Data Science with Artificial Intelligence (AI)

Introduction

The Data Science with AI Course is designed to give learners an in-depth understanding of data science concepts, techniques, and tools, combined with artificial intelligence methodologies. This course covers essential data science and AI skills, including data processing, machine learning, deep learning, and AI applications, to enable students to create data-driven, AI-powered solutions for complex business problems. With a balance of theory and practical projects, this course prepares learners to excel in the data science and AI

Course Objectives

Understand foundational and advanced data science and AI concepts.
Master data collection, pre-processing, and exploration techniques.
Develop skills in machine learning, deep learning, and AI model development.
Gain hands-on experience with popular tools and libraries like Python, TensorFlow, PyTorch, and SQL.
Build end-to-end projects to reinforce practical skills.
Explore real-world AI applications and learn to implement AI-driven solutions.

Syllabus Outline

Module 1: Introduction to Data Science and AI

Overview of Data Science and AI
Differences and Synergies between Data Science and AI
Applications of Data Science and AI in various industries
Introduction to Key Tools and Libraries (Python, Jupyter Notebooks, Git)

Module 2: Data Collection, Wrangling, and Pre-processing

Data Collection Techniques and Sources
Data Wrangling and Cleaning
Handling Missing Data, Outliers, and Duplicates
Data Transformation, Scaling, and Normalization
Introduction to Exploratory Data Analysis (EDA)

Module 3: Statistics and Probability for Data Science

Descriptive Statistics
Probability Theory and Distributions
Hypothesis Testing and A/B Testing
Confidence Intervals and P-Values
Correlation, Causation, and Feature Selection Techniques

Module 4: Data Visualization and Communication

Introduction to Data Visualization Tools (Matplotlib, Seaborn)
- Learn the basics of Matplotlib and Seaborn to create simple and effective visualizations.
- Matplotlib: Create line plots, bar charts, histograms, etc.
- Seaborn: Create statistical plots (e.g., box plots, pair plots) with better styling.
Creating Visualizations to Communicate Insights
- Understand how to choose the right type of chart (e.g., bar charts, scatter plots, line graphs) to effectively display your data.
- Learn how to design clear visuals that highlight important patterns and trends in the data.

Module 5: Introduction to Machine Learning

Overview of Machine Learning and its Types
Supervised Learning (Regression and Classification)
Unsupervised Learning (Clustering and Dimensionality Reduction)
Reinforcement Learning Basics
Model Evaluation Metrics and Techniques

Module 6: Machine Learning with Python

Working with Scikit-Learn, Pandas, and NumPy
Data Pre-processing and Feature Engineering
Building and Evaluating Machine Learning Models
Hyperparameter Tuning and Model Optimization
Introduction to Model Pipelines and Automation

Module 7: Deep Learning Fundamentals

Basics of Neural Networks
Understanding Deep Learning Concepts (Layers, Activation Functions, Backpropagation)
Introduction to Popular Frameworks: TensorFlow and PyTorch
Building, Training, and Tuning Neural Networks
Evaluation and Optimization of Neural Network Models

Module 8: Advanced Deep Learning and Neural Networks

Convolutional Neural Networks (CNNs) for Image Data
Recurrent Neural Networks (RNNs) for Sequential Data
Long Short-Term Memory (LSTM) and GRU networks
Transfer Learning and Pre-trained Models

Module 9: Natural Language Processing (NLP)

Basics of NLP and Text Pre-processing
Bag of Words, TF-IDF, and Word Embeddings (Word2Vec)
NLP Models and Applications (Text Classification, Sentiment Analysis, Chatbots)
Introduction to Transformers and BERT
Using NLP libraries like NLTK, SpaCy, and Hugging Face

Conclusion

This course provides a solid foundation in data science and AI with a practical approach to learning. By the end of the course, learners will have the skills to build and deploy AI-driven solutions, develop machine learning and deep learning models, and use advanced tools and techniques to solve real-world data challenges.

Course Duration

16-24 Weeks (Flexible options available for full-time or part-time study)