Introduction to Data Science Course

Course Objectives

By the end of this course, participants will be able to:

• Understand the key concepts and principles of data science.
• Perform data wrangling, cleaning, and transformation using Python and R.
• Use statistical analysis techniques to derive insights from datasets.
• Implement machine learning models for predictive analysis.
• Create data visualizations to effectively communicate data-driven insights.
• Understand the ethical considerations and challenges in data science.
• Work with large datasets using libraries like Pandas, NumPy, and Scikit-learn.
• Apply basic machine learning algorithms to solve real-world problems.
• Develop an end-to-end data science project from data acquisition to model deployment.
• Gain practical experience with tools such as Jupyter notebooks, RStudio, and Tablea

Course Outline:

Module 1: Introduction to Data Science

• What is Data Science?
• Overview of the Data Science Workflow
• Importance and Applications of Data Science in Various Industries
• Overview of Tools and Technologies (Python, R, Jupyter Notebooks)

Module 2: Data Wrangling and Cleaning

• Introduction to Data Types and Formats
• Data Cleaning Techniques
• Handling Missing Data
• Data Transformation and Feature Engineering
• Practical Session: Cleaning a Dataset in Python/R

Module 3: Exploratory Data Analysis (EDA)

• Importance of EDA
• Descriptive Statistics
• Data Visualization for EDA (Matplotlib, Seaborn, ggplot2)
• Identifying Patterns and Trends in Data
• Hands-on Exercise: Performing EDA on a Real Dataset

Module 4: Introduction to Python/R for Data Science

• Python vs R: When to Use Which
• Key Libraries in Python (Pandas, NumPy, Matplotlib, Seaborn)
• Key Libraries in R (dplyr, ggplot2, tidyr)
• Hands-on: Basic Data Manipulation in Python/R

Module 5: Statistical Analysis and Hypothesis Testing

• Introduction to Statistics for Data Science
• Measures of Central Tendency and Dispersion
• Probability Distributions
• Hypothesis Testing
• Case Study: Applying Statistical Tests on a Dataset

Module 6: Introduction to Machine Learning

• Overview of Machine Learning (ML)
• Types of ML: Supervised, Unsupervised, and Reinforcement Learning
• Key Algorithms (Linear Regression, Decision Trees, k-NN)
• Model Evaluation and Selection (Accuracy, Precision, Recall, F1 Score)
• Practical Session: Building Your First ML Model

Module 7: Data Visualization and Reporting

• Importance of Data Visualization
• Visualization Tools: Matplotlib, Seaborn, Plotly, Tableau
• Best Practices in Data Presentation
• Hands-on Project: Creating Interactive Dashboards and Reports

Module 8: Advanced Machine Learning Algorithms

• Introduction to Clustering (K-means, Hierarchical)
• Decision Trees, Random Forests, and Gradient Boosting
• Introduction to Deep Learning Concepts
• Case Study: Implementing an Advanced ML Model on a Complex Dataset

Module 9: Working with Big Data and Cloud Platforms

• Introduction to Big Data Concepts (Hadoop, Spark)
• Working with Large Datasets Using Python/R
• Introduction to Cloud Platforms for Data Science (AWS, Google Cloud)
• Practical Exercise: Analyzing Large Datasets Using Cloud Services

Module 10: Data Science Ethics, Case Study & Capstone Project

• Ethical Considerations in Data Science
• Data Privacy and Security Issues
• Case Study: End-to-End Data Science Project