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.

Julian McAuley
Ilkay Altintas

Instructors: Julian McAuley

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Get in-depth knowledge of a subject
4.2

(171 reviews)

Intermediate level
Some related experience required
4 weeks to complete
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
4 weeks to complete
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
42 practice exercises

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Specialization - 4 course series

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

Data Processing, Data Visualization Software, Python Programming, NumPy, Matplotlib, Interactive Data Visualization, Data Import/Export, JSON, Web Scraping, Pandas (Python Package), Data Science, Jupyter, Data Visualization, Data Manipulation, and Data Cleansing

What you'll learn

Skills you'll gain

Feature Engineering, Supervised Learning, Regression Analysis, Data Cleansing, Data Manipulation, Predictive Analytics, Classification And Regression Tree (CART), Tensorflow, Predictive Modeling, Deep Learning, Data Science, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Applied Machine Learning, Design Thinking, and Statistical Methods

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.

Skills you'll gain

Predictive Modeling, Data Validation, Supervised Learning, Regression Analysis, Test Data, Machine Learning Methods, Natural Language Processing, Applied Machine Learning, Python Programming, Statistical Methods, and Verification And Validation

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

Django (Web Framework), Flask (Web Framework), Applied Machine Learning, Application Deployment, Predictive Modeling, Python Programming, Data Manipulation, Data Processing, Web Applications, Regression Analysis, and Machine Learning

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Instructors

Julian McAuley
University of California San Diego
5 Courses32,318 learners

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