Data Science

Total Hours: 48 hours total (6 hours per week)
Total Duration: 2 Months Totlal Sessions: 16
2 Sessions per week
Session Duration: 3 Hours 4,800

What is the Course?

A comprehensive program designed to transform beginners into data-driven professionals. Master essential tools and techniques for extracting insights from data, building predictive models, and making data-informed business decisions.

Course Objectives

  • Master Python for data analysis and visualization
  • Develop statistical analysis and modeling skills
  • Learn machine learning fundamentals
  • Understand data preprocessing and cleaning
  • Build end-to-end data science projects

What Will You Learn?

  • Data manipulation with Pandas and NumPy
  • Exploratory data analysis techniques
  • Machine learning algorithms (Regression, Classification)
  • Data visualization with Matplotlib/Seaborn
  • SQL for data querying
  • Model evaluation and optimization

What Will the Course Prepare You For?

  • Data Scientist/Analyst roles
  • Business Intelligence positions
  • Machine Learning Engineer careers
  • Analytics-driven decision making
  • Advanced data science certifications

Prerequisites

  • Basic programming understanding
  • High school mathematics
  • Curiosity for data-driven insights

Why Choose This Course?

  • Real-world datasets and case studies
  • Industry-standard tool training
  • Portfolio-building projects
  • Expert-led practical sessions
  • Career support and guidance

Course Curriculum

Total Sessions: 16

Session Duration: 3 Hours

Sessions per Week: 2

Week 1

  • Session 1: Introduction to Data Science
    • Overview of Big Data, Machine Learning, and Data Analytics
    • Tools and Technologies Overview (Excel, Tableau, Python)
  • Session 2: Data Fundamentals & Excel for Analysis
    • Data Collection, Cleaning, and Basic Statistics
    • Excel Basics for Data Analysis (PivotTables, Charts)

Week 2

  • Session 3: Tableau for Data Visualization
    • Connecting to Data Sources
    • Creating Dashboards and Interactive Visualizations
  • Session 4: Introduction to Python for Data Science
    • Setting Up Python Environment
    • Python Syntax and Data Structures (Lists, Dictionaries, etc.)

Week 3

  • Session 5: Python Libraries (Pandas & NumPy)
    • Data Manipulation with Pandas (DataFrames, Merging, Grouping)
    • Array Operations with NumPy
  • Session 6: Data Visualization with Matplotlib & Seaborn
    • Basic Plotting Techniques (Line, Bar, Scatter)
    • Advanced Visualization Features (Styling, Subplots)

Week 4

  • Session 7: Exploratory Data Analysis (EDA)
    • Descriptive Statistics and Data Profiling
    • Handling Missing Data and Outliers
  • Session 8: Introduction to Machine Learning
    • Supervised vs. Unsupervised Learning
    • ML Workflow and Common Algorithms (Regression, Classification)

Week 5

  • Session 9: Regression & Classification Techniques
    • Linear Regression, Logistic Regression
    • Model Training, Validation, and Performance Metrics
  • Session 10: Advanced ML Algorithms
    • Decision Trees, Random Forests, Gradient Boosting
    • Practical Exercises and Use Cases

Week 6

  • Session 11: Web Data Analytics
    • Collecting and Analyzing Web Data (APIs, Web Scraping)
    • Tools/Packages for Web Analytics
  • Session 12: Cloud Computing for Data Science
    • Overview of Cloud Platforms (AWS, Azure, GCP)
    • Deploying Data Pipelines & Notebooks in the Cloud

Week 7

  • Session 13: Data Ethics & Best Practices
    • Data Privacy, Security, and Ethical Considerations
    • Governance & Compliance (GDPR, etc.)
  • Session 14: Project Environment Setup & Version Control
    • Setting Up Collaborative Environments (Git, GitHub)
    • Final Project Planning & Setup

Week 8

  • Session 15: Final Project Work
    • Team/Individual Project Implementation
    • Hands-on Support, Feedback, and Iteration
  • Session 16: Final Project Presentation & Wrap-Up
    • Presenting Data Science Projects
    • Course Recap, Q&A, and Future Directions
Start Now