Data Science and AI Bootcamp

• Basic Data and Statistical Concepts
• Data Literacy
• Statistical Foundations
• Descriptive Statistics
• Statistical Inference and Sampling Techniques
• Regression Analysis
• Data Cleaning, Preparation, and Management
• Data relationships, data shapes, and primary/unique keys and identifiers
• Basic data cleaning
• Data extraction from external sources
• Sorting, Filtering, and Merging Data
• Common excel formulas and functions
• Further Concepts in Data Cleaning
• Excel Functions for Data Cleaning (E.g. Left, Right, Concatenate, etc.)
• Conditional Formatting
• Data Validation and Error Checking
• Basic Data Processing
• IF and Nested IF statements
• Absolute and Relative cell references
• Data Analysis with Intermediate Excel
• Processing large datasets
• Find, Find & Replace
• Advanced Commands (VLookUp, HLookUp, Index, Match)
• Introduction to Pivot Tables
• Navigating Pivot Tables Interface
• Creating basic Pivot Tables
• Field Settings and layout options
• Working with pivot table data
• Advance Data Analysis with Pivot Tables
• Advanced pivot table functions
• Data slicing
• Pivot charts and visualizations
• Advanced Pivot table visualizations
• Further Data Visualization on Excel
• Intermediate and Advanced Excel Charting Techniques
• Excel Add-ins for advanced visualization
• Data Analysis and Visualization on Google Sheets

Conceptual Overview
Objective: Understand the distinct roles and responsibilities of data scientists
and data engineers within projects.
2. Data Sourcing with Python
Objective: Extracting data from sources like PostgreSQL, MongoDB, APIs, web
scraping, and IoT devices.
3. Databricks Tour
Objective: Get introduced to Databricks as a platform for big data processing
and machine learning.
4. Big Data Handling for Data Science
Objective: Process large-scale datasets using Apache Spark within a distributed
environment.
5. MLflow
Objective: Learn to track experiments and ensure reproducibility in machine
learning workflows.
6. Real-World Project Walkthrough
Objective: Apply learned skills in a practical, case-study-based project

• Installing SQL Workbench

• Introduction to SQL

  • Overview of Database Management Systems(DBMS)
  • Basic SQL syntax and structure
  • Flow of SQL commands
  • Data Types in SQL
  • Running SQL commands using SELECT statement
  • Retrieving data from tables

• Filtering, Sorting, and Aggregating Data

  • Using WHERE clause to filter data based on conditions
  • Sorting query results using ORDER BY clause
  • Limiting the number of results with LIMIT clause
  • Using aggregate functions for summary statistics
  • Grouping query results using GROUP BY clause
  • Filtering grouped data with HAVING clause

• Table Joins and Case Statements

  • Case statements
  • Understanding relationships between tables
  • Using joins to combine data from multiple tables

• Advanced Query Techniques

  • Further practice with different types of joins
  • Working with subqueries
  • Working with derived tables
  • Common table expressions (CTEs)

• Window Functions and Data Modification

  • Working with window functions
  • Ranking data using RANK and DENSE_RANK
  • Modifying data in tables using ALTER, RENAME, INSERT, UPDATE, DELETE
  • Using UNION, INTERSECT, and EXCEPT to combine query results

• Creating New Tables

  • Further practice with window functions
  • Creating new tables
  • Inserting values into new tables
  • Modifying and updating new tables

• Advanced Topics and Artificial Intelligence

  • Working with Primary Keys
  • Auto-Increments
  • Updating Tables
  • Indexing
  • Using AI in SQL programming

• Date Variables and Artificial Intelligence

  • Dealing with date variables in SQL data
  • Setting variables in SQL
  • Using AI in SQL programming
  • Wrapping up

• Introduction to PowerBI/ Connecting & Shaping Data

  • Download and install Power BI Desktop, and adjust the settings.
  • Understand the role that Power BI plays within the broader Microsoft ecosystem
  • Explore core components of the Power BI Desktop interface
  • Review the business intelligence workflow.
  • Explore Power BI’s query editor and understand the role that Power Query plays in the larger BI workflow
  • Introduce different types of connectors and connectivity modes available for getting data into Power BI
  • Review tools for checking data quality and key profiling metrics like column distribution, empty values, errors, and outliers
  • Transform tables using text, numerical and date/time tools, pivot and group records, and create new conditional columns
  • Practice combining, modifying, and refreshing queries

