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

Overview

• Introduction to Data Warehousing
• Definition and purpose
• Key concepts: OLTP vs. OLAP
• What is ETL?
• Overview of Microsoft Tools for Data Warehousing
• Introduction to Excel, SQL Server, SSIS, and SSRS

Data Warehousing Terminology and Concepts

• Understanding Data Warehousing Terminology
• Fact tables, dimension tables, star and snowflake schemas
• Exploring the differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing)
• Use cases and examples

Installing and Setting Up SQL Server and SSMS

• Installation of Microsoft SQL Server and SQL Server Management Studio (SSMS)
• Guided installation process
• Overview of SSMS interface and features
• Introduction to SQL Server databases
• Creating and managing databases in SSMS

Introduction to Microsoft Business Intelligence Tools

• Installing and using SSIS (SQL Server Integration Services) and SSRS (SQL Server Reporting Services)
• Overview of SSIS and SSRS interfaces
• Exploring the capabilities of each tool
• Basic concepts of ETL (Extract, Transform, Load) with SSIS

Data Extraction and Transformation with SSIS

• Introduction to different flat file formats (CSV, TXT, Excel)
• Data extraction using SSIS
• Connecting to various data sources
• Extracting data from flat files and databases
• Data cleaning and transformation using SSIS
• Applying basic transformations (Data Conversion, Conditional Split, etc.)
• Handling common data quality issues

Data Preparation with Microsoft Excel

• Cleaning CSV files using Microsoft Excel
• Removing duplicates, handling missing values, data formatting
• Preparing data for loading into SQL Server
• Validating data and exporting it for SSIS processes

Building ETL Workflows with SSIS

• Creating efficient ETL workflows with SSIS
• Designing ETL packages
• Avoiding common errors in ETL processes
• Best practices for ETL process optimization
• Error handling, logging, and performance tuning

SQL-Based Data Analysis and Reporting with SSRS

• Introduction to SSRS and report creation
• Overview of report types and their purposes
• Designing and building basic reports
• Practicing data analysis with SQL queries in SSRS
• Writing SQL queries to retrieve and analyze data
• Integrating SQL queries with SSRS reports

Advanced Data Transformation Techniques with SSIS

• Implementing complex transformations in SSIS
• Using advanced SSIS components (Lookups, Merge Joins, etc.)
• Data enrichment and aggregation techniques

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.