Basic Data and Statistical Concepts

• Data Literacy
• Statistical Foundations
• Descriptive Statistics
• Statistical Inference and Sampling Techniques
• Regression Analysis

Data analysis using chatGPT 

• Basic Data and Statistical Concepts
• Data Cleaning, Preparation, and Management
• Data Processing

PowerBI 

• Importing data from diverse sources
• Creating a basic report with various visuals
• Data analysis, manipulation and filtering in Power BI
• Creating measures and calculated columns
• Filtering data in a report
• Using slicers, dynamic filtering of a report
• Introduction to DAX
• Using DAX to solve complex data problems
• Visualizing cross sections of data

SQL

• Introduction to SQL
• Filtering, Sorting, and Aggregating Data
• Table Joins and Case Statements
• Advanced Query Techniques
• Window Functions and Data Modification
• Creating New Tables
• Advanced Topics and Artificial Intelligence
•  Date Variables and Artificial Intelligence

Prompt engineering

• Basics of Prompt Engineering
• Definition and importance of prompt engineering.
• The relationship between prompts and AI outputs.
• Applications in Data Science and Machine Learning
• Case studies illustrating the impact of prompt engineering in these fields.

GIS

• Introduction of spatial analysis
•  Introduction of QGIS
• Creating shapefiles. (Point, line and polygon)
• Learning basic Cartography
• Learning basic vector analysis tools.
• Creating heatmaps
• Using Google maps to create maps in QGIS
• Introduction of Raster file
• Real-world examples of GIS in various industries and fields

Mathematical Foundation 

• Linear algebra 
• Probability
• Statistics

Installation

• Introduction to the Python programming language and its applications
• Setting up the Python environment: installation of Python and necessary libraries
• Configuring the development environment: IDEs, text editors, and Jupyter Notebook

Python Basics I

• Introduction to Python: history, features, and advantages
• Expressions and operators: arithmetic, assignment, comparison, and logical
• Understanding type() function and type inference
• Introduction to data structures: lists, tuples, and dictionaries

 Python Basics II

• Recap of Python basics
• Working with arithmetic operators: addition, subtraction, multiplication, division, modulus, and exponentiation
• Using comparison operators: equal to, not equal to, greater than, less than, etc.
• Logical operators: and, or, and not
• Exploring advanced data types: sets and strings manipulation.

Expressions, Conditional Statements & For Loop

• Evaluating expressions: operator precedence and associativity
• Introduction to conditional statements: if, elif, and else
• Executing code based on conditionals.
• Understanding the flow of control in conditional statements
• Iteration using the for loop: range(), iteration over lists, and strings.

While loop, Break and Continue Statements, and Nested Loops

• Working with while loop: syntax, conditions, and examples
• Combining loops and conditionals
• Using the break statement to exit loops prematurely.
• Utilizing the continue statement to skip iterations.
• Implementing nested loops for complex iterations

Functions

• Introduction to functions: purpose, advantages, and best practices
• Defining and calling user-defined functions
• Parameters and arguments: positional, keyword, and default values
• Return statement and function output.
• Variable scope and lifetime
• Function documentation and code readability

Exception Handling and File Handling

• Understanding exceptions: errors, exceptions, and exception hierarchy
• Handling exceptions using try-except blocks: handling specific exceptions, multiple exceptions, and else and finally clauses.
• Raising exceptions and creating custom exception classes
• File handling in Python: opening, reading, writing, and closing files.
• Working with different file modes and file objects

Python Modules: NumPy,Pandas and Matplotlib

• Introduction to the NumPy module: features and applications
• Working with multidimensional arrays: creation, indexing, slicing, and reshaping
• Performing element-wise operations: arithmetic, logical, and statistical
• Overview of the Matplotlib module: data visualization and plotting
• Customizing plots: line properties, markers, colors, labels, and legends

Advanced Topics

• Introduction to Kaggle platform: features and benefits
• Leveraging Kaggle for real-life datasets: data exploration, analysis, and visualization
• Introduction to machine learning modules on Kaggle: scikit-learn, TensorFlow, and PyTorch
• Overview of running machine learning experiments on Kaggle
• Resources for further learning and exploration

Introduction and Missing Value Analysis

• Introduction to Exploratory Data Analysis (EDA)
• Importance of EDA in data analysis
• Steps involved in EDA
• Handling missing values: identification, analysis, and treatment strategies • Imputation techniques for missing values

Data Consistency, Binning, and Outlier Analysis

• Data consistency checks using fuzzy logic
• Binning and discretization techniques for continuous variables
• Outlier detection and analysis methods
• Handling outliers: techniques for treatment or removal

 Feature Selection and Data Wrangling

• Importance of feature selection in EDA
• Feature selection techniques: filter methods, wrapper methods, and embedded methods
• Data wrangling: cleaning and transforming data for analysis
• Handling categorical variables: encoding techniques

Inference, Hypothesis Testing, and Visualization

• Inference and hypothesis testing in EDA
• Common statistical tests: t-test, chi-square test, ANOVA, etc.
• Visualization techniques for EDA: histograms, box plots, scatter plots, etc. 
• Hands-on  practical session for complete EDA using a dataset