• Data Modeling

  • Understand the basic principles of data modeling, including normalization, fact & dimension tables, and common schemas
  • Create table relationships using primary and foreign keys, and discuss different types of relationship cardinality
  • Configure report filters and trace filter context as it flows between related tables in the model
  • Explore data modeling options like hierarchies, data categories, and hidden fields

• DAX

  • Introduce DAX fundamentals and learn when to use calculated columns and measures
  • Understand the difference between row context and filter context, and how they impact DAX calculations
  • Learn DAX formula syntax, basic operators and common function categories (math, logical, text, date/time, filter, etc.)
  • Explore nested functions, and more complex topics like iterators and time intelligence patterns

• Data Visualization

  • Review frameworks and best practices for visualizing data and designing effective reports and dashboards
  • Explore tools and techniques for inserting, formatting and filtering visuals in the Power BI Report view
  • Add interactivity using tools like bookmarks, slicer panels, parameters, tooltips, and report navigation
  • Learn how to configure row-level security with user roles
  • Optimize reports for mobile viewing using custom layouts

• Introduction to Spatial Analysis

  • Understanding Spatial Data: Definition, significance, and examples of spatial data.
  • Spatial Data Types: Differentiating between vector and raster data.
  • Applications of Spatial Analysis: Overview of how spatial analysis is used in various fields such as environmental science, urban planning, and public health.
  • Introduction to GIS: Understanding Geographic Information Systems and their role in spatial analysis.

• Introduction to QGIS

  • Getting Started with QGIS: Installing QGIS and familiarizing with the interface.
  • Basic Operations: Opening and viewing spatial data, navigating the map canvas, and managing layers.
  • Data Import and Export: How to import and export different spatial data formats in QGIS.

• Creating Shapefiles (Point, Line, and Polygon)

  • Understanding Shapefiles: The structure and components of shapefiles.
  • Creating New Shapefiles: Step-by-step guide to creating point, line, and polygon shapefiles.
  • Editing Shapefiles: Adding, modifying, and deleting features in shapefiles.

• Learning Basic Cartography

  • Map Elements: Understanding essential map elements (title, legend, scale, north arrow).
  • Design Principles: Color theory, symbolization, and layout design for clear and effective map-making.
  • Creating Maps: Practical exercises to design maps using QGIS.

• Learning Basic Vector Analysis Tools

  • Spatial Queries: Selecting features based on spatial relationships (e.g., proximity, intersection).
  • Buffer Analysis: Creating buffers around features and understanding their applications.
  • Overlay Analysis: Performing spatial operations like intersect, union, and difference.

• Creating Heatmaps

  • Understanding Heatmaps: Concept and applications of heatmaps in representing data density.
  • Generating Heatmaps in QGIS: Step-by-step guide to creating heatmaps from point data.
  • Customization: Adjusting parameters to refine the heatmap visualization.

• Using Google Maps to Create Maps in QGIS

  • Integrating Google Maps: Methods to use Google Maps as basemaps in QGIS projects.
  • Georeferencing: Aligning external map images with spatial data using georeferencing tools.
  • Practical Exercise: Creating a map project in QGIS with Google Maps as the base layer.

• Introduction to Raster Data

  • Understanding Raster Data: Definition, structure, and examples of raster data.
  • Raster Analysis Tools: Introduction to basic raster operations (e.g., map algebra, reclassification).
  • Working with Satellite Imagery: Basics of accessing and analyzing satellite imagery in QGIS.

• Real-world Examples of GIS in Various Industries and Fields

  • Environmental Management: Use cases of GIS in conservation, pollution monitoring, and resource management.
  • Urban Planning: Applications in zoning, infrastructure development, and traffic management.
  • Public Health: Spatial analysis in epidemiology, access to healthcare facilities, and health outcome mapping.
  • Agriculture: Precision farming, crop yield analysis, and soil mapping.

