Data Science
Learning Objectives
- Understand fundamental concepts of data science and its applications.
- Explain the importance of data in decision-making processes.
- Apply mathematical and statistical methods to analyze data.
- Utilize Excel and Power BI for data visualization and analysis.
- Implement Python programming for data manipulation and machine learning.
- Analyze datasets to derive meaningful insights and conclusions.
- Evaluate the performance of machine learning models.
- Create and present data-driven reports and visualizations.
- Develop and enhance problem-solving and critical-thinking skills.
Course Duration
Total
Duration
12 Weeks (3 Months)
3 Weeks Placement Readiness
Weekly
Commitment
10-12 Hours
Pre-Requisites
Basic Computer Literacy
Familiarity with operating systems, file management, and basic software installation.
Mathematics Basics
Understanding of high school-level mathematics.
Basic Programming Knowledge (Preferred)
Familiarity with any programming language (preferable but not mandatory).
Course Outline
Introduction to Data Science Â
Day 1: Overview of Data Science (Live Class, 90 mins)
- Video: Introduction to Data Science
- Reading Material: Overview of Data Science
- Quiz: Basics of Data Science
Day 2: Data Collection Methods (Live Class, 90 mins)
- Reading Material: Data Sources and Collection
- Practical Assignment: Collecting Data from APIs
Day 3: Data Cleaning and Preprocessing (Live Class, 90 mins)
- Video: Data Cleaning Techniques
- Practical Assignment: Cleaning a Dataset in Excel
Day 4: Introduction to Python for Data Science (Live Class, 90 mins)
- Reading Material: Python Basics
- Practical Assignment: Writing Basic Python Scripts
Day 5: Data Types and Data Structures in Python (Live Class, 90 mins)
- Video: Python Data Structures
- Quiz: Python Basics
Excel for Data AnalysisÂ
Day 1: Excel Basics for Data Analysis (Live Class, 90 mins)
- Reading Material: Excel Fundamentals
- Practical Assignment: Basic Excel Functions
Day 2: Data Cleaning in Excel (Live Class, 90 mins)
- Video: Excel Data Cleaning Techniques
- Practical Assignment: Cleaning a Dataset in Excel
Day 3: Data Analysis with Excel (Live Class, 90 mins)
- Reading Material: Excel Data Analysis Tools
- Practical Assignment: Analyzing a Dataset in Excel
Day 4: Advanced Excel Functions (Live Class, 90 mins)
- Video: Advanced Excel Techniques
- Quiz: Excel Functions
Day 5: Excel Dashboarding (Live Class, 90 mins)
- Reading Material: Creating Dashboards in Excel
- Practical Assignment: Building an Excel Dashboard
Data Visualization with power BIÂ
Day 1: Introduction to Power BI (Live Class, 90 mins)
- Video: Getting Started with Power BI
- Reading Material: Power BI Basics
Day 2: Connecting and Transforming Data in Power BI (Live Class, 90 mins)
- Practical Assignment: Data Connections in Power BI
Day 3: Creating Visualizations in Power BI (Live Class, 90 mins)
- Video: Visualization Techniques in Power BI
- Practical Assignment: Building Power BI Reports
Day 4: Advanced Power BI Features (Live Class, 90 mins)
- Reading Material: Advanced Power BI
- Quiz: Power BI Features
Day 5: Power BI Dashboards (Live Class, 90 mins)
- Practical Assignment: Creating Interactive Dashboards in Power BI
Data Analysis with PythonÂ
Day 1: Introduction to Pandas (Live Class, 90 mins)
- Video: Getting Started with Pandas
- Reading Material: Pandas Basics
Day 2: Data Cleaning with Pandas (Live Class, 90 mins)
- Practical Assignment: Cleaning Data with Pandas
Day 3: Data Manipulation with Pandas (Live Class, 90 mins)
- Reading Material: Advanced Pandas
- Practical Assignment: Data Transformation with Pandas
Day 4: Data Analysis with Pandas (Live Class, 90 mins)
- Video: Analyzing Data with Pandas
- Practical Assignment: Performing EDA with Pandas
Day 5: Data Visualization with Matplotlib (Live Class, 90 mins)
- Practical Assignment: Creating Plots with Matplotlib
Statistics for Data ScienceÂ
Day 1: Introduction to Statistics (Live Class, 90 mins)
- Reading Material: Basic Statistical Concepts
- Quiz: Basic Statistics
Day 2: Descriptive Statistics (Live Class, 90 mins)
- Video: Descriptive Statistics Techniques
- Practical Assignment: Descriptive Analysis in Python
Day 3: Inferential Statistics (Live Class, 90 mins)
- Reading Material: Inferential Statistics Concepts
- Practical Assignment: Hypothesis Testing in Python
Day 4: Probability Theory (Live Class, 90 mins)
- Video: Introduction to Probability
- Quiz: Probability Concepts
