Applied ML: Intro to Analytics with Pandas and PySpark

🚀 Course Title: Applied ML: Intro to Analytics with Pandas and PySpark 🎓 Headline: Hands-on training to analyze and prepare data for Machine Learning using Pandas, Pyspark and SQL

🔥 Course Description:

Welcome to the intersection of data analytics and machine learning! In our previous journey together in "Applied ML: The Big Picture," we established that mastering data exploration and preparation is a cornerstone of successful Machine Learning (ML) projects. This course, "Applied ML: Intro to Analytics with Pandas and PySpark," dives deeper into this vital phase of the ML lifecycle.

Why this course?

• Real-World Skills: Gain hands-on experience with data analysis tools that are critical in real-world ML projects.
• Versatile Tools: Understand when and how to leverage Pandas, PySpark, and SQL for various data analytics tasks.
• Scenario-Based Learning: Engage with a variety of scenarios that challenge you to apply the right tool at the right time.

What you'll learn:

📊 Data Processing Techniques:

• Data cleaning, normalization, and transformation with Pandas.
• Scalable data processing with PySpark for large datasets.
• Writing SQL queries to extract meaningful information from databases.

🔍 Exploration and Transformation:

• Discover patterns, anomalies, and insights in your data.
• Learn how to manipulate data structures efficiently for ML.
• Master data visualization to communicate findings effectively.

Who is this course for?

• Aspiring Data Scientists and ML Engineers looking to solidify their data handling skills.
• Python developers aiming to extend their knowledge of data analysis tools.
• Job seekers preparing for interviews in the fields of ML, Data Science, or Business Analytics.

What you'll need:

👨‍💻 A System with a Python Development Environment:

• A computer with Python installed (Windows, macOS, or Linux).
• An integrated development environment (IDE) like Jupyter Notebook or PyCharm.

What's inside:

• Step-by-step video tutorials.
• Real datasets for practice and application of skills learned.
• Quizzes to test your understanding.
• A supportive community to exchange ideas and solutions.

Key Takeaways:

• A comprehensive understanding of data preparation using Pandas, PySpark, and SQL.
• Ability to perform complex analytics tasks with real-world datasets.
• Knowledge of when and how to choose the most appropriate tool for your ML project.

🎯 Course Outline:

1. Introduction to Data Analysis Tools

   • Overview of Pandas, PySpark, and SQL.
   • Setting up your Python environment.

2. Data Cleaning & Transformation with Pandas

   • Handling missing data.
   • Data type conversions and operations.
   • Advanced data manipulation techniques.

3. Scalable Data Processing with PySpark

   • Resilient Distributed Dataset (RDD) operations.
   • DataFrame API for fast, in-memory analytics.
   • Handling large datasets efficiently.

4. Data Exploration & Visualization

   • Key statistical measures to explore data.
   • Plotting and charting with Python libraries.
   • Interactive visualizations for deeper insights.

5. Integrating SQL for Data Analysis

   • Writing and optimizing SQL queries.
   • Combining SQL with Pandas and PySpark.
   • Best practices for data warehousing.

6. Capstone Project: Real-World Data Analytics Challenge

   • Apply your skills to a comprehensive dataset.
   • Analyze, clean, and transform data for ML applications.
   • Present your findings in an impactful way.

Enroll now and unlock the potential of your data with "Applied ML: Intro to Analytics with Pandas and PySpark"! 🌟