If you’re interested in learning about data science, this free course will introduce you to the fundamentals of data preparation, predictive modeling, data science, and the deployment and maintenance of models in a business environment following a tried and tested project methodology.
Mode autodidacte
Langue: English

Informations sur le cours

Course Summary

In 2012, Harvard Business Review named data science the "sexiest job of the 21st century." Why are data scientists in such demand these days? The answer is that over the past decade, there has been an explosion in the data generated and retained by companies, and they need to leverage and exploit it. Data scientists are the people who make sense out of all this data and figure out just what can be done with it.

If you’re interested in learning about data science, this course will introduce you to the fundamentals of data preparation, predictive modeling, data science, and the deployment and maintenance of models in a business environment following a tried and tested project methodology.

In the first week, we’ll cover the fundamental challenges of business problem understanding and identifying the appropriate analytical approach. In the second week, we’ll cover data preparation, selecting variables, and data encoding. Weeks three and four will introduce you to a wide range of algorithms such as decision trees, regression, neural networks, basket analysis, and simulation. Week five explains how we evaluate the performance of our models and the approaches we take to improve them. In week 6, you’ll learn about model deployment and maintenance, and we’ll debunk some common myths.

Data science is a complex subject to understand, but in this course you’ll learn about the fundamental principles, look at how the algorithms can add value to your business, and we’ll demystify the complex processes. You don’t have to be a rocket scientist to be a data scientist.

Here is what some participants are saying about the course:

  • "Definitely, best introduction into Data Science I have ever seen. Compliments and applause." read the original post

  • "I like this course, this course at some point made the heavy things light and also really good to centralize the knowledge previously had." read the original post

  • "This is a wonderful course and Stuart has done an excellent teaching job. Practical exercises with predictive product are very useful and helpful." read the original post

  • "Special thanks to Stuart Clarke, with all my respect. I like the organization of the units and contents they include. This was a course which requires time spending on it but definitely worth it." read the original post

Course Characteristics

Course Content

Week 1: Business & Data Understanding
Week 2: Data Preparation
Week 3: Modeling (1)
Week 4: Modeling (2)
Week 5: Evaluation
Week 6: Deployment & Maintenance
Week 7: Final Exam

Target Audience

Anybody with a basic understanding of data, data analysis, and simple mathematics.

Additional Content Contributor

John MacGregor

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John MacGregor graduated with a Master’s Degree in Operations Research, after which he spent several years as a management consultant in Unilever’s UK Operation Research Group. John then joined the University of North London as a senior lecturer in Statistics and Operations Research. At the same time, he worked with the first BI products and OLAP servers to be created. John has a strong business background, having started and run his own software distributorship company in Australia as well as taking on the role of managing director for Pilot Software and Gentia Software, ANZ and UK.

Within SAP, John worked as the Australia & NZ regional director for Crystal Decisions before returning to the UK to lead the data mining developments of Crystal Decisions and then BusinessObjects. He is currently a member of the data science team in IoT Predictive Maintenance, part of LOB Digital Assets & IoT, with the official title of VP, Predictive Analysis. John created and delivers the Data Science 101 course for the SAP Development University. He is also the author of “Predictive Analysis with SAP – The Comprehensive Guide”, published by SAP Press.

Contenu du cours

  • Week 1:

    Getting Started with Data Science
  • Week 2:

    Data Preparation
  • Week 3:

    Modeling (1)
  • Week 4:

    Modeling (2)
  • Week 5:

    Evaluation
  • Week 6:

    Deployment & Maintenance
  • I Like, I Wish:

    We Love Your Feedback … And Want More
  • Final Exam:

    Good Luck!

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Ce cours a eu lieu du 1 fév. 2017 au 23 mar. 2017.

11083 apprenants étaient inscrits le jour 1 du cours.

Lorsque l'examen final était terminé, ce nombre avait augmenté à 17592.

33296 apprenants sont actuellement inscrits.

Rating

This course was rated with 4.32 stars in average from 1453 votes.

Certificate Requirements

  • Obtenez un certificat de réussite en gagnant plus de 50% du nombre maximal de points pour la somme de toutes les tâches hebdomadaires.
  • Obtenez une attestation de participation en complétant au moins 50% du matériel du cours.

En savoir plus lisez les lignes directrices pour le certificat.

Ce cours est offert par

Stuart Clarke

Stuart Clarke is a principal consultant in Global Consulting Delivery, Analytics and Insight, focusing on predictive analytics and data science.

He has specialized in operational research, data science, predictive analytics, and advanced analytics for over 25 years, working extensively in the telecommunications, retail, utilities, and financial services sectors.

At SAP, Stuart provides predictive analytics consultancy, developing and implementing predictive models for SAP customers and delivering POCs for customers and prospects. He also delivers introduction courses for SAP Predictive Analytics and deep-dive technical sessions to SAP customers, partners, and internals worldwide.

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