– Scope and Significance of AI and Data Science Across Diverse Industries
– Distinctions between AI Engineers, Software Engineers, and Data Scientists
– Future in AI, Machine learning, and Data Science
– Generative AI, LLM (Large Language Models), and Image Generation

– Data Types & Operators, Control Structures – If-Elif Statement
– Control Structures – For Loop
– Control Structures – While loop
– String Functions and Operations

– Comprehensive Study of Lists and their Function
– Understanding Tuples and their Functionality
– Exploring Dictionary and its Functions
– Leveraging Sets for Unique Data Handling

– Mastering Python Functions and their Application
– Understanding Functional Arguments and their Implementations
– Learning Robust Error Handling Techniques
– Understanding Regular Expressions (Regex) for Pattern Matching

– Introduction to NumPy: Numeric Computing with Python
– Exploring NumPy Broadcasting for Efficient Array Operations
– Introduction to Pandas
– Pandas Functionality for Data Analysis and Manipulation

– Data Storytelling with Matplotlib
– Exploratory Data Analysis (EDA) Techniques and Approaches
– Exploring Databases, Different Models and Use Cases
– Understanding NoSQL Databases and MongoDB, and its Benefits in Data Analysis

– Introduction to Tableau and Data Visualization Techniques (Charts, Heat Maps, Tree Maps, and Box Plots)
– Interactive Dashboards, Compelling Data Stories, Blending and Joining
– Advanced Analytics and Forecasting (Trend Lines, Clustering, and Predictive Modeling etc.)
– Recap, Project, Assessment and Certification

– Probability and Types of Events
– Types of Statistics (Descriptive & Inferential); Types of Data (Qualitative, Qunatitative, & Outliers)
– Backpropagation
– Measure of Central Tendency – Mean, Mode, Median
– Measure of Spread – Range, Variance, Standard Deviation and IQR, Hypothesis Testing

– Introduction to Machine Learning
– Types of Machine Learning: Supervised, Unsupervised, and Reinforcement
– Linear Regression
– Logistic Regression

– Evaluation Metrics
– Decision Trees, Random Forests
– Support Vector Machines (SVM)
– Dimentionality Reduction using Principal Component Analysis (PCA)

– Core Principles of Generative AI, Prompt Engineering and ChatGPT
– Large Language Models (LLM)
– Generative Adversarial Networks (GANs)
– Recap, Project, Assessment and Certification

– Programming and Development Environments: Python
– Data Manipulation and Analysis: NumPy, and Pandas
– Data Visualization: Matplotlib, and Tableau
– Databases: MongoDB
– Scientific Computing: SciPy
– Machine Learning and Deep Learning: Scikit-Learn
– AI and Language Models: ChatGPT, and Prompt Engineering

– Soft Skills Development
– Networking strategies and building a professional online presence
– Address Specific Career Goals and Valuable Advice for Navigating the Job Market
– Professional Resume and Interview Preparation
– Job Assistance through Medh Placement Cell