Data Science and AI Bootcamp - Cohort 7

This repository contains materials and resources for the Data Science and AI Bootcamp, Cohort 7. The bootcamp is designed to equip participants with essential skills in Data Science, Artificial Intelligence, and related domains. Below is a summary of the curriculum and key topics covered throughout the course.

Curriculum Overview

Basics of Data

• Introduction to Data Literacy: Understanding data fundamentals and its significance.
• Data Collection and Sources: Methods of gathering and sourcing data.
• Data Representation Techniques: Various ways to represent and visualize data.
• Data Ethics and Privacy: Ensuring ethical practices and safeguarding data privacy.
• Real-world Applications: Practical applications of data literacy in various industries.

Module: Data Communication

• Introduction to Data Communication: Principles of conveying data-driven insights.
• Data Interpretation: Techniques for interpreting data effectively.
• Storytelling with Data: Crafting compelling narratives using data.
• Best Practices and Presentation Skills: Strategies for effective data presentation.
• Data Storyboarding and Feedback: Planning and refining data-driven stories.

Module: Introduction to Data Analysis using Excel Foundation

• Statistical Foundations: Basic concepts in statistics, including descriptive statistics and regression analysis.
• Data Cleaning and Preparation: Techniques for cleaning, preparing, and managing data.
• Advanced Excel Functions: Using advanced functions like Pivot Tables for data analysis.

Module: Data Visualization and Analysis using PowerBI

• Introduction to PowerBI: Connecting, shaping, and modeling data in PowerBI.
• Data Visualization: Best practices for creating visualizations and dashboards.
• DAX and Data Modeling: Understanding DAX and the principles of data modeling.

Module: Database Management with SQL

• SQL Basics: Introduction to SQL syntax, structure, and database management systems.
• Advanced Query Techniques: Working with joins, subqueries, and window functions.
• Data Modification and Creation: Techniques for altering and creating tables in SQL.

Module: Spatial Data Analysis using GIS

• Introduction to Spatial Analysis: Understanding and working with spatial data using QGIS.
• Shapefiles and Cartography: Creating and editing shapefiles, and learning basic cartography.
• Advanced Spatial Analysis: Performing complex spatial queries, buffer analysis, and creating heatmaps.

Module: Data to Insights - Business Intelligence

• Introduction to Business Intelligence: Concepts and importance of BI in decision-making.
• Analytical Reporting: Creating insightful reports and using BI tools like Tableau and PowerBI.
• Decision Making: Leveraging BI for informed business decisions.

Module: Python for Data Science

• Python Basics: Introduction to Python, including data structures and basic operations.
• Advanced Python: Working with libraries like NumPy and Matplotlib for data analysis.
• File and Exception Handling: Techniques for managing files and handling exceptions in Python.

Module: Exploratory Data Analysis (EDA) and Machine Learning

• EDA Fundamentals: Conducting exploratory data analysis and handling missing values.
• Machine Learning Basics: Introduction to key machine learning algorithms and performance metrics.
• Advanced ML Techniques: Hyperparameter tuning, PCA, and clustering methods.

Module: Deep Learning and Computer Vision

• Deep Learning Basics: Introduction to neural networks and Keras.
• Computer Vision: Working with CNNs, transfer learning, and object detection using YOLO.

Module: NLP, LLMs, and MLOps

• Natural Language Processing: Techniques for text processing, tokenization, and classification.
• Large Language Models: Understanding and fine-tuning transformers and LLMs.
• MLOps: Deploying and managing machine learning models using tools like FastAPI and Docker.

Module: Low-Code and No-Code Tools for Data Science and AI

• Low-Code Platforms: Introduction to tools like PyCaret and TPOT for AutoML.
• No-Code Development: Building chatbots and web applications without writing code.

Professional Development Series

• Email and Report Writing: Mastering professional communication and documentation.
• LinkedIn and Resume Optimization: Enhancing your professional profile and job applications.
• GitHub for Data Scientists: Using GitHub for version control and portfolio building.
• Freelancing and Kaggle: Strategies for starting a freelance career and participating in Kaggle competitions.

Conclusion

This bootcamp covers a wide array of topics essential for becoming proficient in Data Science and AI. Participants will gain hands-on experience with tools and techniques used by professionals in the industry.