About Course

Strategic Business Analytics Specialization

This specialization, offered completely free of charge on Theetay, is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. It’s delivered by ESSEC Business School, one of the world’s leading business schools, and features instruction from two of Europe’s leading professors in business analytics and marketing.

This course is perfect for those with a background in statistics, R or another programming language, and familiarity with databases and data analysis techniques such as regression, classification, and clustering. You will learn to utilize statistical techniques in R to develop business intelligence insights and present them in a compelling way to enable smart and sustainable business decisions.

The specialization will cover a wide variety of analytics approaches in different industry domains, including hands-on case studies in real business contexts. You’ll engage with examples like predicting and forecasting events, statistical customer segmentation, and calculating customer scores and lifetime value. The third course and the Capstone Project are designed in partnership with Accenture, a global leader in consulting, technology services, and outsourcing. You’ll explore applications in various sectors, including media, communications, and public service.

Upon completion of this specialization, available for free on Theetay, you will earn a Specialization Certificate from ESSEC Business School.

This course is offered for free on Theetay and is available from platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and other leading online learning providers.

Keywords: Business Analytics, Data Science, Statistics, R, Regression, Classification, Clustering, Data Analysis, Predictive Analytics, Customer Segmentation, Business Intelligence, ESSEC Business School, Accenture, Free Online Courses

Course Content

01. Foundations of strategic business analytics

A Message from the Professor
001 001_welcome-to-the-course.en.srt

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006 002_becoming-a-business-analytics-expert.mp4

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009 003_why-it-is-all-about-value-not-data.mp4

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012 004_how-to-leverage-data-for-value-from-data-to-insight.mp4

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013 005_dataset-for-practice-quiz_instructions.html

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015 006_practice-quizz-module-1_quiz.html

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018 001_introduction-whats-the-point-of-finding-groups-within-data.mp4

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021 002_basic-clustering-using-ad-hoc-techniques-the-example-of-product-management.mp4

00:00
024 003_identifying-groups-within-data-whats-the-intuition-behind-clustering-the-example.mp4

00:00
027 004_introduction-to-customer-segmentation.mp4

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030 001_presentation-of-pauline-glikman.mp4

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033 002_recital-m2-sku-example.mp4

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036 003_recital-m2-hr-example.mp4

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039 004_recital-m2-telecom-example.mp4

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044 005_script-and-dataset-files-to-replicate-recitals_instructions.html

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050 001_wrap-up-identifying-groups-within-data.mp4

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057 002_why-use-business-analytics-to-understand-the-relationship-between-causes-and.mp4

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060 003_understanding-what-distinguishes-two-categories.mp4

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063 004_beyond-the-regression-estimates-reporting-effects-in-a-visual-way.mp4

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066 001_recital-m3-credit-score-example.mp4

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069 002_recital-m3-hr-example.mp4

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072 003_script-and-dataset-files-to-replicate-recitals_instructions.html

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078 001_wrap-up-identifying-causes-to-effects.mp4

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085 002_predicting-events-sales-defaults-risks-churn-etc.mp4

00:00
088 003_using-classification-and-regression-techniques-to-forecast.mp4

00:00
091 004_predicting-when-an-event-will-happen-with-survival-analysis.mp4

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094 005_introduction-to-time-series-and-seasonality.mp4

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097 001_recital-m4-credit-score.mp4

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100 002_recital-m4-hr-example.mp4

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103 003_recital-m4-predictive-maintenance-example.mp4

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106 004_recital-m4-chocolate-sales-example.mp4

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114 005_script-and-dataset-files-to-replicate-recitals_instructions.html

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121 001_wrap-up-forecasting-events.mp4

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128 002_its-all-about-the-story.mp4

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131 003_one-slide-one-idea.mp4

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134 004_one-picture-is-worth-a-thousand-words.mp4

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137 001_recital-m5-how-to-present-your-findings.mp4

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138 002_presentation-tips_instructions.html

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142 001_wrap-up-reporting-your-results.mp4

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147 002_peer-review_peer_assignment_instructions.html

