Master Data Science with Generative AI

Overview of Data Analytics

1. Explanation of quantitative and qualitative data
2. Role of data analytics in decision-making processes
3. What does a typical data scientist do? What are realistic expectations?

Introduction to Artificial Intelligence

1. Differentiating AI, machine learning, and deep learning
2. Role of AI in predictive analytics and automation

Importance of GenAI Tools and Platforms

1. Specific tools like GPT (Generative Pre-trained Transformer), DALL-E
2. How these tools can be used to generate insights and automate tasks
3. From scratch to Gen AI – Roadmap

Data Collection Techniques and Sources

1. Standards for Data Collection, GIGO concept
2. Evaluating the reliability and validity of data sources

Data Cleaning and Preprocessing

1. Exploratory Data Analysis – Basic Statistics and Data Summarization
2. Detailed methods for handling outliers and missing data (imputation techniques)
3. Data Standardization, Dimensionality Reduction concepts

Introduction to Tools for Data Manipulation

1. Exploratory Data Analysis using Excel

Core Python Concepts with Hands on

1. Variables, Statements, Methods, Non Primitive Data Types, Loops, Functions

Descriptive Statistics and Exploratory Data Analysis

1. Using graphical summaries (box plots, histograms) to understand data

Inferential Statistics

1. Sampling, Significance testing, Extrapolation, Chi-Square testing

Probability Theory Basics

1. Basics of Probability and importance in Predictive Analytics

Principles of Effective Data Visualization

1. Cognitive load theory in visualization design
2. Color theory and its impact on data interpretation

Tools for Creating Dynamic Visualizations

1. Advanced functionalities in Tableau (parameters, calculations)
2. Creating and sharing interactive dashboards in Tableau,Power BI

Advanced Visualization Techniques

1. Dashboards and Story Telling

Overview of Machine Learning in Data Analytics

1. Discussion on bias, variance, and model overfitting
2. Comparative analysis of model performance metrics

Supervised vs Unsupervised Learning

1. Detailed case studies on clustering (customer segmentation) and classification (email spam detection)

Basics of Regression, Classification, and Clustering

1. Advanced regression techniques (multiple regression, logistic regression)
2. Model validation methods (cross-validation, ROC curves)
3. Decision trees: A deeper look into information gain and Gini index
4. Support vector machines: Understanding hyperplanes and kernel trick
5. Other algorithms such as Naïve Bayes, KNN, K means Clustering

What does AI mean? And how did we build current Ais?

1. Unstructured data, Image, Text and sound embedding
2. GIS data, QGIS demo

Natural Language Processing for Text Data Analysis

1. Hands-on projects on sentiment analysis using Python libraries (NLTK, spaCy)

Predictive Analytics with Advanced Machine Learning Models

1. Deep learning applications: Image recognition and natural language processing with Tensorflow and Pytorch

Case Studies from Various Industries

1. Reporting Automation
2. Generating Text, Images, using co-pilot, perplexity

Real-time Analytics Using AI

1. Streaming data and introduction to IoT applications

Ethical and Privacy Considerations

1. Discussing real-world scenarios of AI ethics breaches

Introduction to Big Data Technologies

1. Scalability challenges and solutions with big data

Big Data Analytics with AI

1. Practical use cases of AI in handling big data challenges

Understanding the Hadoop Ecosystem

1. In-depth look at Hadoop components and their interrelationships

Project Design and Implementation

1. Guidance on defining project scope and objectives

Comprehensive Analysis Using AI Tools

1. Integration of multiple AI techniques for holistic analysis

Presentation of Findings

1. Effective techniques for visual and verbal presentation of complex data