About Course

Python Data Products for Predictive Analytics Specialization – FREE Course

This comprehensive specialization from Universitiet Leiden will empower you to become a data-driven expert in predictive analytics using Python. Learn from top instructors and gain the skills needed to create data products that drive impactful results.

Top companies like Google, Facebook, and Netflix leverage data analytics to enhance their offerings, and this specialization provides you with the tools to do the same. Enhance your Python programming skills and learn to build accurate predictive models for a variety of applications.

This specialization is perfect for those with a solid foundation in Python programming. You will delve into building statistical models, designing efficient data workflows, and mastering the art of making meaningful predictions for business and research purposes.

Discover how to extract valuable insights from diverse data sources using advanced data science methods and techniques.

This course is completely FREE and includes four additional courses from UC San Diego.

Enroll today and unlock the power of predictive analytics with Python!

This course is from Udemy, Udacity, Coursera, MasterClass, NearPeer, and other leading platforms.

Keywords: Python, Data Analytics, Predictive Analytics, Machine Learning, Data Science, Specialization, Free Course, Universitiet Leiden, UC San Diego, Data Products, Data-Driven, Business Analytics, Research, Data Workflow, Statistical Models, Data Insights.

Course Content

01. basic-data-processing-visualization-python

A Message from the Professor
001 001_syllabus_instructions.html

00:00
003 002_course-materials_instructions.html

00:00
005 003_set-up-your-system_instructions.html

00:00
009 001_what-is-a-data-product.mp4

00:00
013 002_data-product-examples-in-enterprise.mp4

00:00
017 003_developing-a-data-product-strategy.mp4

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018 004_our-case-study-recommender-systems_instructions.html

00:00
020 005_review-data-products_quiz.html

00:00
024 001_python-and-jupyter-basics.mp4

00:00
028 002_python-recap.mp4

00:00
029 003_optional-python-how-to-run_instructions.html

00:00
030 004_optional-python-additional-resources-and-recommended-readings_instructions.html

