The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Harness the power of data science to uncover actionable insights and transform your organisation’s decision-making
1 September 2022
2 months, online
4-6 hours per week
US$1,600 or get US$240 off with a referral
Our participants tell us that taking this programme together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Data Science & Analytics for Strategic Decisions starting on 1 September 2022 .
We’ve sent you an email with enrolment next steps. If you’re ready to enrol now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.
Apply before 17 August 2022 to enjoy US$100 off the programme fee.
Use code SMU100TA during payment.
WhatsApp an Advisor on +65 8014 3066
Have questions? Our Advisor will assist you promptly.
90+ Video Lectures
32 Assignments
10+ Industry Examples
6 Discussion Boards
6 Case Studies
2 Simulations
Learners will get hands-on experience running various data analysis methodologies and also free access to XLSTAT for a year along with this programme.
The programme comprises 8 modules. Each module is led by an SMU faculty expert with field experience specific to the Data Science & Analytics topics being discussed.
Learn the key terminologies of data science, different levels of data analytics and their significance to decision-making, data features and insights to attain sustainable competitive advantage, and the applications of data analytics and its role in creating new business opportunities.
Discover the appropriate analytical approach to resolve a business problem, whether your organisation is data-driven, trends in data and obtaining related insights to enhance business performance, the impact an organisation’s omnichannel strategies have on sales, and how to identify appropriate data/insights.
Gain a deeper understanding of comparison of independent data sets to obtain insights, and how to apply strategic decision-making using said techniques.
Learn the basics of regression to analyse the strength/impact of variables, how to predict variable impact using optimal model fit and regression effects, how to build a logistic regression model to test and predict expected outcomes, and how to apply predictive analytics to organisational events to advance strengths and counter threats.
Explore correlation and causality and their significance to enhancing business performance, experimentation for business problems to make effective inferences; Multivariate, A/B and Multi-Armed Bandit testing; and the effectiveness of using experimental design to make data-informed recommendations for business growth.
Build your knowledge of machine learning and its role in driving organisational productivity, how machine learning algorithms can be applied to achieve optimal analytical accuracy, the programme-building facets of neural networks and deep learning, and how analytics can be combined with experiments to produce effective business strategies.
Learn the key challenges to data science projects and their solutions, the Delta Framework and Delta Plus Model, project-level risks and examples of failed data science projects, and how to predict the success of your big data project using the DATA technique.
Dive into the drivers, expected outcomes, and technology enablers for Industry 4.0; the components for AI success that can be leveraged to strengthen organisational capabilities; challenges in the implementation of AI in systems; and how to evaluate an organisation’s digital transformation journey and sustain a competitive advantage.
Learn the key terminologies of data science, different levels of data analytics and their significance to decision-making, data features and insights to attain sustainable competitive advantage, and the applications of data analytics and its role in creating new business opportunities.
Explore correlation and causality and their significance to enhancing business performance, experimentation for business problems to make effective inferences; Multivariate, A/B and Multi-Armed Bandit testing; and the effectiveness of using experimental design to make data-informed recommendations for business growth.
Discover the appropriate analytical approach to resolve a business problem, whether your organisation is data-driven, trends in data and obtaining related insights to enhance business performance, the impact an organisation’s omnichannel strategies have on sales, and how to identify appropriate data/insights.
Build your knowledge of machine learning and its role in driving organisational productivity, how machine learning algorithms can be applied to achieve optimal analytical accuracy, the programme-building facets of neural networks and deep learning, and how analytics can be combined with experiments to produce effective business strategies.
Gain a deeper understanding of comparison of independent data sets to obtain insights, and how to apply strategic decision-making using said techniques.
Learn the key challenges to data science projects and their solutions, the Delta Framework and Delta Plus Model, project-level risks and examples of failed data science projects, and how to predict the success of your big data project using the DATA technique.
Learn the basics of regression to analyse the strength/impact of variables, how to predict variable impact using optimal model fit and regression effects, how to build a logistic regression model to test and predict expected outcomes, and how to apply predictive analytics to organisational events to advance strengths and counter threats.
Dive into the drivers, expected outcomes, and technology enablers for Industry 4.0; the components for AI success that can be leveraged to strengthen organisational capabilities; challenges in the implementation of AI in systems; and how to evaluate an organisation’s digital transformation journey and sustain a competitive advantage.
Through real-world examples from multiple industries, you'll see how the theories are put into practice.
Understand how proprietary data was collected by Facebook for targeted advertising.
Learn about how Tesla uses Recurrent Neural Networks(RNN) in autopilot cars.
