Data science with AI

Overview

This course provides a comprehensive introduction to data science and artificial intelligence through hands-on learning. It begins with foundational skills in SQL and Python before moving into advanced topics such as data visualization, big data technologies, and machine learning. Learners will gain experience working with real-world datasets and tools like NumPy, Pandas, Scikit-Learn, and AWS. The curriculum emphasizes practical application and prepares students for building intelligent systems.

Objectives

• Understand core database concepts and perform CRUD operations using SQL and MySQL.
• Apply Python fundamentals and libraries like NumPy and Pandas for data manipulation and analysis.
• Build and evaluate machine learning models using supervised and unsupervised learning techniques.

Key topics

• SQL Database Management
• Python for Data Analysis
• Machine Learning with Scikit-Learn
• Data Visualization with Matplotlib and Seaborn

Expected outcomes

• Analyze datasets using SQL queries and Python libraries such as Pandas and NumPy.
• Create visualizations to communicate insights effectively using Matplotlib and Seaborn.
• Implement machine learning workflows including model training, evaluation, and deployment on cloud platforms like AWS.

How the AI Tutor can help

How the Skillama AI Tutor helps with the listed lectures: explanations, follow-up questions, and running or debugging practice code.

Who this is for

This course is designed for learners seeking to build practical skills in data science and AI using real-world tools and techniques.

Curriculum

1. 1. SQL

   • Database Basics
   • DBMS
   • MySQL
   • Create Tables
   • CRUD Operations
   • SQL Data Types
   • Primary Keys
   • WHERE Conditions
   • Aggregate Functions

2. 2. Python

   • Python Introduction
   • Environment Setup
   • Variables & Data Types
   • Python Operators
   • Conditional Statements
   • Loops
   • Functions
   • Lists & Tuples
   • Dictionaries & Sets

3. 3. NumPy & Pandas

   • NumPy Arrays
   • Array Functions
   • Indexing & Selection
   • Array Operations
   • Pandas Series
   • Pandas DataFrames
   • Missing Data
   • GroupBy
   • Merging Data

4. 4. Data Visualization

   • Matplotlib Basics
   • Plot Creation
   • Plot Customization
   • Seaborn
   • Distribution Plots
   • Bar Charts
   • Scatter Plots
   • Heatmaps
   • Regression Plots

5. 5. Big Data

   • Big Data Basics
   • Distributed Computing
   • Hadoop Architecture
   • Hadoop Cluster
   • MapReduce
   • Hadoop Commands
   • Spark Basics
   • Spark SQL
   • Spark MLlib

6. 6. Statistics & Machine Learning

   • Descriptive Statistics
   • Mean, Median & Mode
   • Probability
   • Machine Learning Basics
   • Supervised Learning
   • Unsupervised Learning
   • Reinforcement Learning
   • Scikit-Learn
   • ML Workflow

7. 7. Regression & Decision Tree

   • Linear Regression
   • EDA
   • Feature Selection
   • Polynomial Regression
   • Multiple Regression
   • Ridge & Lasso
   • Decision Trees
   • Gini & Entropy
   • Model Evaluation

8. 8. Classification

   • Logistic Regression
   • KNN
   • SVM
   • Random Forest
   • Bagging
   • AdaBoost
   • Gradient Boosting
   • XGBoost
   • Confusion Matrix

9. 9. Data Mining

   • Data Mining Basics
   • Mining Techniques
   • Data Mining Architecture
   • Outlier Detection
   • Data Cleaning
   • Data Processing

10. 10. AWS Cloud

    • Cloud Computing
    • AWS Basics
    • AWS S3
    • EC2
    • Deep Learning on EC2
    • Amazon SageMaker
    • Model Training
    • Model Deployment

11. 11. AI & Deep Learning

    • AI Introduction
    • Neural Networks
    • Activation Functions
    • Loss Functions
    • Gradient Descent
    • Hyperparameters
    • CNN
    • RNN
    • LSTM

12. 12. R Programming & Git

    • R & RStudio Basics
    • Data Manipulation
    • Matrices
    • R Functions
    • Data Visualization
    • Git Basics
    • Git Repository
    • Git Commands
    • Version Control