• Introduction to Business Intelligence

  • Concepts and Importance of BI: Understanding BI, its components, and its importance in decision-making.
  • BI vs. Data Science: Differentiating BI from Data Science and understanding their intersection.
  • Overview of BI Tools: Introduction to popular BI tools and technologies (e.g., Tableau, Power BI).

• Data Warehousing and ETL Processes

  • Data Warehousing Concepts: Understanding data warehousing, data marts, and the role they play in BI.
  • ETL Processes: Introduction to Extract, Transform, Load (ETL) processes and tools.
  • Hands-on ETL Project: Practical exercise involving data extraction, data cleansing, transformation, and loading into a data warehouse.

• SQL for Business Intelligence

  • Advanced SQL Queries: Writing complex SQL queries for data analysis and reporting.
  • SQL for Data Manipulation: Techniques for data manipulation and preparation for BI applications.
  • Hands-on SQL Project: Using SQL to solve a business problem and prepare data for analysis.

• Data Visualization and Dashboarding

  • Principles of Data Visualization: Best practices for designing effective and informative visualizations.
  • Introduction to Tableau/Power BI: Getting started with a BI tool, setting up, and basic functionalities.
  • Dashboard Creation: Designing and developing interactive dashboards for business reporting.

• Analytical Reporting and Decision Making

  • Creating Reports: Techniques for creating comprehensive and insightful reports.
  • Storytelling with Data: Learning how to tell a compelling story using data visualizations and reports.
  • Decision Making with BI: Understanding how to use BI reports and dashboards to make informed business decisions.

• Advanced BI Tools and Techniques

  • Predictive Analytics in BI: Introduction to incorporating predictive analytics into BI for forecasting.
  • Real-Time BI: Understanding real-time BI and analytics for dynamic decision-making.
  • Hands-on Project with Advanced Tools: Applying predictive analytics and real-time data in BI projects.

• Implementing BI Solutions

  • BI Strategy and Implementation: Planning and executing a BI project from start to finish.
  • Managing BI Projects: Best practices for managing BI projects and ensuring their success.
  • Case Study: Analysis of a successful BI implementation in a business.

• Business Intelligence in Practice

  • Industry-Specific BI Applications: Exploring how BI is used in different industries such as finance, healthcare, retail, and more.
  • Emerging Trends in BI: Discussion on AI, machine learning in BI, and future directions.
  • Group Project: Developing a BI solution for a real-world business problem.

• Installation

  • Introduction to the Python programming language and its applications
  • Setting up the Python environment: installation of Python and necessary libraries
  • Configuring the development environment: IDEs, text editors, and Jupyter Notebook

• Python Basics

  • Introduction to Python: history, features, and advantages
  • Expressions and operators: arithmetic, assignment, comparison, and logical
  • Understanding type() function and type inference
  • Introduction to data structures: lists, tuples, and dictionaries

• Python Basics

  • Recap of Python basics
  • Working with arithmetic operators: addition, subtraction, multiplication, division, modulus, and exponentiation
  • Using comparison operators: equal to, not equal to, greater than, less than, etc.
  • Logical operators: and, or, and not
  • Exploring advanced data types: sets and strings manipulation

• Expressions, Conditional Statements & For Loop

  • Evaluating expressions: operator precedence and associativity
  • Introduction to conditional statements: if, elif, and else
  • Executing code based on conditionals.
  • Understanding the flow of control in conditional statements
  • Iteration using the for loop: range(), iteration over lists, and strings.

• While loop, Break and Continue Statements, and Nested Loops

  • Working with while loop: syntax, conditions, and examples
  • Combining loops and conditionals
  • Using the break statement to exit loops prematurely.
  • Utilizing the continue statement to skip iterations.
  • Implementing nested loops for complex iterations

• Functions

  • Introduction to functions: purpose, advantages, and best practices
  • Defining and calling user-defined functions
  • Parameters and arguments: positional, keyword, and default values
  • Return statement and function output.
  • Variable scope and lifetime
  • Function documentation and code readability