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links.txt

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Section Quiz

02. Foundations of marketing analytics

003 001_foundations-of-marketing-analytics.mp4

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006 002_setting-up-the-environment-and-exploring-the-data-recital.mp4

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007 003_r-files-and-dataset_instructions.html

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016 001_introduction.mp4

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017 002_acxiom-url_instructions.html

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021 003_hierarchical-segmentation.mp4

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024 004_selecting-the-right-number-of-segments.mp4

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027 005_segmentation-variables.mp4

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030 006_recency-frequency-and-monetary-value.mp4

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033 007_computing-recency-frequency-and-monetary-value-with-r-recital-1.mp4

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036 008_data-transformation.mp4

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039 009_preparing-and-transforming-your-data-in-r-recital-2.mp4

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042 010_running-a-hierarchical-segmentation-in-r-recital-3.mp4

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043 001_instructions-before-starting-the-quiz-1_instructions.html

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046 002_quiz-module-1-20-of-final-grade_exam.html

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049 001_limitations-of-statistical-segmentation.mp4

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052 002_developing-a-managerial-segmentation.mp4

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055 003_coding-a-managerial-segmentation-in-r-recital-1.mp4

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058 004_describing-segments.mp4

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061 005_segmenting-a-database-retrospectively-in-r-recital-2.mp4

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064 006_segments-and-revenue-generation.mp4

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067 007_r-tutorial-recital-3.mp4

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068 001_instructions-before-starting-quiz-2_instructions.html

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071 002_quiz-module-2-20-of-final-grade_exam.html

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074 001_can-target-predict-a-customer-is-pregnant.mp4

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077 002_what-you-need-to-develop-a-scoring-model.mp4

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080 003_calibration-data-and-statistical-model.mp4

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083 004_building-a-predictive-model-in-r-recital.mp4

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084 001_instructions-before-starting-quiz-3_instructions.html

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087 002_quiz-module-3-20-of-final-grade_exam.html

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090 001_what-is-customer-lifetime-value-and-why-it-matters.mp4

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093 002_transition-probabilities-and-transition-matrix.mp4

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096 003_how-to-compute-a-transition-matrix-in-r-recital-1.mp4

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099 004_using-the-transition-matrix-to-estimate-how-customers-will-evolve.mp4

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102 005_using-the-transition-matrix-to-make-predictions-in-r-recital-2.mp4

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105 006_assigning-and-discounting-revenue.mp4

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108 007_computing-customer-lifetime-value-in-r-recital-3.mp4

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109 001_instructions-before-starting-the-quiz-4_instructions.html

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112 002_quiz-module-4_exam.html

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03. Case studies in business analytics with ACCENTURE

003 001_introduction-to-case-studies-in-business-analytics-with-accenture-fabrice-marque.mp4

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006 002_market-trends-and-key-challenges-in-analytics-mickael-svilar.mp4

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009 003_why-is-big-data-really-big-nicolas-glady.mp4

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012 004_winning-in-digital-powered-by-analytics-jean-pierre-bokobza.mp4

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015 001_big-data-predictive-maintenance-in-the-utilities-sector-rohit-banerji.mp4

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016 002_practice-quiz-on-resource-sector-case_quiz.html

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019 001_big-data-advanced-analytics-in-the-communications-industry-cian-o-hare.mp4

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020 002_practice-quiz-on-the-global-communication-case_quiz.html

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023 001_advanced-analytics-in-the-public-service-christopher-gray.mp4

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024 002_practice-quiz-on-the-public-service-case_quiz.html

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027 001_wrap-up-a-conceptual-framework-of-the-applications-of-big-data-analytics-nicolas.mp4

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034 002_solution-and-success-factors-christine-removille.mp4

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035 003_practice-quiz-on-the-canal-case-study_quiz.html

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038 001_introduction-and-key-digital-trends-in-financial-services.mp4

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041 002_analytics-capability-based-on-people-like-you.mp4

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044 003_how-to-leverage-people-like-you-micro-segmentation-example-1-increase-campaign.mp4