Day 5: Statistics with Python (Live Class, 90 mins)
- Practical Assignment: Statistical Analysis with Python
Exploratory Data Analysis (EDA)Â
Day 1: EDA Overview (Live Class, 90 mins)
- Video: EDA Techniques
- Reading Material: Importance of EDA
Day 2: Data Visualization for EDA (Live Class, 90 mins)
- Practical Assignment: Visualizing Data for EDA
Day 3: Summary Statistics and Data Distributions (Live Class, 90 mins)
- Reading Material: Data Distributions
- Practical Assignment: Calculating Summary Statistics
Day 4: Identifying Patterns and Outliers (Live Class, 90 mins)
- Video: Detecting Outliers
- Practical Assignment: Analyzing Patterns in Data
Day 5: EDA Project (Live Class, 90 mins)
- Practical Assignment: EDA on a Real Dataset
Introduction to Machine LearningÂ
Day 1: Machine Learning Basics (Live Class, 90 mins)
- Reading Material: Introduction to Machine Learning
- Quiz: Machine Learning Concepts
Day 2: Supervised Learning – Regression (Live Class, 90 mins)
- Practical Assignment: Implementing Linear Regression
Day 3: Supervised Learning – Classification (Live Class, 90 mins)
- Video: Classification Techniques
- Practical Assignment: Implementing Logistic Regression
Day 4: Unsupervised Learning – Clustering (Live Class, 90 mins)
- Practical Assignment: Implementing K-Means Clustering
Day 5: Model Evaluation and Validation (Live Class, 90 mins)
- Reading Material: Model Evaluation Techniques
- Quiz: Model Evaluation
Advanced Machine LearningÂ
Day 1: Decision Trees and Random Forests (Live Class, 90 mins)
- Video: Tree-Based Methods
- Practical Assignment: Implementing Decision Trees
Day 2: Ensemble Methods (Live Class, 90 mins)
- Reading Material: Ensemble Techniques
- Practical Assignment: Implementing Random Forests
Day 3: Support Vector Machines (Live Class, 90 mins)
- Practical Assignment: Implementing SVM
Day 4: Neural Networks Introduction (Live Class, 90 mins)
- Video: Basics of Neural Networks
- Quiz: Neural Networks Concepts
Day 5: Model Tuning and Optimization (Live Class, 90 mins)
- Practical Assignment: Hyperparameter Tuning
Big Data TechnologiesÂ
Day 1: Introduction to Big Data (Live Class, 90 mins)
- Reading Material: Big Data Concepts
- Quiz: Big Data Basics
Day 2: Working with Hadoop (Live Class, 90 mins)
- Video: Hadoop Ecosystem
- Practical Assignment: Introduction to Hadoop
Day 3: Introduction to Spark (Live Class, 90 mins)
- Practical Assignment: Working with Spark
Day 4: SQL for Data Science (Live Class, 90 mins)
- Reading Material: SQL Basics
- Practical Assignment: Writing SQL Queries
Day 5: Integrating Big Data Tools (Live Class, 90 mins)
- Practical Assignment: Big Data Project
Real-World Data Science ApplicationsÂ
Day 1: Case Studies in Data Science (Live Class, 90 mins)
- Video: Successful Data Science Projects
- Reading Material: Data Science Case Studies
Day 2: Industry-Specific Data Science Applications (Live Class, 90 mins)
- Practical Assignment: Analyzing Industry Data
Day 3: Ethics in Data Science (Live Class, 90 mins)
- Reading Material: Data Ethics
- Quiz: Ethical Considerations in Data Science
Day 4: Data Science for Social Good (Live Class, 90 mins)
- Practical Assignment: Social Good Project
Day 5: Capstone Project Planning (Live Class, 90 mins)
- Practical Assignment: Project Proposal
Capstone Project DevelopmentÂ
Day 1: Project Development – Data Collection (Live Class, 90 mins)
- Practical Assignment: Collecting Project Data
Day 2: Project Development – Data Cleaning (Live Class, 90 mins)
- Practical Assignment: Cleaning Project Data
Day 3: Project Development – Data Analysis (Live Class, 90 mins)
- Practical Assignment: Analyzing Project Data
Day 4: Project Development – Visualization (Live Class, 90 mins)
- Practical Assignment: Visualizing Project Data
Day 5: Project Development – Modeling (Live Class, 90 mins)
- Practical Assignment: Building Project Models
Capstone Project and ReviewÂ
Day 1: Finalizing Capstone Project (Live Class, 90 mins)
- Practical Assignment: Finalizing Project Report
Day 2: Preparing for Presentation (Live Class, 90 mins)
- Practical Assignment: Creating Presentation Slides
Day 3: Presentation Skills Workshop (Live Class, 90 mins)
- Video: Effective Presentation Techniques
- Practical Assignment: Practicing Presentation
Day 4: Capstone Project Presentation (Live Class, 90 mins)
- Practical Assignment: Presenting Capstone Project
Day 5: Review and Feedback (Live Class, 90 mins)
- Practical Assignment: Incorporating Feedback
- Reading Material: Reflective Analysis