00:00
034 005_livecoding-getting-started-with-jupyter.mp4

00:00
035 006_review-python-and-jupyter_quiz.html

00:00
039 001_csv-json-files.mp4

00:00
043 002_reading-csv-json-files.mp4

00:00
047 003_processing-structured-data-in-python.mp4

00:00
050 004_live-coding-json.mp4

00:00
051 005_review-csv-and-json-files_quiz.html

00:00
055 001_extracting-simple-statistics-from-datasets.mp4

00:00
059 002_simple-statistics-live-coding.mp4

00:00
060 003_review-simple-statistics_quiz.html

00:00
065 001_data-filtering-and-cleaning.mp4

00:00
066 002_review-data-filtering-and-cleaning_quiz.html

00:00
070 001_processing-text-and-strings-in-python.mp4

00:00
074 002_processing-times-and-dates-in-python.mp4

00:00
077 003_livecoding-time-and-date-data.mp4

00:00
078 004_review-processing-different-data-types_quiz.html

00:00
083 001_matrix-processing-and-numpy.mp4

00:00
084 002_review-numpy_quiz.html

00:00
088 001_introduction-to-data-visualization.mp4

00:00
092 002_introduction-to-matplotlib.mp4

00:00
095 003_live-coding-matplotlib.mp4

00:00
096 004_review-matplotlib_quiz.html

00:00
100 001_urllib-and-beautifulsoup.mp4

00:00
101 002_review-urllib-and-beautifulsoup_quiz.html

00:00
106 001_course-summary.mp4

00:00
108 002_where-to-find-datasets_instructions.html

00:00
109 003_project-submission_peer_assignment_instructions.html

00:00
111 001__resources.html

00:00
Section Quiz

02. design-thinking-predictive-analytics-data-products

003 002_course-materials_instructions.html

00:00
005 003_set-up-your-system_instructions.html

00:00
012 001_introduction-to-supervised-learning.mp4

00:00
017 002_supervised-learning-regression.mp4

00:00
022 001_regression-in-python.mp4

00:00
027 002_time-series-regression.mp4

00:00
032 003_autoregression.mp4

00:00
037 001_features-from-categorical-data.mp4

00:00
042 002_features-from-temporal-data.mp4

00:00
047 003_feature-transformations.mp4

00:00
052 004_missing-values.mp4

00:00
053 005_supplementary-notebook-for-features_instructions.html

00:00
059 001_supervised-learning-classification.mp4

00:00
064 002_classification-nearest-neighbors.mp4

00:00
069 003_classification-logistic-regression.mp4

00:00
074 004_introduction-to-support-vector-machines.mp4

00:00
079 001_classification-in-python.mp4

00:00
084 002_introduction-to-training-and-testing.mp4

00:00
089 001_gradient-descent-in-python.mp4

00:00
094 002_gradient-descent-in-tensorflow.mp4

00:00
097 003_livecoding-tensorflow.mp4

00:00
098 001_project-description_instructions.html

00:00
099 002_where-to-find-datasets_instructions.html

00:00
101 001__resources.html

00:00
Section Quiz

03. meaningful-predictive-modeling

004 002_syllabus_instructions.html

00:00
005 003_setting-up-your-system_instructions.html

00:00
001 001_introduction-to-course-3-meaningful-predictive-modeling.en.srt

00:00
006 004_optional-additional-resources-and-recommended-readings_instructions.html

00:00
013 001_motivation-behind-the-mse.mp4

00:00
017 002_regression-diagnostics-mse-and-r2.mp4

00:00
011 001_motivation-behind-the-mse.en.srt

00:00
016 002_regression-diagnostics-mse-and-r2.en.txt

00:00
021 003_over-and-under-fitting.mp4

00:00
015 002_regression-diagnostics-mse-and-r2.en.srt

00:00
020 003_over-and-under-fitting.en.txt

00:00
025 001_classification-diagnostics-accuracy-and-error.mp4

00:00
019 003_over-and-under-fitting.en.srt

00:00
024 001_classification-diagnostics-accuracy-and-error.en.txt

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029 002_classification-diagnostics-precision-and-recall.mp4

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023 001_classification-diagnostics-accuracy-and-error.en.srt

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028 002_classification-diagnostics-precision-and-recall.en.txt

00:00
033 001_setting-up-a-codebase-for-evaluation-and-validation.mp4

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027 002_classification-diagnostics-precision-and-recall.en.srt

00:00
030 001_setting-up-a-codebase-for-evaluation-and-validation_PDP_Course3_Slides.pdf

00:00
041 002_adding-a-regularizer-to-our-model-and-evaluating-the-regularized-model.mp4

00:00
031 001_setting-up-a-codebase-for-evaluation-and-validation.en.srt

00:00
040 002_adding-a-regularizer-to-our-model-and-evaluating-the-regularized-model.en.txt

00:00
045 003_evaluating-classifiers-for-ranking.mp4

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039 002_adding-a-regularizer-to-our-model-and-evaluating-the-regularized-model.en.srt

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044 003_evaluating-classifiers-for-ranking.en.txt

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049 001_validation.mp4

00:00
043 003_evaluating-classifiers-for-ranking.en.srt

00:00
048 001_validation.en.txt

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053 002_theorems-about-training-testing-and-validation.mp4

00:00
047 001_validation.en.srt

00:00
052 002_theorems-about-training-testing-and-validation.en.txt

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057 001_implementing-a-regularization-pipeline-in-python.mp4

00:00
051 002_theorems-about-training-testing-and-validation.en.srt

00:00
056 001_implementing-a-regularization-pipeline-in-python.en.txt

00:00
060 002_guidelines-on-the-implementation-of-predictive-pipelines.mp4

00:00
055 001_implementing-a-regularization-pipeline-in-python.en.srt

00:00
062 002_where-to-find-datasets_instructions.html

00:00
003 001_introduction-to-course-3-meaningful-predictive-modeling.mp4

00:00
Section Quiz

04. deploying-machine-learning-models

001 001_syllabus_instructions.html

00:00
004 003_setting-up-your-system_instructions.html

00:00
007 001_introduction-to-recommender-systems.mp4

00:00
010 002_recommender-systems-versus-other-forms-of-supervised-learning.mp4

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013 003_collaborative-filtering-based-recommendation.mp4

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016 001_latent-factor-models-part-1.mp4

