University of California San Diego
Python Data Products for Predictive Analytics Specialization
University of California San Diego

Python Data Products for Predictive Analytics Specialization

Build Predictive Systems with Accuracy. Collect, model, and deploy data-driven systems using Python and machine learning.

Taught in English

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Julian McAuley
Ilkay Altintas

Instructors: Julian McAuley

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.1

(167 reviews)

Intermediate level
Some related experience required
1 month at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Discover how to transform data and make it suitable for data-driven predictive tasks

  • Understand how to compute basic statistics using real-world datasets of consumer activities, like product reviews and more

  • Use Python to create interactive data visualizations to make meaningful predictions and build simple demo systems

  • Perform simple regressions and classifications on datasets using machine learning libraries

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.1

(167 reviews)

Intermediate level
Some related experience required
1 month at 10 hours a week
Flexible schedule
Learn at your own pace

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Specialization - 4 course series

Basic Data Processing and Visualization

Course 110 hours4.3 (187 ratings)

What you'll learn

  • Develop data strategy and process for how data will be generated, collected, and consumed

  • Load and process formatted datasets such as CSV and JSON.

  • Deal with data in various formats (e.g. timestamps, strings) and filter and “clean” datasets by removing outliers etc.

  • Basic experience with data processing libraries such as numpy and data ingestion with urllib, requests

Skills you'll gain

Category: Python Libraries
Category: Data Pre-Processing
Category: Data Visualization
Category: Web Scraping

What you'll learn

Meaningful Predictive Modeling

Course 38 hours4.3 (47 ratings)

What you'll learn

  • Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).

  • Evaluate the performance of regressors / classifiers using the above measures.

  • Understand the difference between training/testing performance, and generalizability.

  • Understand techniques to avoid overfitting and achieve good generalization performance.

Deploying Machine Learning Models

Course 410 hours3.5 (51 ratings)

What you'll learn

  • Project structure of interactive Python data applications

  • Python web server frameworks: (e.g.) Flask, Django, Dash

  • Best practices around deploying ML models and monitoring performance

  • Deployment scripts, serializing models, APIs

Skills you'll gain

Category: Python Programming
Category: Big Data Products
Category: Recommender Systems

Instructors

Julian McAuley
University of California San Diego
5 Courses30,060 learners

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