Business > Business Analytics & Intelligence
26h 1m
Free
Master Data Analysis: Python, SQL, & AI-Powered Insights
What you will learn:
- Advanced SQL techniques for efficient data retrieval
- Data analysis using Python and SQL with a real-world database
- Data visualization using Python libraries
- Web scraping and data extraction using Python
- Building chatbots with Python
- A/B testing using Python
- Machine learning concepts and applications
- Data preprocessing and cleaning techniques
- Time series analysis using Python
- Utilizing ChatGPT for data analysis tasks
Description
Unlock your data analysis potential with our comprehensive bootcamp! This course goes beyond the basics, teaching you Python programming, SQL database management, and leveraging AI tools like ChatGPT for enhanced efficiency. Dive into practical examples, real-world projects using a PostgreSQL database, and a vast array of coding challenges to solidify your skills. Master data visualization techniques, build effective models, and confidently tackle data analysis tasks in any environment.
What You Will Gain:
- Python Mastery: From foundational concepts to advanced techniques, including object-oriented programming, Pandas, NumPy, and data visualization using libraries like Matplotlib and Seaborn.
- SQL Expertise: Learn PostgreSQL from the ground up, performing complex queries, aggregations, joins, and date filtering. Efficiently extract, clean, and manipulate data from relational databases.
- Real-World Projects: Apply your skills to various datasets in hands-on projects, building your portfolio with compelling work for future employers. Examples include building chatbots, web scraping, and A/B testing.
- Jupyter Notebook Proficiency: Develop and execute code within Jupyter Notebooks, a critical tool used by professionals in the data analysis field.
- Industry-Standard Best Practices: Develop robust, efficient code using effective debugging, comprehensive commenting, and collaborative workflows.
- AI-Powered Assistance: Leverage ChatGPT to streamline your workflow, generate code suggestions, and improve your analytical processes.
Why Choose This Bootcamp?
The combination of Python and SQL is in high demand. This course will provide you with the practical skills to land high paying jobs at top companies like Google, Amazon and Facebook. You will build a strong foundation, whether you're a complete beginner or an intermediate analyst seeking to enhance your skillset. Start your data-driven career today!
Enroll Now and Transform Your Career!
Curriculum
Python Introduction
This section lays the groundwork for your Python journey. Lectures cover introductory topics like setting up your Python environment, understanding data types and structures, differences between Python 2 and 3, and a firm grasp of control flow using 'if', 'for', and 'while' loops, and advanced techniques like functions, recursion, decorators, lambda functions, and error handling. The final lecture integrates these concepts, providing a comprehensive overview of Python fundamentals.
Python Basics
Building upon the introduction, this section delves deeper into Python's core functionality. You'll master string manipulation techniques through multiple lectures, exploring various methods and functions. You will strengthen your knowledge of essential data structures, including lists, tuples, sets, and dictionaries. The section concludes with a comprehensive review of fundamental programming concepts.
OOP in Python
This section introduces the principles of Object-Oriented Programming (OOP) in Python. You'll learn about classes, constructors, dunder methods, inheritance, encapsulation, multiple inheritance, overriding, and decorators, mastering the fundamentals of OOP principles and techniques.
Lots of Python Challenges with Answers
Reinforce your Python knowledge with a comprehensive set of coding exercises, each accompanied by a detailed solution. These challenges allow you to put your knowledge into practice and identify areas for improvement. This hands-on approach is key to building confidence and competence.
Project 1 in Python
This section involves a practical Python project, walking you through the process from a high level overview to final execution. The lectures guide you through helpful notes and a complete solution.
Project 2 in Python
Further develop your Python skills with another project, offering a walk through, helpful notes and a step by step solution spread across multiple lectures.
Project 3 in Python
This section presents a third Python project, enhancing your ability to apply learned concepts to real-world scenarios. Lectures will cover a project walk through, helpful notes and the solution.
Project 4: Twitter Chat Bot
This section guides you through building a Twitter chatbot, breaking down the process into manageable parts over three lectures. Learn to create interactive applications using Python.
Project 5: Facebook Chat Bot
Build a Facebook chatbot using the Python Flask library and Ngrok. This project combines backend development with chatbot creation, offering a robust project to showcase your skills.
Numpy Python Library
Explore the NumPy library for numerical computing in Python. This section covers array manipulation, calculations, aggregations, reshaping, comparisons, and image processing. You'll learn efficient ways to work with numerical data in Python.
SQL Introduction
This section introduces you to the world of SQL, starting with downloading and installing PostgreSQL. You will learn to create and restore databases, setting the stage for efficient data management.
Airlines Database Overview
Familiarize yourself with a real-world PostgreSQL database – the Airlines database. Lectures provide a detailed overview of its structure and data types, preparing you for practical data analysis tasks.
SQL Basics Part 1
Master fundamental SQL queries using the 'SELECT' statement. Learn how to extract data using different techniques like 'DISTINCT' to retrieve unique values and efficiently querying across multiple tables within the Airlines database.
SQL Basics Part 2
Build upon your SQL foundation by mastering the 'WHERE' clause. Explore operators, conditional statements ('AND', 'OR'), pattern matching ('LIKE'), and range selection ('BETWEEN', 'IN'). This section equips you to filter data effectively.