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047 004_how-to-leverage-people-like-you-micro-segmentation-example-2-optimize-pricing.mp4

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050 005_digital-transformation-and-wrap-up.mp4

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051 006_practice-quiz-on-the-financial-service-case-study_quiz.html

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054 001_healthcare-analytics-a-conceptual-framework-nicolas-glady.mp4

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057 002_introduction-and-key-challenges-paul-pierotti.mp4

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060 003_correlation-between-life-expectancy-and-health-spending-paul-pierotti.mp4

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063 004_presentation-of-5-health-analytics-use-cases-paul-pierotti.mp4

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066 005_focus-on-care-management-for-patients-with-chronic-diseases-paul-pierotti.mp4

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069 006_wrap-up-paul-pierotti.mp4

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070 007_practice-quiz-on-the-health-service-case_quiz.html

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073 001_advanced-analytics-in-the-pharmaceutical-industry-xavier-cimino.mp4

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074 002_practice-quiz-on-the-life-science-industry-case_quiz.html

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077 001_how-to-create-value-from-data-fabrice-marque.mp4

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080 002_wrap-up-mickael-svilar.mp4

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083 003_data-exploration-is-an-iterative-process-nicolas-glady.mp4

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086 004_analytics-exploration-oonagh-o-shea-noelle-doody.mp4

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089 005_wrap-up-capstone-guidelines-nicolas-glady.mp4

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090 001_preparation-for-the-capstone-project_peer_assignment_instructions.html

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Section Quiz

04. Capstone Create Value from Open Data

003 002_datasets-used-for-the-capstones_instructions.html

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006 003_example-deliverable-1_instructions.html

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008 004_example-final-deliverable_instructions.html

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010 001_guidelines-for-watching-the-following-video_instructions.html

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013 002_reporting-your-results-introduction.mp4

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014 003_guidelines-for-watching-the-following-video_instructions.html

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017 004_its-all-about-the-story.mp4

00:00
018 005_guidelines-for-watching-the-following-video_instructions.html

00:00
021 006_one-slide-one-idea.mp4

00:00
022 007_guidelines-for-watching-the-following-video_instructions.html

00:00
025 008_a-picture-is-worth-a-thousand-words.mp4

00:00
026 009_guidelines-for-watching-the-following-video_instructions.html

00:00
029 010_recital-m5-how-to-present-your-findings.mp4

00:00
030 011_guidelines-for-watching-the-following-video_instructions.html

00:00
033 012_wrap-up-reporting-your-results.mp4

00:00
034 001_guidelines-for-watching-the-following-video_instructions.html

00:00
037 002_how-to-create-value-from-data-fabrice-marque.mp4

00:00
038 003_guidelines-for-watching-the-following-video_instructions.html

00:00
041 004_wrap-up-mickael-svilar.mp4

00:00
042 005_guidelines-for-watching-the-following-video_instructions.html

00:00
045 006_data-exploration-is-an-iterative-process-nicolas-glady.mp4

00:00
046 007_guidelines-for-watching-the-following-video_instructions.html

00:00
049 008_analytics-exploration-oonagh-o-shea-noelle-doody.mp4

00:00
050 009_guidelines-for-watching-the-following-video_instructions.html

00:00
053 010_wrap-up-capstone-guidelines-nicolas-glady.mp4

00:00
054 001_optional-ungraded-peer-review-information_instructions.html

00:00
links.txt

00:00
Section Quiz

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About Course

Strategic Business Analytics Specialization

What Will You Learn?

