Data Science Online Training for Industry-Ready Skills

Introduction to Python

• What is Python?
• Why use Python for Data Science?
• Installation of Anaconda
• Understanding Jupyter Notebook (IDE)
• Python syntax and basic commands

Core Python Concepts

• Variables, Data Types, and Strings
• Lists, Tuples, Sets, and Dictionaries
• Conditional Statements and Loops(if, while, For, while, pass, break,continue)
• Functions and Lambda Functions
• Map, Filter and Reduce
• Exception Handling

Object-Oriented Programming (OOP) in Python

• Classes and Objects
• Constructors (init method)
• Object Methods and Properties
• Inheritance and Polymorphism

Modules and File Handling

• Importing and Using Modules
• File Handling: Read, Write, and Append Operations

Flask Framework for API Development

• Introduction to Flask
• Creating and Deploying APIs using Flask
• Connecting APIs with Data Science Models

Data Analysis with NumPy G Pandas

• Introduction to NumPy and Arrays
• Creation and Printing of array
• Basic Operations in Numpy
• Mathematical Functions of Numpy
• Introduction to Pandas
• Pandas Data Structures : Data Frame and Series
• Data Importing and Exporting (CSV, Excel, JSON)
• Data Cleaning: Handling Missing Values C Duplicates
• Data filtering and Data manipulation
• Grouping, Merging, and Pivot Tables

Data Visualization with Matplotlib G Seaborn

• Basic and Advanced Plotting
• Histograms, Pie Charts, and Bar Graphs, Scatter Plot
• Box Plots, Violin Plots, and Heatmaps
• Exploratory Data Analysis (EDA) using Seaborn

Unstructured Data Processing G Web Scraping

• Regular Expressions for Text Processing
• Web Scraping with BeautifulSoup and Scrapy
• Converting Unstructured Data into Structured Format
• Case Study: Web Scraping for Data Collection

Hands-on Activities:

• Web Scraping for Data Collection
• Data Analysis on a data set

Fundamentals of Statistics

• Descriptive vs Inferential Statistics
• Measures of Central Tendency and Dispersion
• Correlation and Covariance

Probability G Probability Distributions

• Probability Concepts and Bayes' Theorem
• Discrete Distributions: Bernoulli, Binomial, Poisson
• Continuous Distributions: Normal, Standard Normal

Inferential Statistics G Hypothesis Testing

• Confidence Intervals and Sampling Techniques
• Hypothesis Testing: Z-Test, p-value, Chi-Square Test, ANOVA

Hands-on Activities:

• Perform Hypothesis testing using Python
• Solve stats and Probability problem using python

Data Profiling:

• Handling missing values, outliers, and inconsistent data
• Uni-variate and Bi-variate analysis

Feature Engineering:

• Normalization, encoding categorical variables, feature selection.

Automating EDA with Generative AI:

• Use AI models to create data summaries and insights

Hands-on Activities:

• Perform EDA on a dataset using Python libraries
• Create an AI-assisted EDA pipeline using Hugging Face or OpenAI tools

SǪL for Data Science

• Basics of SǪL and Databases
• SǪL Commands: SELECT, WHERE, GROUP BY, ORDER BY
• Joins: INNER, OUTER, LEFT, RIGHT
• CASE and DATE functions as well as work with NULLvalues
• Aggregations (Min,Max,Sum, Avg) and Subqueries
• Window Functions: ROW_NUMBERS(), RANK(), DENSE_RANK(), LAG, LEAD
• Common Table Expressions (CTE)

Business Intelligence with Power BI

• Introduction to Power BI
• Power BI Desktop overview
• Power BI Data Types
• Data Import and Filtering: csv, excel and web
• Transformation: Power Ǫuery/M- Language
• Data Modeling and DAX Functions : Logical,Text, Aggregation, Time Intelligent,math and Stats Functions, filter
• Creating Dashboards and Reports : Slicer details , Tables and Matrix
• Power Ǫuery and Power Pivot
• Ǫueries: Merge and Combine
• Row Level Security
• Conditional Formatting and python script for visuals

Hands-on Activities:

• Analyze data using SǪL
• Analyze and Build a Dashboard using PowerBI

Supervised Learning

• Introduction to Machine Learning
• Regression: Linear, Polynomial,SVM and Regularization Techniques
• Classification: Logistic Regression, Naïve Bayes, Decision Trees
• Model Evaluation

Ensemble Learning G Model Evaluation

• Random Forest and Gradient Boosting
• Evaluation Metrics: Confusion Matrix, Precision-Recall, ROC-AUC
• Cross-validation and Hyperparameter Tuning

Un-Supervised Learning

• Principal Component Analysis
• Clustering: K-means, Hierarchical: Agglomerative Clustering
• Recommendation systems

Hands-on Activities:

• Build a Regression model for Stock price prediction
• Build a Classification model
• Apply K-Means clustering on customer segmentation data
• Create a recommendation system

Introduction to Generative AI

• Overview of Generative AI
• Difference between Traditional AI and Generative AI
• Applications of Generative AI in Data Science

Deep Learning Fundamentals

• Neural Networks: Perceptrons , Activation Functions, Gradient Descent,Back Propagation, Feed Forward
• Training Neural Networks using TensorFlow/Keras
• Convolutional Neural Networks (CNNs) : Image Processing and Object Detection
• Transfer Learning
• CNNs at Work - Semantic Segmentation, Siamese Network

Generative Models

• Basic Autoencoders
• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• Case Study: Implementing GANs for Image Generation

Large Language Models (LLMs) G NLP

• Natural Language Processing (NLP) Basics
• Tokenization, stop words, normalization, Stemming and lemmatization
• Bag of words, TF-IDF, Word2vec, POS Tagger, Name Entity Recognition(NER)
• Sequential Models : RNN , LSTM
• Text Generation using GPT Models
• Sentiment Analysis and Chatbots
• Prompt Engineering for LLMs

Hands-on Activities:

• Summarize text using Hugging Face's T5 or OpenAI’s GPT API
• Generate images with DALL-E or Stable Diffusion
• Develop a simple chatbot using AI APIs

Introduction to LangChain

• Foundations and Core Concepts
• Benefits of Using LangChain
• Value Propositions for LLM Applications

Key Components of LangChain

• Understanding LangChain's Architecture
• Off-the-Shelf Chains in LangChain
• Customizing and Extending Chains

Building and Deploying LLM-Powered Applications

• Hands-on: Developing an End-to-End LLM Application
• Deployment Strategies and Best Practices

Retrieval-Augmented Generation (RAG) with LangChain

• Concept and Importance of RAG
• Applications in LLM Workflows

Working with Data in LangChain

• Document Loading and Splitting
• Vector Stores and Embeddings
• Retrieval Mechanisms for LLMs

Hands-on Activity:

• Building a RAG Model using LangChain
• Build an anomaly detection system using Autoencoders

Ethics in AI

• Bias, privacy, and responsible AI practices

Challenges:

• Interpretability, scalability, and overfitting

Trends:

• AI democratization, edge AI, and explainable AI

Activities:

• Group discussions: Ethical dilemmas in AI
• Analyze case studies on bias and privacy

Model Deployment G Cloud Integration

• Deploying Models with Flask and FastAPI
• Docker and Kubernetes for ML Models
• Cloud Deployment with AWS/GCP/Azure

Case Studies G Capstone Project

• End-to-End Data Science C GenAI Project
• Real-World Business Applications
• Best Practices C Future Trends in AI/ML