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

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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.

Julian McAuley
Ilkay Altintas

Instructors: Julian McAuley

14,387 already enrolled

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Get in-depth knowledge of a subject
4.2

(171 reviews)

Intermediate level
Some related experience required
1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.2

(171 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

Details to know

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Taught in English

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Specialization - 4 course series

Basic Data Processing and Visualization

Course 110 hours4.3 (192 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 (48 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 Courses30,917 learners

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