SQL Basics Part 3
Refine your SQL skills with techniques such as 'LIMIT' and 'ORDER BY' for data pagination and sorting. Explore handling NULL values and data type conversions using 'CAST'.
SQL Aggregations
Learn to perform data aggregation using functions like 'COUNT', 'SUM', 'MIN', 'MAX', and 'AVG'. You'll also master 'GROUP BY' and 'HAVING' clauses for advanced data summarization.
SQL Conditional
Master advanced SQL conditional logic using 'CASE', 'NULLIF', and 'COALESCE' functions, enhancing your ability to handle different data scenarios efficiently.
SQL Time Data Management
This section focuses on managing and manipulating time-related data in PostgreSQL, using functions such as 'EXTRACT', 'DATE_TRUNC', 'DATE_PART', 'CURRENT_DATE', and 'NOW' to handle and analyze time-series data.
Intermediate: SQL Joins
Explore various SQL join types – INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, CROSS, and SELF joins – gaining a deep understanding of their applications and how to use 'USING' and 'NATURAL JOIN'.
Intermediate: Sub-queries & Common Table Expression in SQL
Enhance your SQL expertise with subqueries and Common Table Expressions (CTEs). Learn how to use subqueries with EXISTS, ANY, and ALL, and master CTEs for simplifying complex queries.
Advanced: PostgreSQL Math & Window functions
This section introduces advanced PostgreSQL features, including mathematical functions and window functions ('OVER', 'RANK', 'NTILE', 'LEAD', and 'LAG'). You'll learn to perform complex calculations and data analysis.
Advanced: PostgreSQL String Functions
Master string manipulation techniques using PostgreSQL functions such as 'POSITION', 'STRPOS', 'REPLACE', 'LEFT', 'RIGHT', 'BTRIM', 'SPLIT_PART', 'CONCAT', 'LOWER', 'UPPER', and 'INITCAP'.
Advanced: Other SQL Functions
Explore additional advanced SQL functions such as 'GROUPING SETS', 'ROLLUP', 'CUBE', data loading using 'SELECT INTO', and creating views.
Data Analysis Process Overview
Understand the comprehensive data analysis process, from defining objectives to presenting insightful conclusions. This lecture provides the structure for your analysis journey.
How to Use Python + SQL
Learn to integrate Python with SQL to efficiently analyze data. This section covers fetching data using 'fetchmany' and 'fetchall', querying using Pandas, and loading PostgreSQL data into Python for analysis.
Project 6: Using Python Pandas in Data Analysis of PostgreSQL Data output
This project demonstrates the power of combining Python's Pandas library with PostgreSQL. You'll perform advanced data analysis, utilizing visualization techniques to gain insights and draw conclusions.
Accessing, Manipulating & Filtering DataFrames Lectures
Master essential Pandas DataFrame techniques. Learn how to access, manipulate, filter, aggregate, and summarize data for comprehensive data analysis using Python.
Data Visualization
Learn to visualize data effectively using various Python techniques, including histograms, line charts, and others. This section guides you through choosing the right visualizations for your datasets.
Project 7: Data Analysis using SQL + Python
Combine your SQL and Python skills to analyze scraped data. Learn to extract data from a webpage using Python's `bs4` and `lxml` libraries, then analyze it using Pandas.
Project 8: Data Visualization of the Scraped Data
Visualize data you scraped from a website in the previous project, using relevant charts and graphs to present key insights.
Project 9: Save The Scraped Data to a Database
This section teaches you to load the data scraped from the webpage into a database, demonstrating efficient data handling techniques.
A/B Testing Overview using Python
Learn how to design and perform A/B testing for marketing data using Python. You will learn to analyze results and understand statistical significance.
Machine Learning Mastery: From Basics to Advanced Techniques
This section offers a comprehensive introduction to machine learning using scikit-learn. Topics include supervised learning, regression, classification, model evaluation, hyperparameter tuning, data preprocessing, and both supervised and unsupervised learning techniques such as k-Nearest Neighbors, Random Forest, Logistic Regression, Support Vector Machines, KMeans, Hierarchical Clustering, t-SNE, and PCA.
Project 10: Building Recommender System using NMF
Develop a recommender system using Non-negative Matrix Factorization (NMF), a powerful machine learning technique.
Project 11: Google App Data Analysis
Perform a comprehensive exploratory data analysis (EDA) of the Google App Store dataset. Learn techniques for data cleaning, preprocessing, visualization, statistical analysis, and storytelling.
Project 12: Using Pandas + Automation to Manage a Business Email List
This project demonstrates how to use Pandas and automation to efficiently manage a business email list. Learn how to handle large datasets and automate repetitive tasks.
Time Series Data Analysis using Python
Learn time series analysis techniques in Python, including data manipulation, handling time series growth, frequency changes, window functions, and lagging. This section enhances your ability to analyze data over time.
Project 13: Time Series Analysis of Betcoin Historical Data dataset
Apply time series analysis methods to a real-world dataset, focusing on forecasting future values based on historical data.
Using ChatGPT and Generative AI for Data
Discover how to leverage ChatGPT and other Generative AI tools to enhance your data analysis and science processes. This section explores effective prompts and strategies for AI-assisted analysis.
Bonus
A final thank you to conclude the course.
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