
Data Collection and Sampling
Population and Sample
Sampling Techniques
Data Organization and Representation
Graphical Representation
Mean
Median
Mode
Measures of Dispersion
Variance
Standard Deviation
What is Skewness?
What is Kurtosis?
Correlation and Regression
Probability and its components
Hypothesis Testing
Null and Alternate Hypothesis
Type I and Type II Errors
Z-test
T-test
Chi-Square test
Analysis of Variance (ANOVA)
F-test
Simple Linear Regression
Big Data
What is Big Data?
5Vs of Big Data
Big Data Architecture Overview
Batch vs Stream Processing
Introduction to Hadoop (concept only)
What is HDFS? (still used in enterprise setups)
Introduction to Hive (used for SQL-on-Big Data)
Introduction to HBase (used for NoSQL use cases)
Introduction to Apache Spark
What is DataFrame in Spark?
What is Spark SQL?
What is PySpark?
What is Spark Streaming?
Big Data on Cloud (AWS, Azure, GCP)
Real-World Use Cases of Big Data
Machine Learning
Introduction to Machine Learning
What is Machine Learning?
Difference between AI, ML, DL, and Data Science
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Applications of Machine Learning
Setting up ML Environment
Installing Anaconda and Jupyter Notebook
Introduction to Python Libraries for ML
Working with NumPy and Pandas
Data Visualization using Matplotlib and Seaborn
Data Preprocessing
Understanding Dataset Structure
Handling Missing Data
Encoding Categorical Variables
Introduction to Feature Scaling (Standardization, Normalization)
Basics of Feature Engineering
Exploratory Data Analysis (EDA)
Descriptive Statistics
Visualizing Data Distributions
Introduction to Correlation and Heatmaps
Introduction to Outlier Detection
Finding Patterns and Drawing Insights
Supervised Learning
Introduction to Supervised Learning
Splitting Dataset: Train-Test Split
Regression Algorithms:
Linear Regression
Multiple Linear Regression
Polynomial Regression
Classification Algorithms:
Logistic Regression
K-Nearest Neighbors (KNN)
Introduction to Support Vector Machines (SVM)
Introduction to Decision Trees
Overview of Random Forest
Model Evaluation Techniques
Confusion Matrix
Accuracy, Precision, Recall, F1 Score
Introduction to Error Metrics: MSE, RMSE, MAE
Unsupervised Learning
Introduction to Unsupervised Learning
Clustering Algorithms:
K-Means Clustering
Introduction to Hierarchical Clustering
Feature Selection and Engineering
Understanding Feature Importance
Using Domain Knowledge for Features
Model Deployment Basics
Saving Models using Pickle or Joblib
Creating a Simple Web App with Streamlit
Overview of Model Deployment on Cloud
Real-World Project Workflow
Defining the Problem
Data Collection & Cleaning
EDA & Preprocessing
Model Building & Evaluation
Interpreting Results and Making Conclusions
Report or Dashboard Creation
Cloud
What is Cloud Computing?
Why do we use the cloud?
Examples of cloud in daily life (Google Drive, YouTube, Netflix)
Difference between saving on computer and saving on cloud
Types of Cloud Services (Brief of IaaS, PaaS, SaaS)
Examples of services: Google Docs, Gmail, Minecraft Realms
Introduction to Public and Private Cloud
Benefits of using cloud (access anywhere, no USB needed)
Understanding files, storage, and sharing in the cloud
Logging into a cloud account (Google, OneDrive)
Uploading and downloading files
Collaborating with friends using cloud apps
What is cloud gaming and cloud learning?
How businesses use the cloud (easy example: online shopping)
Basic safety and privacy in the cloud
Fun activity: Create and share a document using the cloud
Course Outcome
- Use tools like Excel and Power BI to organize, analyze, and visualize data.
- Apply Python programming for data handling, visualization, and simple projects.
- Work with SQL to store, query, and manage data effectively.
- Understand basics of statistics to analyze patterns and make data-driven decisions.
- Explore Big Data and Cloud concepts and how they are used in real life.
- Build simple Machine Learning models and deploy small projects.
- Develop problem-solving skills by applying data science concepts to real-world scenarios.
