Applied Data Science Concepts with Python

Course Overview

Python for Data Science

• Introduction to Python for Data Science
• Overview of Data Science Tools
• Setting Up the Environment
• Jupyter Notebooks
• Practical Work with Pandas Series and DataFrames

Data Science Fundamentals

• What is Data Science?
• Data Science Workflow
• Introduction to Machine Learning
• Ethical Considerations in Data Science
• Data Manipulation with Pandas
• Data Types and Structures
• Feature Engineering

Data Wrangling & Visualization

• Exploratory Data Analysis (EDA) with Pandas
• Data Cleaning
• Summarizing Data
• Visual Exploratory Data Analysis (EDA) with Pandas
• Combining Data Wrangling with Visual EDA
• Multivariate Plots
• Univariate Plots

Applied Data Science with Pandas & Scikit-Learn

• Introduction to Data Science
• Numpy Refresher
• Steps in the Basic ML Pipeline
• Further Refinement of ML Predictions
• Model Selection
• Evaluation Metrics

Basics of Predictive AI

• Defining Machine Learning, AI, Predictive AI, and Generative AI
• Overview of Neural Networks
• The Machine Learning Workflow
• Data Collection, Exploration, and Preprocessing
• Model Training, Evaluation, and Selection
• Model Deployment and Monitoring
• Applications of Predictive AI
• Challenges in Machine Learning

Predictive AI with Scikit-Learn

• Getting Started with Scikit-Learn for Predictive AI
• Important Algorithms in Scikit-Learn
• Data Preparation for Scikit-Learn
• Evaluating Models in Scikit-Learn
• Deploying Predictive Models with Scikit-Learn
• Using Cross-Validation
• Tuning Hyperparameters