Data Science and AI

Data Science and AI Course Curriculum (Syllabus)

Course Introduction:

Welcome to our comprehensive Data Science and Artificial Intelligence (AI) course! This program is designed to provide you with a strong foundation in data science, machine learning, and AI techniques. You'll learn essential skills in Python programming, machine learning algorithms, data visualization, and more. Real-world projects and career guidance are integrated to give you hands-on experience and help you embark on a successful career in data science and AI.

Job Roles in Data Science and AI:

Upon completing this course, you'll be prepared for a diverse range of job roles in the data science and AI domain, including:

· Data Scientists

· Machine Learning Engineers

· AI Researchers

· Business Intelligence Analysts

· Data Engineers

· Python Developers

These roles encompass tasks such as data analysis, machine learning model development, AI research, business intelligence, data engineering, and Python development, providing you with a wide range of opportunities in the data-driven industry.

Module 1: Introduction to Data Science and AI

· Introduction to Data Science (DS) and Artificial Intelligence

· What is Data Science (DS)

· What is Artificial Intelligence (AI)

· What is machine learning? (ML)

· What is deep learning (DL)?

· Relatonship among AI, ML, DL and DS

· DS and AI Applications in HealthCare

· DS and AI Applications in Entertainment Industry

· DS and AI Applications in IoT

· DS and AI Applications in Retail

· DS and AI Applications in Agriculture

· DS and AI Applications in Cyber Security

· Different Tools or Technologies involved in DS and AI

o SQL

o Python

o Maths and Statistics

o Jupyter Notebook

o Visual Studio Code

o Scikit-learn.

o Numpy

o Pandas

o Matplotlib

o Seaborn

o Keras

o TensorFlow

o Cloud: Azure/AWS

· Different Job Roles in DS and AI

· Why is Data Scienceso Demand today?

· What’s the future of Data science and AI?

Module 2: SQL for Data Science

· Introduction to SQL

· Its importance in data science

· Understanding databases and their structure

· Installing and setting up SQL environments (e.g., MySQL, MS SQL Server)

· Basic SQL Operations

· Creating databases and tables

· Inserting, updating, and deleting data

· Retrieving data using SELECT statements

· Filtering and Sorting Data

· Using WHERE clause for data filtering

· Comparison operators

· Logical operators

· Sorting retrieved data using ORDER BY

· Aggregate Functions and Grouping

· Understanding aggregate functions (COUNT, SUM, AVG, MIN, MAX)

· Performing calculations on grouped data

· Using GROUP BY clause to aggregate data

· Working with Strings and Dates

· Manipulating strings with functions like CONCAT, SUBSTRING

· Handling date and time data using date functions

· Joining Tables

· Understanding the concept of table joins (Ven Diagrams)

· Implementing INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN

· Handling NULL values in joins

· Subqueries and Derived Tables

· Understanding subqueries and their role in SQL

· Using subqueries for complex queries and aggregations

· Utilizing derived tables for intermediate results

Module 3: PythonProgramming language

· Introduction to Python

· Role of python in Data science

· Explanation of Python's simplicity and readability.

· Python Syntax

· Data Types : (integers, floats, strings, booleans).

· Variablesdeclaration

· Variables assignment

· Variables naming conventions

· Lists in Python

· List creation, indexing, slicing, and modifying

· Sets in Python:

· Set operations (union, intersection, difference).

· Sets: data deduplication

· Sets: unique value extraction.

· List Comprehensions:

· Control Structures:

o for loops,

o while loops.

· Conditional Statements (if and elif)

· Logical operators (and, or, not)

· Lambda Functions

· Map, Filter

· Create, Read, Write Files

· File Operations & Errors

· Introduction to Classes and Objects (OOPS)

· Classes & Objects

· Create Class & Methods

· Working with Objects

· The init() Method

· Modify Properties & Methods

· 'self' Parameter

· Delete Objects

· 'pass' Statements

Module 4: Pythonfor Data Analytics and Data Science

· Real-world data science scenarios using Python.

· Numpy

· Pandas

· Matplotlib

· Types of Data: Structured, Unstructured, Semi-Structured:

· Structured (tabular)

· Unstructured (text, images), and

· Semi-structured (XML, JSON).

· Numpy

· Introduction to Array

· Creation and Printing of an array

· Basic Operations in NumPy

· Indexing

· Numpy: Where, count, arg

· Pandas

· What is Pandas Data frame

· Tabular data structure with rows and columns

· Series, Index

· Read_csv, Head, Tail

· Shape, Columns

· Iloc, loc, Drop

· GroupBy: Grouping and aggregation operations.

