Introduction to BI & Power BI Basics

• Understanding Business Intelligence: definition, goals, and its role in Data Science & AI

• Power BI Overview: architecture, components, and ecosystem

• Introduction to the Power BI interface: reports, datasets, dashboards

• Connecting to data sources: CSV, Excel, mySQL

• Basic data loading and shaping in Power Query

• Exploring the Power BI workspace: fields, filters, visuals

Data Transformation & Modeling in Power BI

• Using Power Query Editor: cleaning, transforming, and merging data

• Common transformation steps: remove columns, rename, pivot/unpivot

• Introduction to data modeling: star schema vs. snowflake schema

• Creating and managing relationships between tables

• Understanding primary keys and foreign keys in Power BI context

• Role of data models in BI dashboards

DAX and Visualizations

• Introduction to DAX (Data Analysis Expressions): calculated columns vs measures

• Key DAX functions: SUM, AVERAGE, COUNTROWS, CALCULATE, FILTER

• Building interactive visuals: bar charts, line charts, maps, cards

• Using slicers and filters to add interactivity

• Drillthrough, bookmarks, and report navigation
• Publishing reports and dashboards to Power BI Service

SQL Basics for BI and Analytics

• Introduction to relational databases and SQL syntax

• Writing basic queries using SELECT, WHERE, ORDER BY

• Aggregating data using GROUP BY and aggregate functions (SUM, AVG, COUNT)

• Combining data using INNER JOIN, LEFT JOIN, and RIGHT JOIN

• Working with NULLs and aliases

• Filtering and sorting data for analysis

Advanced SQL for Analytical Workflows

• Subqueries and Common Table Expressions (CTEs)

• Using CASE statements for conditional logic

• Window functions: RANK, DENSE_RANK, ROW_NUMBER, LAG, LEAD

• Performing cohort and retention analysis with SQL

• Creating views for reusable queries
• Real-world business problems solved using SQL

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

• 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

Mathematical Concepts

• Linear Algebra:

• • • Vectors, matrices, operations, and their applications in data science.
    • Introduction to numpy for matrix operations.

• Introduction to numpy:

• • Why numpy is the preferred library for numerical operations in Python.
  • Basic numpy operations: creating arrays, array indexing, array slicing.
  • Matrix operations using numpy: matrix multiplication, finding determinants, solving linear equations.

Probability and Statistics

• Probability Theory:

• • • Basic probability, rules, and distributions (normal, binomial, Poisson).
    • Random variables, expectations, and variance.

• Descriptive Statistics:

• • • Measures of central tendency (mean, median, mode).
    • Measures of variability (range, variance, standard deviation)

• Introduction to pandas and scipy

• • Using pandas for data manipulation and summary statistics.
  • scipy for performing hypothesis tests and building confidence intervals.
  • Visualization with seaborn and matplotlib to understand data distributions and relationships.

Probability and Statistics II

• Inferential Statistics:
  • Sampling distributions and the Central Limit Theorem.
  • Confidence intervals and hypothesis testing.
• Correlation and Regression:
  • Scatter plots, correlation coefficients.
  • Simple linear regression analysis.
• Introduction to Seaborn and Matplotlib
  • Visualization of correlation matrices and scatter plots to identify relationships between variables.
  • Advanced plotting techniques such as pair plots, heatmaps, and regression plots to extract deeper insights from data.

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

Overview of Machine Learning

• Supervised, unsupervised, and reinforcement learning.
• Machine learning workflow from data collection to model deployment.
• Introduction to Python libraries essential for ML (Scikit-learn, TensorFlow, PyTorch)

Supervised learning and Linear Regression

• Linear Regression: Concept, loss function, evaluation metrics (MSE, R²)
• Building a Linear Regression model in scikit-learn
• Visualizing predictions vs actuals

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 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.

Conceptual Overview 

• Understand the distinct roles and responsibilities of data scientists and data engineers within projects. 

Data Sourcing with Python 

• Extracting data from sources like PostgreSQL, MongoDB, APIs, web scraping, and IoT devices.

Databricks Tour 

• Get introduced to Databricks as a platform for big data processing and machine learning.

Big Data Handling for Data Science 

• Process large-scale datasets using Apache Spark within a distributed environment. 

MLflow 

• Learn to track experiments and ensure reproducibility in machine learning workflows

Introduction to Convolutional Neural Networks (CNNs)

• Explanation of CNNs, their architecture, and their role in image processing.
• Codel 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 Students apply learned techniques in a guided project using OpenCV.

