Data Wrangling and Analysis with Python
Course Overview
This course covers essential data wrangling techniques to clean, transform, and analyze real-world datasets using Python. Participants will work with libraries such as Pandas and NumPy to handle missing data, reshape datasets, and perform exploratory analysis. Practical exercises will focus on healthcare data, enabling students to extract meaningful insights and prepare data for advanced analytics. By the end, learners will be proficient in manipulating and analyzing structured data using Python.
Key Skills
- Supervised Learning Fundamentals (Classification & Regression)
- Python for Machine Learning (Pandas, NumPy, Scikit-learn)
- Key ML Algorithms (Linear Regression, Decision Trees, SVM, k-NN)
- Model Evaluation & Metrics (Accuracy, Precision, Recall, F1-Score)
- Data Preprocessing & Feature Engineering
- Hyperparameter Tuning & Model Optimization
Course Outline
Data Manipulation and Analysis with Python
Handling and cleaning datasets using Pandas and NumPy
Lessons Objective
- Understanding Supervised Learning (Classification & Regression)
- Model Training & Evaluation
- Overfitting & Underfitting
Data exploration techniques (e.g., filtering, aggregating, and reshaping data)
Lessons Objective
- Working with Scikit-learn
- Data Handling with Pandas & NumPy
- Data Visualization using Matplotlib & Seaborn
Working with various data formats (CSV, Excel, JSON, SQL)
Lessons Objective
- Linear Regression
- Decision Trees
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
- Logistic Regression
Efficient data manipulation techniques to prepare data for analysis
Lessons Objective
- Performance Metrics (Accuracy, Precision, Recall, F1-Score)
- Train-Test Split & Cross-Validation
- Hyperparameter Tuning
- Feature Selection & Engineering
Projects in this course
In this project, you will apply supervised machine learning techniques to predict customer churn for a telecom company. Using a real-world dataset, you will:
- Preprocess the data (handling missing values, encoding categorical features)
- Train and evaluate models like Logistic Regression, Decision Trees, and k-NN
- Compare model performance using metrics like accuracy, precision, recall, and F1-score
- Optimize models through hyperparameter tuning
- Visualize insights with Matplotlib & Seaborn
By completing this project, you will gain hands-on experience in classification problems, model evaluation, and real-world data handling.
Course Duration:
10 Hours
Earned Skills:
Python, Problem Solving, Supervised Learning Algorithms
Earn Certification:
Earned a valuable certificate to boost your resume
