AI AND DATA SCIENCE

Artificial Intelligence and Data Science Course Outline

Department: Al Khwarizmi Institute of Computer Science (KICS)

Course Instructor: Dr. Muhammad Usman Ghani, Hafiz Umer Draz, Abdullah Javaid

Course Description: This course provides an introduction to the field of data science, Artificial Intelligence covering foundational concepts, techniques, and tools used to analyze and derive insights from data. Students will learn the principles of data collection, cleaning, analysis, visualization, and interpretation, as well as the ethical considerations and best practices in data science.

1. Course Contents:

1. Introduction to Artificial Intelligence and Data Science

  • Definition and scope of data science and Artificial Intelligence
  • Importance of AI/DS applications in different domains
  • Evolution of AI/DS and its interdisciplinary nature
  • Setting up environments (Anaconda, PyCharm, Jupyter notebook, Google Colab)
  • Python Basics:
    1. Printstatement
    2. Variables
    3. Operators
    4. Execution of Hello World Program
  • Conditional statements and control structures
  • Loops:
    1. For loop
    2. while loop
    3. nested loops
  • Modular Programming:
    1. Function creation and calling

2. Advanced Python and Data Handling

  • String Handling
  • Data types in Python:
    1. List
    2. Tuple
    3. Dictionary
    4. Accessing
    5. Adding, and removing data
  • OOP Basic
  • Introduction to NumPy Library:
    1. Import and install
    2. Creating arrays
    3. Data types
    4. Indexing
    5. Slicing
    6. Array attributes
    7. Array attributes
    8. operations on arrays
    9. Sorting arrays
  • Introduction to Pandas Library:
    1. Data structures (series & data frame)
    2. input & output operations

3. Data Science in Python

  • Understand techniques such as lambdas and manipulating csv files
  • Describe common Python functionality and features used for data science
  • Query DataFrame structures for cleaning and processing
  • Explain distributions, sampling, and t-tests

4. Data Visualization and Machine Learning Basics

  • Selection operations, dropping data, sorting, ranking
  • Applying functions, data alignment, data preprocessing using pandas
  • Data Visualization with Matplotlib:
    1. Installation
    2. Preparing data
    3. Creating plots
    4. Plotting routines
    5. Customizing plots
    6. Saving and displaying plots
    7. Types of plots
  • Introduction to Machine Learning
    1. Supervised
    2. Unsupervised
    3. Semi Supervised
    4. Reinforcement Learning
  • Supervised Learning (Part 1):
    1. Types
    2. Regression
    3. Classification
    4. Installing and importing Sci kit learn
    5. Feature selection
    6. Data splitting
  • Advanced Machine Learning
    Supervised Learning (Part 2)
    1. Model training using regression and classification
    2. Predictions on unseen data
    3. Model evaluation metrics
    • Unsupervised Learning:
      1. Clustering
      2. Types of clustering
    • Implementation of K Means and Hierarchical clustering
    1. Introduction to Time Series Analysis:
      1. What is Time Series
      2. Application
        • Seasonality
        • Cyclicity
    • Introduction to Deep Learning I:
      1. Machine Learning Vs Deep Learning
      2. Neuron Vs Perceptron
      3. MLP
      4. Neural network
      5. Types of Neural network
    • Introduction to Deep Learning II:
      1. Feed Forward neural network
      2. Backpropagation
      3. Activation Functions
      4. Loss Function
      5. Optimization
      6. Implementation of ANN using TensorFlow
    • Introduction to Deep Learning III:
      1. Theory of CNN
      2. Image Classification using CNN