Advance Course in Data Analytics with AI

Business Statistics

Descriptive Statistics

• Data Types
• Measure of Central Tendency
• Measures of Dispersion
• Graphical Techniques
• Skewness & Kurtosis
• Box Plot

Probability

• Random Variable
• Probability Distribution
• Normal Distribution
• SND (Standard Normal Distribution)
• Expected Value

Inferential Statistics

• Sampling Funnel
• Sampling Variation
• Central Limit Theorem
• Confidence Interval
• Hypothesis Testing
  • 2 Proportion Test
  • 2 Sample t-Test
• ANOVA and Chi-Square

Basics of Excel

• Introduction to Excel
• Navigating the Excel Interface
• Basic Excel Functions and Formulas
• Data Entry Techniques
• Formatting Cells and Sheets
• Data Sorting and Filtering
• Introduction to PivotTables
• Conditional Formatting for Data Insights
• Excel Functions for Statistical Analysis
• Date and Time Functions
• Lookup Functions (VLOOKUP, HLOOKUP)

Fundamentals of AI

• Introduction to Artificial Intelligence
• History of AI
• AI Applications and Case Studies
• Ethics in AI
• Basic Machine Learning Concepts
• Supervised vs. Unsupervised Learning
• Neural Networks
• Deep Learning Fundamentals
• Natural Language Processing (NLP)
• Computer Vision Basics
• Robotics and AI
• AI in Business and Industry
• Future Trends in AI

Analytics in Python

• Introduction to Python
• Python Setup and Environment Configuration
• Basic Syntax and Variables
• Data Types in Python
• Control Structures (if-else, loops)
• Functions and Modules
• Exception Handling
• NumPy for Numerical Data
• Pandas for Data Manipulation
• Data Cleaning Techniques
• Introduction to Statistics with Python
• Basics of Data Gathering and Web Scraping

Database Management Systems

• Introduction to Databases
• Database Design Principles
• SQL Syntax and Query Fundamentals
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Control Language (DCL)
• Transaction Management
• Indexing and Optimization Techniques
• Normalization
• Entity-Relationship (ER) Model
• SQL Joins and Subqueries
• Stored Procedures and Functions
• Database Security
• Backup and Recovery Techniques

Exploratory Data Analysis

• Data Quality Assessment
• Handling Missing Values
• Data Filtering
• Outlier Detection
• Data Transformation
• Feature Scaling and Normalization
• Encoding Categorical Data
• Data Integration
• Date and Time Data Handling
• Dealing with Duplicate Data
• Using Regular Expressions for Data Cleaning
• Data Validation Techniques

Data Visualization using Power BI

• Introduction to Power BI
• Power BI Desktop Interface
• Data Importing and Transformation
• Data Modeling
• Creating Dashboards
• DAX Basics
• Visualization Techniques
• Publishing and Sharing Reports
• Power BI Service
• Data Refresh and Scheduling
• Integrating Power BI with Other Tools
• Security Features in Power BI
• Advanced DAX Functions

Machine Learning

• Introduction to Machine Learning
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Data Preprocessing
• Feature Engineering
• Regression Analysis
• Classification Techniques
• Decision Trees
• Random Forests
• Support Vector Machines
• Neural Networks
• Deep Learning
• Clustering Algorithms
• Dimensionality Reduction
• Ensemble Methods
• Model Evaluation and Validation
• Hyperparameter Tuning
• Machine Learning Pipelines

Advanced SQL

• Complex Queries and Subqueries
• Advanced JOIN Operations
• Window Functions
• Common Table Expressions (CTEs)
• Recursive Queries
• Indexing and Performance Tuning
• Stored Procedures and Functions
• Triggers and Events
• Dynamic SQL
• SQL Security Measures
• Transaction Management and Concurrency Control
• Handling Large Datasets
• SQL with JSON and XML
• Integrating SQL with Other Programming Languages