• Exception Handling and File Handling

  • Understanding exceptions: errors, exceptions, and exception hierarchy
  • Handling exceptions using try-except blocks: handling specific exceptions, multiple exceptions, and else and finally clauses.
  • Raising exceptions and creating custom exception classes
  • File handling in Python: opening, reading, writing, and closing files.
  • Working with different file modes and file objects

• Python Modules: NumPy and Matplotlib

  • Introduction to the NumPy module: features and applications
  • Working with multidimensional arrays: creation, indexing, slicing, and reshaping
  • Performing element-wise operations: arithmetic, logical, and statistical
  • Overview of the Matplotlib module: data visualization and plotting
  • Customizing plots: line properties, markers, colors, labels, and legends

• Mathematics for Data Science

  • Vectors and Matrices: Definition, addition, scalar multiplication.
  • Matrix Multiplication: Concept and application in data transformation and neural networks.
  • Eigenvalues and Eigenvectors: Importance in dimensionality reduction techniques like PCA
  • Derivatives and Gradients: Understanding how changes in input affect changes in output.
  • Partial Derivatives and the Gradient Vector: Application in gradient descent and optimization.
  • Introduction to Optimization: Concept of loss functions and how gradients are used to minimize them.
  • Probability Theory: Basics and conditional probability.
  • Bayes' Theorem: Importance in machine learning for making predictions.
  • Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
  • Distributions: Normal distribution and its significance in machine learning.
  • Beyond Gradient Descent: Introduction to stochastic gradient descent and mini-batch gradient descent.
  • Regularization Techniques: L1 (Lasso) and L2 (Ridge) regularization to prevent overfitting.
  • Introduction to Convex Optimization: Understanding convex functions and their relevance to machine learning optimization.

• Introduction and Missing Value Analysis

  • Introduction to Exploratory Data Analysis (EDA)
  • Importance of EDA in data analysis
  • Steps involved in EDA
  • Handling missing values: identification, analysis, and treatment strategies
  • Imputation techniques for missing values

• Data Consistency, Binning, and Outlier Analysis

  • Data consistency checks using fuzzy logic
  • Binning and discretization techniques for continuous variables
  • Outlier detection and analysis methods
  • Handling outliers: techniques for treatment or removal

• Feature Selection and Data Wrangling

  • Importance of feature selection in EDA
  • Feature selection techniques: filter methods, wrapper methods, and embedded methods
  • Data wrangling: cleaning and transforming data for analysis
  • Handling categorical variables: encoding techniques

• Inference, Hypothesis Testing, and Visualization

  • Inference and hypothesis testing in EDA
  • Common statistical tests: t-test, chi-square test, ANOVA, etc.
  • Visualization techniques for EDA: histograms, box plots, scatter plots, etc.
  • Hands-on practical session for complete EDA using a dataset

• Machine Learning Performance Metrics and Naive Bayes

  • Evaluation metrics for classification problems: accuracy, precision, recall, F1 score, etc.
  • Introduction to Naive Bayes algorithm and its applications
  • Implementing Naive Bayes for classification tasks

• Logistic Regression, SVM, Decision Trees, and Random Forests

  • Logistic Regression: theory, interpretation, and applications
  • Support Vector Machines (SVM): concepts, kernels, and use cases
  • Decision Trees: construction, pruning, and interpretability
  • Random Forests: ensemble learning and feature importance
  • Bagging and Boosting: techniques for improving model performance

• Clustering Introduction, Partitioning Algorithms, and Cluster Evaluation

  • Introduction to clustering: unsupervised learning technique
  • Partitioning algorithms: K-means, K-medoids
  • Hierarchical clustering: agglomerative and divisive approaches
  • Density-based clustering: DBSCAN, OPTICS
  • Cluster evaluation metrics: silhouette coefficient, Davies-Bouldin index

• Regression and Evaluation of Regression Methods

  • Introduction to regression analysis
  • Linear regression: assumptions, interpretation, and model evaluation
  • Evaluation metrics for regression: mean squared error, R-squared, etc.
  • Other regression methods: polynomial regression, ridge regression, lasso regression.

• Introduction to NLP and Text Normalization

  • Overview of Natural Language Processing (NLP)
  • Techniques for text normalization: lowercasing, punctuation removal, etc.