Understand how AI runs Google Adwords to decide advertisement ranking.
Learn how Tinder uses the data-enabled network effect for New Smart Photo algorithm.
Identify how Walmart uses historical data for data-driven predictive technology to predict shopping pattern during natural calamities.
Understand how Pandora uses data insights to improve their service/product offerings.
Other companies that are included as examples or as use cases in the programme include Apple, Netflix, Google Trends, Spotify, Microsoft etc.
Note: All brand, product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with, endorsement by, or association of any kind between them and programme owners.
Enterprises across the globe are shifting their focus to data-driven goals and decision-making. In fact, the International Data Corporation reports that worldwide data will grow 61% to 175 zettabytes by 2025*. So, why is data science so important? Because it enables organisations to efficiently process and interpret data that can be used to make informed business decisions & drive growth, optimisation and performance.
In the online Data Science & Analytics for Strategic Decisions programme—offered by Singapore Management University—you can learn how to process and understand data that can be used to drive better, smarter decisions within your organisation.
*Source: IDC, 2021
is the expected rise in employment of data scientists by 2030 - much faster than the average for all occupations.
of businesses cite the need to manage unstructured data as a problem for their business.
Associate Professor of Marketing
Sandeep serves as Associate Professor of Marketing. Prior to joining SMU, Sandeep worked at 3M and before that he worked as a Jr Faculty Fellow at the Indiana University’s Kelley School of Business. He has a PhD in Marketing (with a minor in Statistics) from The Ohio State University... More info
Professor of Information Systems (Education); Associate Dean, SCIS Post-Graduate Professional Education; Director, Doctor of Engineering
Professor Cheong has served a variety of academic appointments at SMU since 2005, including as a Lecturer, Assistant Professor and Associate Professor of Information Systems... More infoSandeep R. Chandukala, PhD
Associate Professor of Marketing
Sandeep serves as Associate Professor of Marketing. Prior to joining SMU, Sandeep worked at 3M and before that he worked as a Jr Faculty Fellow at the Indiana University’s Kelley School of Business. He has a PhD in Marketing (with a minor in Statistics) from The Ohio State University, MS (MAS) and MBA from University of Texas at Dallas, and MS (Computer Engineering) from University of Minnesota.
Associate Professor Chandukala’s research interests are related to developing quantitative models of consumer behaviour using industrial data. His research primarily focuses on retail analytics. Specifically, understanding and measuring the impact of promotions, advertising and new products and proposing new approaches for market segmentation using Bayesian and Markov Chain Monte Carlo (MCMC) methods. His research has appeared in Marketing Science, Journal of Marketing, Journal of Retailing, Marketing Letters and Customer Needs and Solutions. Associate Professor Chandukala was awarded Lee Kong Chian Research Fellowship in 2016-17 and was also in the Dean’s Teaching Honour List for Postgraduate Teaching in 2018.
Michelle Cheong, PhD
Professor of Information Systems (Education); Associate Dean, SCIS Post-Graduate Professional Education; Director, Doctor of Engineering
Professor Cheong has served a variety of academic appointments at SMU since 2005, including as a Lecturer, Assistant Professor and Associate Professor of Information Systems. In addition to her current role as Professor of Information Systems, Professor Cheong also holds administrative positions at SMU, serving as an Associate Dean of SIS Post-Graduate Professional Education and as Director of Doctor of Engineering.
Professor Cheong’s research interests include data and decision analytics, spreadsheets modeling and pedagogy, and learning analytics and text mining. In 2018, she was awarded the SMU Teaching Excellence Award - Postgraduate Professional Programmes by the SMU Centre of Teaching Excellence.
Professor Cheong’s work has been featured in a number of journal articles, books and book chapters, conference proceedings and papers, and magazine articles. Her latest work on the impact of peer helper training at SMU was published by the International Journal of Evidence Based Coaching and Mentoring.
The programme is designed for both tech and non-tech professionals with 6 – 20+ years of relevant work experience—No coding is required; however, a basic knowledge of Excel would be beneficial. Industries and Functions that can benefit include:
Industries:
IT, E-Commerce, Computer Software, Finance, Marketing and Advertising, Banking, Education Management, and Management Consulting
Functions:
Engineering, Programming, Technology, General Management, Marketing, Finance, Operations, and HR Functions
This programme is especially helpful for professionals who aspire to:
Upon successful completion of the programme, participants will be awarded a verified digital certificate by Singapore Management University.
Download BrochureAll certificate images are for illustrative purposes only and may be subject to change at the discretion of Singapore Management University.
Flexible payment options available.
