Course Overview:

Course Title: Data Science Essentials

Course Duration: 12 Weeks (3 Months)

Course Description: This three-month program is designed for individuals aiming to gain essential skills in data science. Participants will explore core concepts, methodologies, and practical applications of data science techniques. Through hands-on projects, real-world scenarios, and detailed tutorials, participants will develop proficiency in collecting, analyzing, and interpreting data to extract valuable insights.

Detailed Course Outline:

Weeks 1-2: Introduction to Data Science and Python Basics

• Understanding Data Science

  • Overview of data science concepts and applications
  • Differentiating data science from related fields

• Introduction to Python for Data Science

  • Basics of Python programming language
  • Key Python libraries for data science (NumPy, Pandas)

Weeks 3-4: Exploratory Data Analysis (EDA) and Data Visualization

• Exploratory Data Analysis (EDA)

  • Techniques for exploring and summarizing data
  • Identifying patterns and outliers

• Data Visualization

  • Creating effective visualizations using libraries like Matplotlib and Seaborn
  • Communicating insights through graphs and charts

Weeks 5-6: Statistical Analysis and Hypothesis Testing

• Statistical Analysis in Data Science

  • Descriptive statistics and inferential statistics
  • Probability distributions and statistical hypothesis testing

• Hypothesis Testing

  • Conducting hypothesis tests for data-driven decision-making
  • Interpreting test results and drawing conclusions

Weeks 7-8: Machine Learning for Data Science

• Supervised Learning Algorithms

  • Regression and classification algorithms
  • Model training, evaluation, and prediction

• Unsupervised Learning Algorithms

  • Clustering and dimensionality reduction techniques
  • Applications of unsupervised learning in data science

Weeks 9-10: Feature Engineering and Model Deployment

• Feature Engineering

  • Creating relevant features for machine learning models
  • Handling missing data and outliers

• Model Deployment Basics

  • Overview of deploying machine learning models
  • Considerations for model deployment and monitoring

Weeks 11-12: Data Science in Practice and Final Project

• Data Science Lifecycle and Best Practices

  • Overview of the end-to-end data science process
  • Best practices for successful data science projects

• Final Project: Data Science Solution Implementation

  • Applying learned concepts to a comprehensive real-world data science project
  • Developing and presenting a complete data science solution

Evaluation and Assessment:

• Weekly practical exercises, mid-term projects, and a final project will be used to assess participants' understanding and application of data science skills.