Machine Learning Performance Metrics and Naive Bayes

• Evaluation metrics for classification problems: accuracy, precision, recall, F1 score, etc.
• Introduction to Naive Bayes algorithm and its applications
• Implementing Naive Bayes for classification tasks

 Logistic Regression, SVM, Decision Trees, and Random Forests

• Logistic Regression: theory, interpretation, and applications
• Support Vector Machines (SVM): concepts, kernels, and use cases
• Decision Trees: construction, pruning, and interpretability
• Random Forests: ensemble learning and feature importance
• Bagging and Boosting: techniques for improving model performance

Hyperparameter Tuning, PCA, and SVD

• Hyperparameter tuning techniques: grid search, random search, and Bayesian optimization
• Principal Component Analysis (PCA): dimensionality reduction and feature extraction
• Singular Value Decomposition (SVD): applications in matrix factorization and data compression

Clustering Introduction, Partitioning Algorithms, and Cluster Evaluation

• Introduction to clustering: unsupervised learning technique
• Partitioning algorithms: K-means, K-medoids
• Hierarchical clustering: agglomerative and divisive approaches
• Density-based clustering: DBSCAN, OPTICS
• Cluster evaluation metrics: silhouette coefficient, Davies-Bouldin index

Regression and Evaluation of Regression Methods

• Introduction to regression analysis
• Linear regression: assumptions, interpretation, and model evaluation • Evaluation metrics for regression: mean squared error, R-squared, etc.
• Other regression methods: polynomial regression, ridge regression, lasso regression

Introduction to Time Series and Time Series Forecasting 

• Concepts and characteristics of time series data
• Time series components: trend, seasonality, and noise
• Popular time series forecasting models: ARIMA, SARIMA, exponential smoothing • Implementing time series forecasting models

Models and Hyperparameter Tuning

• Evaluation metrics for time series forecasting: mean absolute error, mean absolute percentage error, etc.
• Cross-validation techniques for time series data
• Hyperparameter tuning for time series models

Introduction to Natural Language Processing (NLP) and Large Language Models (LLMs)

• Overview of Natural Language Processing (NLP)
• Evolution of Large Language Models (LLMs)
• Importance and Applications of NLP and LLMs

Fundamentals of NLP

• Linguistic Concepts
• Tokenization and Text Preprocessing
• Part-of-Speech (POS) Tagging
• Named Entity Recognition (NER)
• Sentiment Analysis
• Text Classification
• Word Embeddings and Language Representations

Introduction to Large Language Models

• The Transformer Architecture
• Attention Mechanisms
• GPT, BERT, and Other Key Models
• Pretraining and Fine-Tuning Techniques
• Evaluation Metrics and Benchmarks

Practical Applications of NLP and LLMs

• Chatbots and Conversational AI
• Text Summarization
• Machine Translation
• Content Generation and Creative Writing
• Question Answering Systems
• Semantic Search and Text Mining

Ethical Considerations and Challenges

• Bias and Fairness
• Privacy and Security
• Model Interpretability and Explainability
• Environmental Impact and Computational Requirements

 Hands-On Exercises

• Getting Started with NLP Libraries (spaCy, NLTK, Hugging Face Transformers)
• Building a Simple Text Classifier
• Fine-Tuning a Large Language Model for a Specific Task
• Evaluating Model Performance and Error Analysis

Future Trends and Opportunities in NLP and LLMs

• Multimodal Models and Human-AI Interaction
• Low-Resource Languages and Transfer Learning
• Knowledge-Enhanced Language Models
• Efficient Training and Deployment Techniques

Computer Vision

• Cascade and HOG classifiers to detect faces
• Face detection using OpenCV and Dlib library
• Detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
• KCF and CSRT algorithms to perform object tracking
• convolutional neural networks and implement them using Python and TensorFlow
• Detect objects in images in videos using YOLO, one of the most powerful algorithms today
• Recognize gestures and actions in videos using OpenCV
• Create hallucinogenic images with Deep Dream
• Create images that don’t exist in the real world with GANs (Generative Adversarial Networks)

Reinforcement Learning

• Fundamentals of Reinforcement Learning
• Sample-based Learning Methods
• Prediction and Control with Function Approximation

Stable Diffusion Models

• Fundamentals of Diffusion Models
• Stable Diffusion in Practice
• Methods, Jobs and Tools of Stable Diffusion

Machine Learning Operations(MLOps)

• Github Actions
• Airflow
• Kubernetes
• MLFlow
• ML System Design
• API Building (Flask/FastAPI)
• Cloud Services (AWS/Azure)
• WandB

-10+ projects in 6 months

-International speakers and mentors for guided projects

-Industry level data sets and projects 

-Continuous practice with real world case studies with data analytics

-Skills demonstration on data cleaning, data analysis, data visualization

-Email writing

-Logic and critical thinking

-Reporting writing

-LinkedIn optimisation

-Presentation and visual communication

-Resume,CV and cover letter writing

-Acing interviews

-Personal branding

-Global market understanding

-One on one mentorship