Leveraging Online Resources for Python Analytics
This course introduces important resources for data science, including visualization libraries, deep learning frameworks, and cloud-based environments. It also explores BigML and Google Colab—powerful resources for building and sharing analytics.
What you'll learn
As data science and data analytics become ever more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming.
In this course, Leveraging Online Resources for Python Analytics, you will gain the ability to find resources that can help you to correctly frame and solve your problem. First, you will survey some of the important visualization libraries, machine learning and deep learning frameworks, and cloud-based solutions out there.
Next, you will discover the benefits of using a tool like BigML, which is a platform for building ML models that abstracts away much of the underlying complexity. Democratization of ML is an important trend today, and technologies like BigML are at the forefront of that trend. You will see, for instance, how BigML seamlessly integrates visualizations known as partial dependency plots, which combine the results of large numbers of ML predictions into an easily understandable form so that you can understand exactly what your ML model is doing.
Finally, you will round out your knowledge by working with Google Colab, a free web-based way to build models. The models are hosted in Jupyter notebooks that reside on Google Drive and run on virtual machines in the cloud.
When you’re finished with this course, you will have the skills and knowledge to quickly and efficiently identify valuable online resources and libraries that will help you on your journey as a data science practitioner.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 1m
- Python for Data Analysts 7m
- Python Resources for Analysts 5m
- Demo: Exploring Online Resources 6m
- Workflows in Data Analytics 5m
- Demo: Cleaning Data 6m
- Demo: Summary Statistics and Basic Analysis 3m
- Demo: Visualizing Relationships in Data 4m
- Demo: Sharing Visualizations Online Using Plotly 5m
- Demo: Prototyping a Classifier 3m
- Demo: Writing a Python Script for a Classification Model 8m
- Module Summary 1m
- Module Overview 1m
- Introducing Big ML 3m
- Demo: Getting Started with Big ML 1m
- Demo: Configuring Data Sources and Creating Datasets 8m
- Demo: Data Preparation and Visualization 6m
- Demo: Splitting into Training and Test Subsets 2m
- Demo: Building Models 5m
- Demo: Evaluating Models 2m
- Demo: Batch and Individual Predictions 5m
- Demo: Clustering 5m
- Demo: Anomaly Detection 3m
- Module Summary 1m
- Module Overview 1m
- Introducing Google Colab 2m
- Demo: Introducing the Google Colab Interface 3m
- Demo: Colab Notebooks—Similar yet Different 2m
- Demo: Interactive Forms 6m
- Demo: Accessing Google Drive Contents from Colab 2m
- Demo: Widgets 5m
- Demo: Building a Regression Model 6m
- Demo: Integrating with Github 2m
- Summary and Further Study 2m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more