Introduction to Data Science & Gen AI

Table of Contents

           Python for Data Analytics / Data Science

• Why learn Python for data analysis?
• How to install Python?
• Running a few simple programs in Python
• Python libraries and data structures
  • Python Data Structures Lists Strings Tuples
  • Python Iteration and Conditional Constructs
• Python Libraries
  • NumPy
  • SciPy
  • Matplotlib
  • Pandas
  • Scikit Learn
  • Statsmodels
  • Seaborn Bokeh
  • Blaze Scrapy
  • LLM Application
  • SymPy
  • Requests
• Exploratory analysis in Python using Pandas
  • Introduction to series and dataframes
  • Data Munging in Python using Pandas
• Building a Predictive Model in Python
  • Logistic Regression
  • Decision Tree
  • Random Forest
• Practice data set –
  • Loan Prediction Problem
  • Distribution analysis
  • Quick Data Exploration
  • Importing libraries and the data set:
  • Building a Predictive Model in Python
    •  Logistic Regression Decision
    •  Tree Random Forest

         Python

• Introduction To Python
  •  Installation and Working with Python
  •  Understanding Python variables
  •  Python basic Operators
  •  Understanding python blocks
• Python Data Types
  • Declaring and using Numeric data types: int, float, complex
  • Using string data type and string operations
  • Defining list and list slicing
  • Use of Tuple, Set, Dictionary data type
• Python Program Flow Control
  • Conditional blocks using if, else and elif
  • Simple for loops in python
  • For loop using ranges, string, list and dictionaries
  • Use of while loops in python
  • Loop manipulation using pass, continue, break and else
  • Programming using Python conditional and loops block
• Python Functions, Modules and Packages
  • Organizing python codes using functions
  • Organizing python projects into modules
  • Importing own module as well as external modules
  • Understanding Packages
  • Powerful Lamda function in python
  • Programming using functions, modules and external packages
• Python String, List and Dictionary Manipulations
  • Building blocks of python programs
  • Understanding string in build methods
  • List manipulation using in build methods
  • Dictionary manipulation
  • Programming using string, list and dictionary in build function
• Python File Operation
  • Reading config files in python
  • Writing log files in python
  • Understanding read functions, read(), readline() and readlines()
  • Understanding write functions, write() and writelines()
  • Manipulating file pointer using seek
  • Programming using file operations
• Python Object Oriented Programming – Oops
  • Concept of class, object and instances
  • Constructor, class attributes and destructors
  • Real time use of class in live projects
  • Inheritance, overlapping and overloading operators
  • Adding and retrieving dynamic attributes of classes
  • Programming using Oops support
• Python Exception Handling
  • try….except…else
  • try-finally clause
  • Avoiding code break using exception handling
  • Safeguarding file operation using exception handling
  • Handling and helping developer with error code
  • Programming using Exception handling
• Graphical User Interface
  • GUI in python Tkinter widgets
  • Programming using Tkinter
• Introduction to Numpy
  • Explain what Numpy is, its uses, and why it's important in data science and machine learning.
• Numpy Arrays
  • Discuss the concept of Numpy arrays, including creation, indexing, slicing, and reshaping.
• Array Operations
  • Cover basic array operations like addition, subtraction, multiplication, and division.
• Universal Functions (ufuncs)
  • Introduce universal functions and how they can be used to perform element-wise operations on arrays.
• Broadcasting
  • Explain the concept of broadcasting and how it allows Numpy to work with arrays of different shapes.
• Pandas
  • Introduction to Pandas
    • Explain what Pandas is, its uses, and its role in data manipulation and analysis.
  • Data Structures
    • Discuss the two main Pandas …

         Statistics

• Correlation
• Linear Regression
• Non Linear Regression
• K means clustering
• Find outlier
• Error Measure

          Machine Learning Algorithm

• Sentiment analysis with Machine learning C 5.0
• Support vector Machines
• K Means
• Random Forest
• Naïve Bayes algorithm
• Deep Learning, NLP ' LAN Chain, RAG, Agent K I, Generative AI