· Reshaping: Dataframe manipulation

· Plotting: Data visualization tools.

· Missing Data:

· Merge and Join: Combining DataFrames.

· Matplotlib

· Figure: Top-level container.

· Axes: Individual plots.

· Line Plot: Connects data points.

· Scatter Plot: Displays individual points.

· Bar Plot: Uses rectangular bars.

· Histogram: Shows data distribution.

· Pie Chart: Displays composition.

· Annotations: Adds text/arrows.

· Subplots: Divides figure.

· Styles: Customizes appearance.

· Case Study on Exploratory Data Analysis (EDA) and Visualizations

· What is EDA?

· Uni – Variate Analysis

· Bi-Variate Analysis

· More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.

Module 5: Stats and Maths

· Statistics in Data science:

o What is Statistics?

o Role in Data Science

o Population vs. Sample

o Parameter vs. Statistic

o Types of Variables

· Data Gathering Techniques

o Collecting Data

o Sampling Techniques:

o Convenience, Simple Random Sampling

o Stratified, Systematic, Cluster Sampling

· Descriptive Statistics

o Univariate and Bivariate Analysis

o Central Tendencies

o Measures of Dispersion

o Skewness and Kurtosis

o Box Plots and Outliers

o Covariance and Correlation

· Probability Distribution

o Basics of Probability

o Discrete Distributions:

o Bernoulli, Binomial, Poisson

o Continuous Distributions:

o Normal, Standard Normal

· Inferential Statistics

o Central Limit Theorem

o Confidence Intervals, p-value

o Hypothesis Testing

o Z-test, T-test

o Chi-Square Test

Module 6: Machine Learning : Supervised Learning

· What’s supervised learning: Regression and Classification

· Linear Regression:

o Concept: Models linear relationships between independent and dependent variables.

o Data Preparation

o Model Representation

o Gradient Descent

o Concept and Purpose

o Steps in Gradient Descent

o Cost Function (MSE)

o Definition and Purpose

o Solving Linear Regression

o Normal Equation Method

o Metrics:Mean Absolute Error (MAE), R-squared (R2) Score

o Example: House price prediction

· K-Nearest Neighbors (KNN):

o Concept: Classifies data points based on their nearest neighbors in feature space.

o Introduction to k-NN Classifier

o Data Preparation

o How k-NN Works

o Utilizing Nearest Neighbors

o Distance Metrics

o Optimal Value of k

o Making Predictions

o Evaluating Performance

o k-NN: Strengths and Weaknesses

o Real-world Applications of k-NN

o Classification on Iris Dataset

o Metrics:Classification Accuracy

· Support Vector Machines (SVM):

o Concept: Finds a hyperplane that best separates classes in feature space.

o Introduction to Support Vector Machines (SVM)

o Data Preparation for SVM

o Understanding the SVM Algorithm

o Concept and Purpose

o Kernel Trick for Non-Linear Data

o Hyperparameter Tuning in SVM

o Making Predictions with SVM

o Evaluating SVM Performance

o Classification Accuracy

o Confusion Matrix

o F1-score (for binary classification)

o Pros and Cons of SVM

o Applications of SVM

· Logistic Regression:

o Concept: Models the probability of a binary target variable given predictors.

o Introduction to Logistic Regression

o Data Preparation for LR

o Understanding the LR Model (Sigmoid Function)

o Interpreting LR Coefficients

o Training the LR Model

o Cost Function (Log Loss)

o Evaluating LR Performance

o Accuracy, Precision, Recall, F1-score

o Confusion Matrix

o ROC-AUC Score

o Regularization in LR (L1 and L2)

o Pros and Cons of LR

o Applications of LR

o Disease prediction using LR

· Random Forest:

o Concept: Ensemble method combining multiple decision trees for improved accuracy.

o Data Prep

o How it Works

o Ensemble of Trees

o Feature Importance

o Training

o Evaluation

o Accuracy (Classif.)

o MAE (Reg.)

o Overfitting Handling

o Pros and Cons

o Applications: Breast Cancer Wisconsin (Diagnostic) dataset

Module 7: Machine Learning: Unsupervised Learning

· Different types of techniques in Unsupervised Learning

· Dimension Reduction:

o Why Dimension Reduction is Important

o Principal Component Analysis (PCA)

o Concept and Purpose

o Steps in PCA

o t-Distributed Stochastic Neighbor Embedding (t-SNE)

o Concept and Use Cases

o Applications: Image Compression

· Introduction to Clustering

o Types of Clustering Algorithms (Focusing on KMeans)

o How KMeans Works

o Concept and Purpose

o Choosing the Optimal Number of Clusters (k)

