Data Science Fundamentals and Supervised Machine Learning

Description

Overview

This training program provides a foundational understanding of data science principles and supervised machine learning techniques. Participants will learn the essential concepts, methods, and applications through a combination of theoretical learning and hands-on exercises.

Objectives:

• Understand Data Science Foundations: Grasp the fundamental concepts and workflow of data science.

• Master Supervised Machine Learning Techniques: Gain proficiency in implementing and evaluating supervised learning algorithms.

• Apply Skills in Real-World Scenarios: Translate theoretical knowledge into practical applications through case studies and hands-on labs.

• Prepare for Advanced Learning: Establish a solid foundation for further exploration in data science and machine learning

  Content

  Module 1: Introduction to Data Science

  • What is Data Science? (Real-world applications)

  • Python/R for Data Science (Pandas, NumPy, Scikit-learn)

  • Data Collection & Cleaning (handling missing values, outliers)

  Module 2: Exploratory Data Analysis (EDA) & Visualization

  • Descriptive Statistics (mean, median, skewness)

  • Correlation & Feature Importance

  • Visualization tools (Matplotlib, Seaborn, Plotly)

  Module 3: Supervised Machine Learning Fundamentals

  • Regression: Linear, Polynomial, Ridge/Lasso

  • Classification: Logistic Regression, Decision Trees, SVM

  • Model Evaluation:

    • Metrics (MSE, R², Accuracy, Precision, Recall, ROC-AUC)

    • Bias-Variance Tradeoff

  Module 4: Feature Engineering & Model Optimization

  • Encoding Categorical Variables (One-Hot, Label Encoding)

  • Hyperparameter Tuning (GridSearchCV, Random Search)

  • Cross-Validation (k-Fold, Stratified)

  Module 5: Capstone Project

  • End-to-end ML project (e.g., Predicting House Prices, Customer Churn, Spam Detection)

  • Model Deployment (Flask, Streamlit, Heroku)

Outcomes:

• Participants will be equipped with the knowledge and skills to:

  • Collect, preprocess, and analyze data using Python and relevant libraries.

  • Apply supervised learning algorithms to solve predictive and classification problems.

  • Evaluate model performance and interpret results effectively.

  • Communicate insights and findings from data science projects to stakeholders.

Takeaways:

• Hands-On Experience: Practical exposure through interactive labs and exercises.

• Comprehensive Materials: Course materials and resources for ongoing reference.

• Networking Opportunities: Engage with peers and instructors to exchange ideas and insights.

• Career Advancement: Enhanced capabilities for roles involving data analysis and machine learning.

Duration: 2 Days