         Machine Learning & Deep Learning

• Introduction to Machine Learning & Data Science in Industry
  • Overview & Motivation
    • What is Machine Learning (ML), Artificial Intelligence (AI), and Data Science?
    • Real-world applications (e.g., predictive analytics, recommendation systems, process optimization)
    • Role of ML in decision making across industries
  • Course Roadmap & Tools
    • Introduction to the learning environment (Jupyter Notebooks, Python IDEs)
    • Overview of key libraries and frameworks
    • Setting career goals and project expectations
• Programming Foundations for ML
  • Python Refresher
    • Basic syntax, data types, control structures, functions, and error handling
  • Essential Libraries
    • NumPy: Array operations, linear algebra basics
    • Pandas: Data Frames, data manipulation, merging/ joining datasets
    • Visualization: Matplotlib, Seaborn, Plotly for exploratory data analysis
  • Practical Lab:
    • Working with real-world datasets to perform data cleaning, transformation, and visualization
• Mathematics & Statistics for Machine Learning
  • Mathematical Foundations
    • Linear Algebra: Vectors, matrices, eigenvalues/ eigenvectors, and their application in ML
    • Calculus: Derivatives, gradients, and optimization basics
  • Probability & Statistics
    • Descriptive statistics, distributions, hypothesis testing, and confidence intervals
    • Basics of Bayesian thinking and inference
  • Application:
    • How these mathematical tools underpin ML algorithms
• Data Acquisition, Cleaning, and Preprocessing
  • Data Sourcing
    • Data from databases (SQL/NoSQL), APIs, and web scraping techniques
  • Data Cleaning & Preparation
    • Handling missing values, outlier detection, and data normalization/scaling
    • Feature engineering and encoding categorical variables
  • Exploratory Data Analysis (EDA)
    • Techniques to summarize and visualize data trends and patterns
  • Hands-on Lab:
    • End-to-end EDA on a sample industry dataset
• Supervised Learning Techniques
  • Regression Analysis
    • Linear regression, polynomial regression, and regularization methods (Ridge, Lasso)
    • Evaluation metrics (Mean Squared Error, R2)
  • Classification Methods
    • Logistic regression, decision trees, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Naive Bayes
    • Model evaluation using confusion matrices, precision, recall, F1-score, ROC curves
  • Hands-on Lab:
    • Build, train, and evaluate regression and classification models using scikit-learn
• Unsupervised Learning Techniques
  • Clustering
    • K-Means, hierarchical clustering, DBSCAN
    • Methods to choose the optimal number of clusters (e.g., elbow method)
  • Dimensionality Reduction
    • Principal Component Analysis (PCA), t-SNE
  • Anomaly Detection
    • Techniques for identifying outliers and unusual patterns in data
  • Practical Session:
    • Applying clustering and dimensionality reduction to real-world datasets
• Neural Networks & Deep Learning
  • Fundamentals of Neural Networks
    • Architecture: layers, activation functions, and the backpropagation algorithm
    • Building a simple feedforward network
  • Deep Learning Architectures
    • Convolutional Neural Networks (CNNs) for image data.
    • Recurrent Neural Networks (RNNs) and LSTM for sequential data
    • Transfer learning basics
  • Frameworks:
    • Introduction to PyTorch
  • Lab:
    • Hands-on project: Build and train a neural network for a classification task
• Advanced Topics & Ensemble Methods
  • Ensemble Techniques
    • Bagging, boosting (AdaBoost, Gradient Boosting, XGBoost), and stacking
  • Reinforcement Learning (Overview)
    • Basic concepts and simple algorithm examples (e.g., Q-learning)
  • Hyperparameter Tuning & Model Optimization
    • Cross-validation, grid search, random search, and regularization techniques
  • Model Interpretability
    • Introduction to tools such as SHAP and LIME
  • Case Study:
    • Improve model performance on a benchmark dataset using ensemble methods
• Introduction to Deep Learning
• Neural Networks
• Backpropagation
• Keras
  • Introduction to Keras
  • Building a Neural Network in Keras
  • Training and Evaluating a Model

Data Visualization and Reporting tool (Choose One)

          Power BI

• Structured Query Language (SQL)
  • SQL commands
    • DDL DML commands
    • Create
    • Alter
    • Drop
    • Delete
  • Select queries
  • Where clause
  • Order by
  • Group by
  • Joins
  • Sub queries

