Introduction to Data Analytics

• Overview of data analysis, the role of a data analyst.
• Types of data (structured, unstructured, and semi-structured).
• Business and real-world applications of data analysis.

Data Collection & Preparation

• Data sourcing: How to collect data from APIs, databases, and external sources.
• Data cleaning: Handling missing values, outlier removal, and data preprocessing.
• Exploratory Data Analysis (EDA): Descriptive statistics, data summarization techniques.

Working with Data in Excel

• Data manipulation using Excel.
• Creating pivot tables, charts, and using formulas for analysis.

SQL for Data Analysts

• Basics of SQL: Queries, joins, and aggregations.
• Advanced SQL: Subqueries, window functions, and common table expressions (CTEs).
• Data extraction and manipulation using SQL.

Introduction to Data Visualization

• Importance of data visualization in analysis.
• Types of visualizations and when to use them.

Data Visualization Tools

• Matplotlib and Seaborn: Python libraries for creating static, informative plots.
• Power BI and Tableau: Tools for creating interactive dashboards and reports.

Effective Visualization Design

• Best practices for designing dashboards and data stories.
• Principles of storytelling with data.

Python Basics

• Variables, data types, loops, and conditionals.
• Functions and libraries used in data analysis.

Pandas for Data Manipulation

• Dataframes: Loading, cleaning, and transforming data.
• Aggregating, sorting, and filtering data with Pandas.

NumPy for Data Computation

• Working with arrays and performing mathematical operations on data.

Statistical Analysis in Python

• Descriptive statistics (mean, median, mode, standard deviation).
• Hypothesis testing, correlation, and regression analysis.

Introduction to Machine Learning

• Supervised vs. unsupervised learning.
• Types of algorithms (regression, classification, clustering).

Regression Analysis

• Linear regression, polynomial regression.
• Model evaluation metrics (R-squared, mean squared error).

Classification Techniques

• Decision trees, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).
• Model evaluation: Confusion matrix, precision, recall, F1-score.

Overview of Generative AI

• What is Generative AI?
• Applications in content creation, data generation, and automation.

Natural Language Processing (NLP) Basics

• Tokenization, sentiment analysis, text classification.
• Introduction to transformers and large language models.

Working with OpenAI GPT Models

• Understanding GPT models and their architecture.
• Implementing text generation, summarization, and translation using GPT APIs.

Generative Adversarial Networks (GANs)

• Introduction to GANs and how they work.
• Applications of GANs in generating images, data, and more.

Data Augmentation with Generative AI

• How generative models can create synthetic data for analysis.
• Using synthetic data to overcome data scarcity issues.

Automated Reporting with Generative AI

• Automating data report generation using GPT models.
• Generating insights from data and writing automated summaries.

Generative AI for Predictive Analytics

• Leveraging generative models for predictive data analysis.
• Applications in forecasting, market predictions, and trend analysis.