o Applying KMeans for Clustering

o Evaluating Clustering Performance

o Silhouette Score

o Inertia

o Demo on using Digits Datasets

· Introduction to Recommender Systems

o Types: Content-Based, Collaborative Filtering

o Collaborative Filtering: User-Based, Item-Based

o Matrix Factorization: SVD

o Evaluation Metrics: RMSE, MAE

o Cold Start Problem & Solutions

o Applications: Product recommendations

Module 8: Deep Learning

· Perceptron& Neural Network History

· Activation Functions

· Sigmoid, Relu, Softmax, Leaky Relu, Tanh

· Gradient Descent

· Learning Rate Tuning

· Optimization Functions

· TensorFlow Introduction

· Keras Introduction

· Backpropagation & Chain Rule

· Fully Connected Layer

· Cross Entropy

· Weight Initialization

· Regularization

· TensorFlow 2.0

· TensorFlow basic syntax

· TensorFlow Graphs

· Tensorboard

· Artificial Neural Network with TensorFlow

· Regression

· Classification

· Evaluating the ANN

· Improving and tuning the ANN

· Saving and Restoring Graphs

Module 9: NLP

· Statistical NLP Basics

· Intro to NLP

· Text Prep (Cleaning, Simplifying)

· Word Importance (Bag of Words, TF-IDF)

· Language Patterns (N-grams, Channel Model)

· Word Representation

· Word Meanings (Word2vec, Glove)

· Tagging Words (POS Tagger)

· Spotting Names (NER)

· Identifying Words (POS with NLTK, TF-IDF with NLTK)

· Sequential Models

· Remembering Sequences (RNN, LSTM)

· LSTM in Detail (Forward & Backward)

· Practical LSTM (Hands-on)

· Practical Applications

· Judging Feelings (Sentiment Analysis)

· Creating Sentences

· Changing Languages (Machine Translation)

Module 10: Computer Vision Basics

· Introduction to Computer Vision

· Image Representation

· Color Channels

· Introduction to Convolutional Neural Networks (CNNs)

· Motivation for CNNs

· Building Blocks of CNNs

· Convolutional Layers

· Pooling Layers

· Fully Connected Layers

· Advanced CNN Concepts

· Activation Functions (ReLU, etc.)

· Batch Normalization

· Dropout

· Training CNNs

· Loss Functions

· Optimization Methods (SGD, Adam, etc.)

· Backpropagation in CNNs

· Popular CNN Architectures

o LeNet

o AlexNet

o VGG

o ResNet

· Image Classification and Recognition

· Understanding Labels and Classes

· Top-k Accuracy

· Visualizing CNN Activations

· Practical Projects:

o Implementing CNN for Image Classification

o Building an Object Detector

o Image Understanding with Semantic Segmentation

Module 11: Data Science Project in the Cloud

Overview:

Introduction to cloud computing
its role in data science projects.

Key Topics:

[Topic 1]: Setting up cloud environments for data science projects.
[Topic 2]: Deploying an end-to-end data science project in the cloud.
[Topic 3]: Practical steps for model training and deployment using cloud resources.

Practical Applications:

o Real-world examples of data science projects hosted in the cloud.

o Demonstrations of cloud-based data processing and model deployment.

Hands-On Experience:

Setting up cloud environments (e.g., AWS, Azure) for data science work.
Deploying and managing data science projects in cloud platforms.

Module 12: Real-world Projects and Case Studies

Application of concepts learned throughout the course in practical projects.

Real-world data science projects across various domains (3-4).

Credit Card Fraud detection
Movie Recommender System (IMDB)
Image Classification on CIFAR 10
Sentiment Analysis on product page reviews
Crop Disease detection using AI and Computer Vision
Speech to Text Detection for Digits

Insights from industry experts and their experiences in data science.
Completion of hands-on data science projects.
Presentation of findings and insights from real-world datasets.
Assessment:

o Evaluation of final data science projects.

o Peer assessment and presentation evaluation.

Module 13: Career and Industry Insights

Overview:

o Exploration of job roles in data science, AI, and ML.

o Guidance on resume building and interview preparation.

o Discussion on continuing education and lifelong learning in the field.

Key Topics:

o [Topic 1]: Job roles in data science, AI, and ML.

o [Topic 2]: Resume building and interview preparation for data science positions.

o [Topic 3]: Strategies for ongoing learning and professional development.

Practical Applications:

o Real-world insights into career paths and opportunities in data science.

o Tips and best practices for job application and interviews.

Hands-On Experience:

o Resume building exercises and mock interview practice.

o Development of a personalized career development plan.

· Evaluation of resumes and interview preparation progress.

· Completion of a career development plan.

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