• Introduction to Power BI
  • Get Started with Power BI
  • Overview: Power BI concepts
  • Sign up for Power BI
  • Overview: Power BI data sources
  • Connect to a SaaS solution
  • Upload a local CSV file
  • Connect to Excel data that can be refreshed
  • Connect to a sample
  • Create a Report with Visualizations
  • Explore the Power BI portal
• Viz and Tiles
  • Overview: Visualizations
  • Using visualizations
  • Create a new report
  • Create and arrange visualizations
  • Format a visualization
  • Create chart visualizations
  • Use text, map, and gauge visualizations and save a report
  • Use a slicer to filter visualizations
  • Sort, copy, and paste visualizations
  • Download and use a custom visual from the gallery
• Reports and Dashboards
  • Modify and Print a Report
  • Rename and delete report pages
  • Add a filter to a page or report
  • Set visualization interactions
  • Print a report page
  • Send a report to PowerPoint
  • Create a Dashboard
  • Create and manage dashboards
  • Pina report tile to a dashboard
  • Pin a live report page to a dashboard
  • Pin a tile from another dashboard
  • Pin an Excel element to a dashboard
  • Manage pinned elements in Excel
  • Add a tile to a dashboard
  • Build a dashboard with Quick Insights
  • Set a Featured (default) dashboard
  • Ask Questions about Your Data
  • Ask a question with Power BI Q&A
  • Tweak your dataset for Q&A
  • Enable Cortana for Power BI
• Publishing Workbooks and Workspace
  • Share Data with Colleagues and Others
  • Publish a report to the web
  • Manage published reports
  • Share a dashboard
  • Create an app workspace and add users
  • Use an app workspace
  • Publish an app
  • Create a QR code to share a tile
  • Embed a report in SharePoint Online
• DAX functions
  • New Dax functions
  • Date and time functions
  • Time intelligence functions
  • Filter functions
  • Information functions
  • Logical functions
  • Math & trig functions
  • Parent and child functions
  • Text functions

                                 OR

         Tableau

• Tableue Introduction
  • Tableue Architecture
  • The Tableue Interface
  • Distributing and Publishing
• Tableue Pre Builder
  • The Input Step
  • The Cleaning Step
  • Group and Replace
  • The Profile Pane
  • The Pivot Step
  • The Aggregate Step
  • The Join Step
  • The Union Step
• Connecting to Data
  • Getting Started with Data
  • Managing Metadata
  • Saving and Publishing Data Sources
  • Data Prep with Text and Excel Files
  • Join Types with Union
  • Cross-database Joins
  • Data Blending
  • Connecting to PDFs
• Visual Analytics
  • Getting Started with Visual Analytics
  • Drill Down and Hierarchies
  • Sorting
  • Grouping
  • Creating Sets
  • Set Actions
  • Ways to Filter
  • Using the Filter Shelf
  • Interactive Filters
  • Parameters
  • Formatting
  • Basic Tooltips & Viz in Tooltip
  • Trend Lines
  • Reference Lines
  • Forecasting
  • Clustering
• Dashboards and Stories
  • Getting Started with Dashboards and Stories
  • Building a Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Dashboard Extensions
  • Story Point
• Mapping
  • Getting Started with Mapping
  • Maps in Tableau
  • Editing Unrecognized Locations
  • Spatial Files
  • The Density Mark Type (Heat maps)
  • Expanding Tableau's Mapping Capabilities
  • Custom Geocoding
  • Polygon Maps
  • Mapbox Integration

• Calculations
  • Getting Started with Calculations
  • Calculation Syntax
  • Introduction to LOD Expressions
  • Intro to Table Calculations
  • Modifying Table Calculations
  • Aggregate Calculations
  • Date Calculations
  • Logic Calculations
  • String Calculations
  • Number Calculations
  • Type Calculations
  • Conceptual Topics with LOD Expressions
  • Aggregation and Replication with LOD Expressions
  • Nested LOD Expressions
• Why Tableue is doing it
  • Understanding Pill Types
  • Measure Names and Measure Values
  • Aggregation, Granularity, and Ratio Calculations
  • When to Blend and When to Join
  • One-to-many relationships
  • Joins inflating the number of rows
  • Filtering for Top Across Panes
• How to Use
  • Using a Parameter to Change Fields
  • Finding the Second Purchase Date with LOD Expressions
  • Cleaning Data by Bulk Re-aliasing
  • Bollinger Bands
  • Bump Charts
  • Control Charts
  • Funnel Charts
  • Step and Jump Lines
  • Pareto Charts
  • Waterfall Charts

                 Projects

• Machine learning(Simple linear regression) -Project 1
• Machine learning(Multiple linear regression) -Project 2
• Machine learning(Logistic Regression) -Project 3
• Machine learning(Natural Language Processing) -Project 4

This structure combines all the provided information into a comprehensive table of contents, organized by topic area. You can choose to focus on either Power BI or Tableau in your actual course delivery.