• Text Representation and Tokenization

  • Introduction to vectors in NLP: Bag of Words and Count Vectorizer
  • Basics of tokenization and stopword removal

• Stemming, Lemmatization, and N-gram Language Models

  • Understanding and applying stemming and lemmatization
  • Introduction to N-gram language models

• Markov Models and Language Model Evaluation

  • Basics of Markov models in NLP
  • Techniques for evaluating language models: probability smoothing and performance metrics

• Text Classification Fundamentals

  • Overview of Text Classification
  • Introduction to Naive Bayes and Sentiment Classification

• Advanced Classifiers and Vector Semantics

  • Generative vs. discriminative Classifiers
  • Understanding vector semantics and embeddings: TF-IDF theory and vector similarity

• Neural Word Embeddings and Sequence Models

  • Introduction to neural word embeddings: Word2Vec and GloVe
  • Exploring sequence of words in NLP tasks

• Transformers and Large Language Models

  • Overview of transformers and their impact on NLP
  • Introduction to large language models (LLMs) and their applications.

• Introduction to Deep Learning

  • Overview of deep learning, its importance in computer vision, key concepts, and architectures.
  • Code along session for building Deep Neural Network from scratch

• Deep Learning Hyperparameter Tuning

  • Strategies for optimizing hyperparameters like learning rate, batch size, and regularization to improve model performance.

• Introduction to Convolutional Neural Networks (CNNs)

  • Explanation of CNNs, their architecture, and their role in image processing.
  • Code along session on Convolutional Neural Networks

• Building Custom Image Classification Models

  • Step-by-step guide to creating and training a custom image classifier using a CNN.

• Transfer Learning and Introduction to Object Detection

  • Introduction to transfer learning, its applications, and an overview of object detection techniques.

• Hands-on with YOLO Object Detection

  • Practical session on using the YOLO (You Only Look Once) algorithm for object detection.

• Custom Training YOLO model

  • Detailed guidance on training a YOLO model with a custom dataset for specific object detection tasks.

• Using State-of-the-Art Models for Real-World Applications

  • Exploring and implementing advanced models in computer vision for practical use cases.

• Introduction to OpenCV

  • Introduction to OpenCV, its libraries, and its importance in computer vision tasks.

• Image Pre-processing and Pre-build Algorithms in OpenCV

  • Hands-on session on image pre-processing techniques and using built-in algorithms in OpenCV.

• Advance guided project with OpenCV

  • Capstone project where participants apply learned techniques in a guided project using OpenCV.

• Introduction to MLOps and AI/NLP Fundamentals

  • Overview of MLOps and its importance in the AI lifecycle
  • Current trends in AI
  • Setting up the development environment

• Deep Dive into Machine Learning Models for NLP

  • Understanding NLP models (llama2, GPT, Mistral, etc.)
  • Introduction to Hugging Face Transformers and Datasets
  • Hands-on: Building a simple NLP model with Hugging Face

• Introduction to FastAPI for ML Model Deployment

  • Basics of API development with FastAPI
  • Deploying a simple ML model with FastAPI
  • Hands-on: Creating your first ML API with FastAPI

• Advanced FastAPI Features for Production-Ready APIs

  • Authentication and authorization in FastAPI
  • Hands-on: Enhancing your ML API with advanced features

• Introduction to Docker for AI Applications

  • Basics of Docker and containerization
  • Building Docker images for AI/ML applications
  • Hands-on: Containerizing your FastAPI application

• Leveraging Lang Chain and LangSmith for Enhanced NLP Applications

  • Introduction to Lang Chain and its Ecosystem
  • Overview of LangSmith for debugging, testing, evaluating, and monitoring LLM applications
  • Hands-on: Integrating Lang Chain with your NLP models and using LangSmith for enhanced capabilities

• Advanced Model Deployment with Hugging Face and Lang Chain

  • Integrating Hugging Face models for advanced NLP capabilities
  • Exploring Lang Chain for building complex NLP applications
  • Hands-on: Deploying a Hugging Face model via FastAPI with LangSmith integration

• Deploying ML Models on Google Cloud

  • Overview of Google Cloud Platform (GCP) for ML
  • Introduction to Google Cloud Run
  • Hands-on: Deploying your Dockerized FastAPI application on GCP with LangSmith monitoring.