Time Series

• Introduction to Time Series Analysis
• Components of Time Series
• Time Series Data Collection and Preparation
• Moving Averages
• Exponential Smoothing
• ARIMA Models
• Seasonal Adjustments
• Trend Analysis
• Cyclical and Irregular Components
• Forecasting Techniques
• Model Diagnostics and Validation
• Spectral Analysis
• Multivariate Time Series Analysis

Data Analysis using R Programming

• Introduction to R
• R Studio Interface
• Data Structures in R (Vectors, Lists, Data Frames, Matrices)
• Data Import and Export in R
• Data Manipulation with dplyr
• Data Cleaning Techniques
• Exploratory Data Analysis in R
• Statistical Analysis with R
• Regression Analysis
• Classification Techniques
• Data Visualization with ggplot2

Advanced Machine Learning

• Ensemble Methods
• Neural Networks
• Unsupervised Learning
• Reinforcement Learning
• Advanced Regression Techniques
• Support Vector Machines
• Natural Language Processing
• Generative Models
• Graphical Models
• Advanced Optimization Techniques
• Model Evaluation and Hyperparameter Tuning
• Advanced Feature Engineering
• Model Interpretability and Explainability
• Deployment of Machine Learning Models

Deep Neural Network

• Introduction to Neural Networks
• Feedforward Neural Networks
• Backpropagation
• Activation Functions
• Loss Functions
• Optimization Algorithms
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory Networks (LSTMs)
• Autoencoders
• Generative Adversarial Networks (GANs)
• Deep Reinforcement Learning
• Transfer Learning
• Neural Network Regularization Techniques
• Hyperparameter Tuning

Data Visualization using Tableau

• Introduction to Tableau
• Tableau Interface and Features
• Connecting to Data Sources
• Data Preprocessing in Tableau
• Creating Basic Charts (Bar, Line, Pie)
• Advanced Chart Types (Heat Maps, Tree Maps, Scatter Plots)
• Dashboard Creation
• Interactive Dashboards
• Using Filters, Groups, and Sets
• Calculated Fields and Table Calculations
• Parameters
• Data Blending

Fundamentals of Cloud Computing

• Introduction to Cloud Computing
• Cloud Service Models (IaaS, PaaS, SaaS)
• Cloud Deployment Models (Public, Private, Hybrid)
• Virtualization Basics
• Cloud Infrastructure Components
• Cloud Security Principles
• Networking in the Cloud
• Storage Options in the Cloud
• Identity and Access Management (IAM)
• Cloud Migration Strategies
• Monitoring and Management in the Cloud

Generative AI and Prompt Engineering

• Introduction to Generative AI
• Basics of Natural Language Processing (NLP)
• Introduction to Prompt Engineering
• Pretrained Language Models (such as GPT, BERT)
• Text Generation Techniques
• Fine-Tuning Language Models
• Conditional Generation
• Unconditional Generation
• Sampling Strategies
• Diversity and Control in Generation
• Bias and Fairness in Generative AI

Big Data Engineering with Hadoop and Spark

• Introduction to Big Data and Distributed Computing
• Overview of Hadoop Ecosystem
• Hadoop Distributed File System (HDFS)
• MapReduce Programming Paradigm
• Hadoop Installation and Configuration
• Hadoop YARN Architecture
• Hadoop MapReduce Optimization Techniques
• Introduction to Apache Spark
• Spark RDDs (Resilient Distributed Datasets)
• Spark DataFrame and Dataset APIs
• Spark SQL for Data Processing
• Spark Streaming for Real-time Analytics
• Spark MLlib for Machine Learning
• Spark GraphX for Graph Processing
• Integration of Hadoop and Spark
• Performance Tuning and Optimization in Spark
• Best Practices for Big Data Engineering
• Real-world Use Cases of Hadoop and Spark