Course Content

01. Foundations of strategic business analytics

A Message from the Professor

001 001_welcome-to-the-course.en.srt

006 002_becoming-a-business-analytics-expert.mp4

009 003_why-it-is-all-about-value-not-data.mp4

012 004_how-to-leverage-data-for-value-from-data-to-insight.mp4

013 005_dataset-for-practice-quiz_instructions.html

015 006_practice-quizz-module-1_quiz.html

018 001_introduction-whats-the-point-of-finding-groups-within-data.mp4

021 002_basic-clustering-using-ad-hoc-techniques-the-example-of-product-management.mp4

024 003_identifying-groups-within-data-whats-the-intuition-behind-clustering-the-example.mp4

027 004_introduction-to-customer-segmentation.mp4

030 001_presentation-of-pauline-glikman.mp4

033 002_recital-m2-sku-example.mp4

036 003_recital-m2-hr-example.mp4

039 004_recital-m2-telecom-example.mp4

044 005_script-and-dataset-files-to-replicate-recitals_instructions.html

050 001_wrap-up-identifying-groups-within-data.mp4

057 002_why-use-business-analytics-to-understand-the-relationship-between-causes-and.mp4

060 003_understanding-what-distinguishes-two-categories.mp4

063 004_beyond-the-regression-estimates-reporting-effects-in-a-visual-way.mp4

066 001_recital-m3-credit-score-example.mp4

069 002_recital-m3-hr-example.mp4

072 003_script-and-dataset-files-to-replicate-recitals_instructions.html

078 001_wrap-up-identifying-causes-to-effects.mp4

085 002_predicting-events-sales-defaults-risks-churn-etc.mp4

088 003_using-classification-and-regression-techniques-to-forecast.mp4

091 004_predicting-when-an-event-will-happen-with-survival-analysis.mp4

094 005_introduction-to-time-series-and-seasonality.mp4

097 001_recital-m4-credit-score.mp4

100 002_recital-m4-hr-example.mp4

103 003_recital-m4-predictive-maintenance-example.mp4

106 004_recital-m4-chocolate-sales-example.mp4

114 005_script-and-dataset-files-to-replicate-recitals_instructions.html

121 001_wrap-up-forecasting-events.mp4

128 002_its-all-about-the-story.mp4

131 003_one-slide-one-idea.mp4

134 004_one-picture-is-worth-a-thousand-words.mp4

137 001_recital-m5-how-to-present-your-findings.mp4

138 002_presentation-tips_instructions.html

142 001_wrap-up-reporting-your-results.mp4

147 002_peer-review_peer_assignment_instructions.html

links.txt

Section Quiz

02. Foundations of marketing analytics

003 001_foundations-of-marketing-analytics.mp4

006 002_setting-up-the-environment-and-exploring-the-data-recital.mp4

007 003_r-files-and-dataset_instructions.html

016 001_introduction.mp4

017 002_acxiom-url_instructions.html

021 003_hierarchical-segmentation.mp4

024 004_selecting-the-right-number-of-segments.mp4

027 005_segmentation-variables.mp4

030 006_recency-frequency-and-monetary-value.mp4

033 007_computing-recency-frequency-and-monetary-value-with-r-recital-1.mp4

036 008_data-transformation.mp4

039 009_preparing-and-transforming-your-data-in-r-recital-2.mp4

042 010_running-a-hierarchical-segmentation-in-r-recital-3.mp4

043 001_instructions-before-starting-the-quiz-1_instructions.html

046 002_quiz-module-1-20-of-final-grade_exam.html

049 001_limitations-of-statistical-segmentation.mp4

052 002_developing-a-managerial-segmentation.mp4

055 003_coding-a-managerial-segmentation-in-r-recital-1.mp4

058 004_describing-segments.mp4

061 005_segmenting-a-database-retrospectively-in-r-recital-2.mp4

064 006_segments-and-revenue-generation.mp4

067 007_r-tutorial-recital-3.mp4

068 001_instructions-before-starting-quiz-2_instructions.html

071 002_quiz-module-2-20-of-final-grade_exam.html

074 001_can-target-predict-a-customer-is-pregnant.mp4

077 002_what-you-need-to-develop-a-scoring-model.mp4

080 003_calibration-data-and-statistical-model.mp4

083 004_building-a-predictive-model-in-r-recital.mp4

084 001_instructions-before-starting-quiz-3_instructions.html

087 002_quiz-module-3-20-of-final-grade_exam.html

090 001_what-is-customer-lifetime-value-and-why-it-matters.mp4