• Email Writing

  • Basics of Professional Email Communication: Structure, tone, and etiquette.
  • Writing Effective Subject Lines: Techniques to ensure your emails are opened.
  • Emails for Networking: Approaching professionals and mentors in data science/AI.
  • Follow-up Emails: Strategies for following up without being intrusive.

• Report Writing + Presentations

  • Structure of a Data Science Report: Elements including abstract, methodology, results, and conclusion.
  • Visualizing Data: Incorporating charts, graphs, and other visual tools to enhance comprehension.
  • Creating Engaging Presentations: Tips for PowerPoint, storytelling, and engaging your audience.
  • Presentation Skills: Delivering your message confidently, handling Q&A sessions.

• LinkedIn Optimization

  • Building a Professional Profile: Key components of a LinkedIn profile for data science/AI professionals.
  • Networking Strategies: Connecting with industry professionals and joining relevant groups.
  • Content Sharing and Creation: Establishing thought leadership by sharing insights, articles, and engaging with community content.

• Resume/CV Writing

  • Tailoring Your Resume for Data Science/AI: Highlighting relevant skills, projects, and experiences.
  • Action Verbs and Quantifiable Achievements: Demonstrating impact in previous roles or projects.
  • Design and Layout: Making your resume/CV visually appealing and easy to read.

• Cover Letter

  • Structure of a Cover Letter: Introduction, body, and closing.
  • Customizing Your Message: Researching the company and role to personalize content.
  • Highlighting Fit and Value: Articulating how your skills and experiences align with the job requirements.

• Freelancing

  • Getting Started with Freelancing: Platforms for data science/AI freelancers, setting up a profile.
  • Finding Projects and Clients: Strategies to secure freelance work and build a portfolio.
  • Pricing Your Services: Understanding market rates and value-based pricing.
  • Client Management: Communicating effectively and managing expectations.

• Kaggle for Data Science

  • Introduction to Kaggle: Overview of the platform, competitions, datasets, and notebooks.
  • Participating in Competitions: Tips for success, collaboration, and learning from the community.
  • Building a Portfolio: Using Kaggle to showcase your skills and projects to potential employers.

• GitHub

  • Why GitHub for Data Scientists: Importance of version control and code sharing.
  • Creating and Managing Repositories: Best practices for organizing and documenting projects.
  • Collaborating on Projects: Contributing to open-source projects and collaborating with others.
  • GitHub as a Portfolio: Presenting your work and contributions to potential employers.

• How to Crack Data Science Interviews

  • Understanding the Interview Process: Types of interviews (technical, behavioral, case studies).
  • Preparing for Technical Interviews: Common questions, coding challenges, and statistical questions.
  • Behavioral Interview Preparation: Crafting your story, STAR method for responses.
  • Mock Interviews: Practicing with peers or mentors to gain confidence.

• Global Market Understanding

  • Data Science/AI Trends: Understanding global trends and emerging technologies.
  • Cultural Competence: Working in multicultural teams and serving diverse user bases.
  • Regulatory Environment: Overview of data privacy laws and ethical considerations in different regions.

• AI Product Development

  • From Idea to Product: Ideation, validation, and development processes.
  • User-Centric Design: Incorporating user feedback and UX/UI principles.
  • Product Management for AI: Unique challenges in managing AI projects, iteration, and deployment.
  • Metrics and Performance: Evaluating the success and impact of AI products.

• Storytelling Using Data

  • Principles of Data Storytelling: Crafting narratives that resonate with your audience.
  • Visual Narrative Techniques: Using data visualizations effectively in your story.
  • Engaging Presentations: Combining data, visuals, and narrative for impactful presentations.

• Intro to Data Commons

  • Understanding Data Commons: Concept, importance, and examples.
  • Accessing and Contributing to Data Commons: Guidelines and best practices.
  • Leveraging Data Commons: How data scientists can use these resources for research and development.