00:00
019 002_latent-factor-models-part-2.mp4

00:00
022 001_implementing-a-similarity-based-recommender.mp4

00:00
025 002_similarity-based-recommender-for-rating-prediction.mp4

00:00
028 001_implementing-a-latent-factor-model-part-1.mp4

00:00
031 002_implementing-a-latent-factor-model-part-2.mp4

00:00
034 001_intro-to-web-server-frameworks-in-python.mp4

00:00
037 002_intro-to-django.mp4

00:00
038 003_setting-up-your-workspace-with-docker-django_instructions.html

00:00
041 004_flask.mp4

00:00
043 001_project-description_instructions.html

00:00
044 002_how-to-find-a-dataset_instructions.html

00:00
047 001_description-of-capstone-tasks.mp4

00:00
050 002_capstone-overview_instructions.html

00:00
052 001__resources.html

00:00
Section Quiz

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About Course

Python Data Products for Predictive Analytics Specialization – FREE Course

What Will You Learn?

Course Content

01. basic-data-processing-visualization-python

A Message from the Professor

001 001_syllabus_instructions.html

003 002_course-materials_instructions.html

005 003_set-up-your-system_instructions.html

009 001_what-is-a-data-product.mp4

013 002_data-product-examples-in-enterprise.mp4

017 003_developing-a-data-product-strategy.mp4

018 004_our-case-study-recommender-systems_instructions.html

020 005_review-data-products_quiz.html

024 001_python-and-jupyter-basics.mp4

028 002_python-recap.mp4

029 003_optional-python-how-to-run_instructions.html

030 004_optional-python-additional-resources-and-recommended-readings_instructions.html

034 005_livecoding-getting-started-with-jupyter.mp4

035 006_review-python-and-jupyter_quiz.html

039 001_csv-json-files.mp4

043 002_reading-csv-json-files.mp4

047 003_processing-structured-data-in-python.mp4

050 004_live-coding-json.mp4

051 005_review-csv-and-json-files_quiz.html

055 001_extracting-simple-statistics-from-datasets.mp4

059 002_simple-statistics-live-coding.mp4

060 003_review-simple-statistics_quiz.html

065 001_data-filtering-and-cleaning.mp4

066 002_review-data-filtering-and-cleaning_quiz.html

070 001_processing-text-and-strings-in-python.mp4

074 002_processing-times-and-dates-in-python.mp4

077 003_livecoding-time-and-date-data.mp4

078 004_review-processing-different-data-types_quiz.html

083 001_matrix-processing-and-numpy.mp4

084 002_review-numpy_quiz.html

088 001_introduction-to-data-visualization.mp4

092 002_introduction-to-matplotlib.mp4

095 003_live-coding-matplotlib.mp4

096 004_review-matplotlib_quiz.html

100 001_urllib-and-beautifulsoup.mp4

101 002_review-urllib-and-beautifulsoup_quiz.html

106 001_course-summary.mp4

108 002_where-to-find-datasets_instructions.html

109 003_project-submission_peer_assignment_instructions.html

111 001__resources.html

Section Quiz

02. design-thinking-predictive-analytics-data-products

003 002_course-materials_instructions.html

005 003_set-up-your-system_instructions.html

012 001_introduction-to-supervised-learning.mp4

017 002_supervised-learning-regression.mp4

022 001_regression-in-python.mp4

027 002_time-series-regression.mp4

032 003_autoregression.mp4

037 001_features-from-categorical-data.mp4

042 002_features-from-temporal-data.mp4

047 003_feature-transformations.mp4

052 004_missing-values.mp4

053 005_supplementary-notebook-for-features_instructions.html

059 001_supervised-learning-classification.mp4

064 002_classification-nearest-neighbors.mp4

069 003_classification-logistic-regression.mp4

074 004_introduction-to-support-vector-machines.mp4

079 001_classification-in-python.mp4

084 002_introduction-to-training-and-testing.mp4

089 001_gradient-descent-in-python.mp4

094 002_gradient-descent-in-tensorflow.mp4

097 003_livecoding-tensorflow.mp4

098 001_project-description_instructions.html

099 002_where-to-find-datasets_instructions.html

101 001__resources.html

Section Quiz

03. meaningful-predictive-modeling

004 002_syllabus_instructions.html

005 003_setting-up-your-system_instructions.html

001 001_introduction-to-course-3-meaningful-predictive-modeling.en.srt

006 004_optional-additional-resources-and-recommended-readings_instructions.html

013 001_motivation-behind-the-mse.mp4

017 002_regression-diagnostics-mse-and-r2.mp4