Data Science and AI training

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)


·         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.


Tags: Data Science and AI