Comprehensive Data Science Bootcamp
Master data analysis, visualization, statistics, and machine learning with real-world projects
Course Introduction
This course is designed to take you from zero to job-ready in Data Science. You'll gain hands-on experience working with data, performing statistical analysis, visualizing insights, and building machine learning models using Python and industry-standard tools like Pandas, NumPy, Matplotlib, and Scikit-learn.
Learning Outcomes
- Understand core concepts of data science and analytics
- Clean and manipulate data using Pandas and NumPy
- Create data visualizations with Matplotlib and Seaborn
- Apply statistical techniques to real-world datasets
- Build and evaluate machine learning models
- Work with real datasets and deliver actionable insights
Course Modules
Module 1: Introduction to Data Science
- What is Data Science?
- Roles and responsibilities of a Data Scientist
- Overview of the Data Science workflow
- Setting up your Python environment
Module 2: Python for Data Science
- Python basics and data structures
- Using Jupyter notebooks
- Working with NumPy and Pandas
- Data wrangling techniques
Module 3: Data Visualization
- Exploratory data analysis (EDA)
- Plotting with Matplotlib
- Advanced visualizations using Seaborn
- Creating dashboards with Plotly
Module 4: Statistics & Probability
- Descriptive statistics
- Probability distributions
- Hypothesis testing
- Correlation and regression analysis
Module 5: Working with Real Data
- Importing and exporting datasets
- Cleaning messy data
- Handling missing values
- Feature engineering basics
Module 6: Introduction to Machine Learning
- Types of machine learning
- Supervised vs unsupervised learning
- Model building with Scikit-learn
- Evaluating model performance
Module 7: Capstone Project
- Choose a real dataset
- Define a business problem
- Perform EDA and modeling
- Present insights and results
Module 8: Bonus Topics (Optional)
- Time Series Analysis
- Introduction to Big Data tools (Spark)
- SQL for Data Analysis
- Data Science portfolio building
Prerequisites
- Basic understanding of Python programming
- Familiarity with high-school level math
- Interest in working with data
- No prior experience in data science required
Certification
Upon successful completion of this course, you will receive a certificate of completion, showcasing your skills and readiness for data science roles in industry.