Computer Vision

• Introduction to Computer Vision
• Image Formation and Representation
• Image Filtering and Enhancement
• Edge Detection
• Feature Detection and Description
• Image Segmentation
• Object Detection and Recognition
• Deep Learning for Computer Vision
• Convolutional Neural Networks (CNNs)
• Transfer Learning in Computer Vision
• Semantic Segmentation
• Instance Segmentation
• Object Tracking
• Pose Estimation
• 3D Computer Vision
• Image Registration and Alignment
• Image Retrieval and Similarity Matching
• Face Recognition
• Biometric Systems
• Medical Image Analysis
• Applications of Computer Vision in Industry and Research

Natural Language Processing

• Introduction to Natural Language Processing
• Text Preprocessing Techniques (Tokenization, Stemming, Lemmatization)
• Part-of-Speech Tagging
• Named Entity Recognition
• Text Classification
• Sentiment Analysis
• Language Modeling
• Word Embeddings (Word2Vec, GloVe)
• Seq2Seq Models
• Attention Mechanisms
• Transformer Models
• Pretrained Language Models (BERT, GPT)
• Text Generation Techniques
• Machine Translation
• Question Answering Systems
• Text Summarization
• Coreference Resolution
• Dependency Parsing
• Discourse Analysis
• Ethical Considerations in NLP
• Real-world Applications of NLP

Advanced Cloud Computing – AWS/Azure

• Introduction to Cloud Computing
• Overview of AWS/Azure Services
• Virtual Machines (EC2 for AWS, VMs for Azure)
• Containerization (Docker, Kubernetes)
• Serverless Computing (AWS Lambda, Azure Functions)
• Networking in the Cloud (VPCs, Virtual Networks)
• Storage Options (S3, EBS for AWS; Blob Storage, Disk Storage for Azure)
• Database Services (RDS, DynamoDB for AWS; Azure SQL Database, Cosmos DB for Azure)
• Identity and Access Management (IAM, Azure Active Directory)
• Security Best Practices in the Cloud
• Monitoring and Logging (CloudWatch, CloudTrail for AWS; Azure Monitor, Log Analytics for Azure)
• DevOps and Continuous Integration/Continuous Deployment (CI/CD) in the Cloud
• Cost Management and Optimization Strategies
• Hybrid Cloud and Multi-Cloud Architectures
• Advanced Networking Features (Load Balancers, CDN)
• Machine Learning and AI Services (AWS SageMaker, Azure Machine Learning)
• Big Data and Analytics Services (AWS EMR, Azure HDInsight)
• IoT Services (AWS IoT Core, Azure IoT Hub)
• Blockchain Services (AWS Blockchain Templates, Azure Blockchain Service)
• Real-world Use Cases and Case Studies

Project Overview

• Objective: To apply data analytics and AI techniques to a real-world problem or dataset, demonstrating the ability to gather, analyze, interpret, and present data.
• Scope: Projects can be individual or group-based, focusing on industries like finance, healthcare, retail, or any sector relevant to the students’ interests or local economic needs.

Stages of the Project

Stage 1: Project Proposal

• Deliverables:
  • Project title and team members (if applicable)
  • A clear statement of the problem
  • Objectives and expected outcomes
  • Preliminary research and existing solutions

Stage 2: Data Collection and Preparation

• Deliverables:
  • Description of data sources
  • Data collection methodology
  • Data cleaning and preprocessing steps

Stage 3: Exploratory Data Analysis

• Deliverables:
  • Statistical summaries and visualizations of the data
  • Initial insights and hypotheses based on the data

Stage 4: Model Development

• Deliverables:
  • Selection of appropriate AI and machine learning models
  • Training models and tuning parameters
  • Validation and testing of models

Stage 5: Results and Interpretation

• Deliverables:
  • Detailed analysis of model results
  • Comparison with initial hypotheses and objectives
  • Discussion of the model’s effectiveness and limitations

Stage 6: Final Presentation and Report

• Deliverables:
  • Comprehensive report documenting all stages of the project
  • Presentation of the project outcomes to an audience which may include peers, faculty, and industry professionals

This structured approach ensures thorough exploration and application of data analytics and AI techniques throughout the project lifecycle.