Introduction to NLP and Text Normalization

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

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
• Introduction to vectors in NLP: Bag of Words, Count Vectorizer, and TF-IDF

  Markov Models and Language Model Evaluation

• Basics of Markov models in NLP
• Overview of Text Classification
• Techniques for evaluating language models: probability smoothing and performance metrics
• Introduction to Naive Bayes and Sentiment Classification

Advanced Classifiers and Vector Semantics

• Generative vs. discriminative classifiers
• Understanding vector semantics and embeddings
• Introduction to neural word embeddings: Word2Vec and GloVe

Deep Learning for NLP and Sequence Models

• Overview of deep learning techniques for NLP
• Applying supervised and unsupervised learning methods to NLP tasks
• Exploring sequence of words in NLP tasks

Transformers and Large Language Models

• Understanding the architecture and mechanisms of transformers
• Overview of large language models (LLMs) and their capabilities

Training and Fine-Tuning LLMs

• Techniques for training large language models
• Fine-tuning pre-trained LLMs for specific tasks

Introduction to Generative AI and Multimodal Models

• Overview of Generative AI and its impact on various industries.
• Understanding multimodal models and how they differ from single-modality models.
• Exploring the OpenAI API: Capabilities and applications for text, speech, and image-based AI.

Speech Synthesis and Conversational Agents

• Objective: Dive into text-to-speech (TTS) and speech-to-text (STT) models and explore conversational AI with agent frameworks.
• Overview of text and voice synthesis technologies.
• Introduction to Eleven Labs and Heygen: Creating high-quality audio and speech synthesis.
• Using OpenAI API for speech synthesis and understanding its capabilities

Advanced Agent Development with LangChain

• Basics of LangChain and its role in agent-based AI.
• Building, customizing, and deploying agents using LangChain.
• Exploring integration of LangChain with other APIs for multimodal interactions, including OpenAI API for image and text generation.

Building Complex Agent-Based Applications with CrewaI, Synthesia, and OpenAI

• Introduction to CrewaI: Building intelligent agents for various tasks.
• Synthesia for video and visual content generation in AI-driven applications.
• Using OpenAI API to generate multimodal content (text, images, and speech).
• Building a final project: Integrate multiple APIs to create a multimodal generative application.  

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 (llama, 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

Docker Image Creation and Application Containerization

• Introduction to Docker and its role in application deployment
• Hands-on: Creating Docker images for NLP applications
• Converting existing applications into Docker containers for scalable deployment

 LangGraph and Advanced Model Integration

• Introduction to LangGraph for building graph-based NLP Applications
• Exploring advanced integration scenarios with LangGraph and LangChain
• Hands-on: Designing graph-based workflows for NLP tasks

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.

1. 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.

1. 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.

1. 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.

1. Freelancing Series

• 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.

1. 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.

1. 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.

1. How to Crack AI  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.

1. 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.

1. 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.

1. Intro to Data Commons

• Understanding Data Commons: Concept, importance, and examples.
• Accessing and Contributing to Data Commons: Guidelines and best practices.

SQL for Data Handling

• Objective:
  • Students need to acquire basic to intermediate SQL skills to effectively manage and query databases, which are crucial for data science.
• Self-Learning Resources:
  • Provide a list of recommended online courses, tutorials, and platforms where they can learn SQL. Examples include Khan Academy, Codecademy, or Coursera.
  • Suggest specific SQL topics to cover, such as SELECT statements, joins, subqueries, and aggregation functions.
• Assessment:
  • Require Students to complete a practical SQL project or pass an assessment by the end of the first two months to demonstrate their proficiency.
    
    

AWS Cloud Services

• Objective:
  • Students need to gain foundational knowledge in AWS, focusing on services that are commonly used in deploying machine learning models and handling large datasets.
• Learning Modules:
  • Introduction to AWS: Overview of the cloud platform, setting up an AWS account, and understanding key services like EC2, S3, RDS, and Lambda.
  • AWS for Machine Learning: Using Amazon SageMaker for deploying machine learning models, leveraging AWS Glue for data preparation, and understanding the role of AWS IAM in managing access and security.
• Hands-On Practice:
  • Organize workshops or labs where Students can practice setting up environments, deploying simple applications, and using AWS for data storage and machine learning tasks.
• Final Assessment:
  • Incorporate a capstone project that requires using AWS to deploy a machine learning model they developed earlier in the bootcamp. Assess their ability to integrate AWS services with their data science projects effectively.