Data Science in Action using Python

🌟 Course Title: Data Science in Action using Python

🚀 Course Headline: Gain hands-on experience in building a Data Driven AI engagement using Python

Course Description:

In today's world, where data is generated at an unprecedented scale and speed, understanding how to harness this vast amount of information to drive intelligent decision-making is not just beneficial—it's essential. Data Science in Action using Python is designed to take you through the full lifecycle of a data science project, from selecting a problem to deploying AI models and iterating based on user feedback and learning.

This course goes beyond theoretical knowledge by modifying the widely acknowledged CRISP-DM (Cross-Industry Standard Process for Data Mining) framework to address the challenges of big data. We've applied our modifications to real-world, large-scale projects and are excited to share this methodology with you.

🔍 Real-World Application: You will engage with a real case study throughout the course, applying the concepts learned directly to a tangible project. This hands-on experience ensures that the skills and knowledge you gain are applicable in your day-to-day life.

Course Structure:

For the 'Clickers': If you prefer to use data science tools with user interfaces like SPSS Modeler, Excel, or Alteryx, our course caters to that as well. You'll learn to add formulas and specifications without delving into coding.

For the 'Coders': For those who enjoy programming in Python, this course will provide an introductory coding experience tailored to data science tasks. Python is the language of choice for a majority of data scientists, and our course will guide you through its libraries and applications.

Key Course Components:

1. Setting Up Your Environment: We kick off with comprehensive instructions on preparing your sandbox environment for executing Python code. This ensures that from day one, you're ready to start coding.

2. Data Science Methodology Overview: A critical review of the key steps, tasks, and activities associated with our data science methodology sets the foundation for what's to come.

Our 7-step data science methodology is the core of this course, and each step will be explained using Python and our real-life use case example:

1. Describe Use Case: We begin by explaining how to select a use case for your data science work.

2. Describe Data: Understanding your data sources and exploring your datasets are crucial steps, which we'll cover using Python.

3. Prepare Datasets: Learn to prepare your data sets for analysis with Python.

4. Develop Model: Dive into applying various AI modeling techniques such as time series analysis, classification, clustering, regression, and forecasting—all with a focus on Python.

5. Evaluate Model: Gain measurements to evaluate your AI model results effectively.

6. Deploy Model: Understand the process for deploying your AI models into real-world applications.

7. Monitor Model: Learn how to continuously monitor and evaluate your models in production to ensure they remain effective over time.

Course Outcomes:

Throughout the course, you will design a use case, work on its implementation using Python, and submit your final notebook following the provided instructions. You will download necessary data sets and sample Python code, complete all assignments in each section, and see your skills develop as you apply them to a real-world project.

By completing this course, you'll not only have a solid understanding of data science methodologies but also be able to implement these strategies using Python—a skill set that is highly sought after in the field of Data Science.

🎉 Join us on this journey to transform raw data into actionable AI insights with Python! 🎉