Syllabus
- Syllabus
Course Title: Data Science with GenAI using Python
Course Duration: 12 - 14 Weeks
Course Overview:
This course provides an in-depth understanding of data science fundamentals, machine learning techniques, and the integration of generative AI models using Python. It covers data preprocessing, visualization, statistical analysis, supervised and unsupervised learning, deep learning, and practical applications of generative AI models like GPT, Stable Diffusion, and GANs.
Course Eligibility:
- Freshers
- Any Engineers
- Any Graduates
- Any Post-Graduates
- Working Professionals
Syllabus Summary:
- Basic Python
- Advance Python
- MySQL
- MongoDB
- Power BI
- Concept of Big Data Hadoop
- Statistics & Prob
- AI, ML, DL, NLP
- Ethics in AI
- Gen AI
- Project 5
- Overview of Data Science
- Python Basics: Variable, Data Type, Conditional Statement, Control Flow, Built-in Functions
- Functions, Lambda, Exception Handling, Object Oriented Programming
- NumPy
- Python Pandas (Filtering, Sorting, Grouping, Merging, Deleting)
- Matplotlib
SELECT, WHERE, AND, OR, NOT, ORDER BY, INSERT INTO, NULL Values, UPDATE, DELETE, LIMIT, MIN and MAX, COUNT, AVG, SUM, LIKE, Wildcards, IN, BETWEEN, Aliases, Joins, INNER JOIN, LEFT JOIN, RIGHT JOIN, CROSS JOIN, Self Join, UNION, GROUP BY, HAVING, EXISTS, ANY, ALL, INSERT SELECT, CASE, Null Functions, Comments, Operators,
Create Table, Drop Table, Alter Table, Constraints, Not Null, Unique, Primary Key, Foreign Key, Check, Default, Create Index, Auto Increment, Dates, Views
Create Table, Drop Table, Alter Table, Constraints, Not Null, Unique, Primary Key, Foreign Key, Check, Default, Create Index, Auto Increment, Dates, Views
MongoDB Query API, Create DB, Collection, Insert, Find, Update, Delete, Query Operators, Update Operators, Aggregations, Indexing/Search, Validation, Data API, Drivers, Node.js Driver, Charts
- Data Visualisation
- Wireframes in Dashboard
- Designing a chart
- Effective Data Visualisation
- Data Visualisation with Power BI
- Stories about your Data
- Developing presentations and slideshows
- Importance of Statistics & Descriptive Statistics
- Data and Types
- Frequency Distribution Measures of Central Tendency
- Measures of dispersion
- Coefficient of Variation
- Random Variable (RV)
- Testing of Hypothesis
- Type I Error & Type II Error
- T-test
- Chi-Square test
- ANOVA, ANCOVA, MANOVA
- Regression Analysis
- Factor Analysis & Cluster Analysis
- Forecasting Methods (Time Series Analysis)
- Data Preprocessing
- Supervised Learning | Unsupervised Learning | Deep Learning
- Natural Language Processing
- Introduction to Generative AI
- Common GenAI models & Natural Language Models
- Industrial Applications
- GenAI Application Landscape
- GANs & VAE and Anomaly Detection
- Future Predictions
- Understanding How Chat AI Works
- Generative AI and Ethics & Ethical Concerns with AI
- Developing the skills of Ethical Analysis in AI
- Understanding Ethical AI Framework
- Framework in Real World Situation
- Prepare to address ethics in AI
