Data Science & Analytics for Strategic Decisions

Harness the power of data science to uncover actionable insights and transform your organisation’s decision-making

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Course Dates

STARTS ON

15 December 2021

Course Duration

DURATION

2 months, online
4-6 hours per week

Course Duration

PROGRAMME FEE

US$1,500

Course Information Flexible payment available
Course Information Special group enrolment pricing

Enjoy a special holiday offer by applying today! Use code SMU150EBTA during payment to receive US$ 150 off the programme fee.

Have a chat with us - +65 3129 5093 (Monday-Friday) (11:30 AM - 06:30 PM Singapore time)

Why Enrol in Data Science & Analytics for Strategic Decisions?

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

22%

is the expected rise in employment of data scientists by 2030 - much faster than the average for all occupations.

SOURCE: US Bureau of Labor Statistics, 2021

95%

of businesses cite the need to manage unstructured data as a problem for their business.

SOURCE: SharesPost, 2019

Programme Faculty

Faculty Member Sandeep R. Chandukala, PhD

Sandeep 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... More info
Faculty Member Michelle Cheong, PhD

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... More info

Programme Highlights

90+ Video Lectures

90+ Video Lectures

32 Assignments

32 Assignments

10+ Industry Examples

10+ Industry Examples

6 Discussion Boards

6 Discussion Boards

6 Case Studies

6 Case Studies

2 Simulations

2 Simulations

Programme Modules

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.

Module 1:

Leveraging Data as a Competitive Edge

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.

Module 2:

Data Analytics in Action

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.

Module 3:

Basic Statistics for Data Analysis

Gain a deeper understanding of comparison of independent data sets to obtain insights, and how to apply strategic decision-making using said techniques.

Module 4:

Predictive Analytics

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.

Module 5:

Field Experiments and Causality

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.

Module 6:

Machine Learning Models for Data Analytics

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.

Module 7:

Addressing Key Challenges and Risks in Data Science Projects

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.

Module 8:

Data Science and the Future

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.

Module 1:

Leveraging Data as a Competitive Edge

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.

Module 5:

Field Experiments and Causality

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.

Module 2:

Data Analytics in Action

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.

Module 6:

Machine Learning Models for Data Analytics

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.

Module 3:

Basic Statistics for Data Analysis

Gain a deeper understanding of comparison of independent data sets to obtain insights, and how to apply strategic decision-making using said techniques.

Module 7:

Addressing Key Challenges and Risks in Data Science Projects

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.

Module 4:

Predictive Analytics

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.

Module 8:

Data Science and the Future

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.

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Industry Examples

Through real-world examples from multiple industries, you'll see how the theories are put into practice.

Facebook

Facebook

Understand how proprietary data was collected by Facebook for targeted advertising.

Tesla

Tesla

Learn about how Tesla uses Recurrent Neural Networks(RNN) in autopilot cars.

Google

Google

Understand how AI runs Google Adwords to decide advertisement ranking.

Tinder

Tinder

Learn how Tinder uses the data-enabled network effect for New Smart Photo algorithm.

Walmart

Walmart

Identify how Walmart uses historical data for data-driven predictive technology to predict shopping pattern during natural calamities.

Pandora

Pandora

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.

Case Studies

The Weather Company: Creating Consumer Apps that Leverage Big Data

The Weather Company: Creating Consumer Apps that Leverage Big Data

Iuiga’s Challenge: Is Omni-Channel Worth It?

Iuiga’s Challenge: Is Omni-Channel Worth It?

3M Moves to Customer Focus Using a Global Data Warehouse

3M Moves to Customer Focus Using a Global Data Warehouse

Advertising Experiments at RestaurantGrades

Advertising Experiments at RestaurantGrades

Predicting Customer Churn at QWE Inc

Predicting Customer Churn at QWE Inc

Digital Transformation of the Certis Group

Digital Transformation of the Certis Group

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.

Data Analytics Simulation: Strategic Decision-Making

Data Analytics Simulation: Strategic Decision-Making

Digital Marketing Simulation: Media Attribution at ExerciseMinder

Digital Marketing Simulation: Media Attribution at ExerciseMinder

Who Is This Programme For?

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:

  • Transition into a data-centric senior management role
  • Gather analytical expertise to handle greater responsibilities
  • Utilise predictive models to build effective strategies that address key issues in business operations and product quality
  • Become a leader for sustainable business growth
  • Spearhead complete ownership of key business tasks and understand underlying strategic implications

What You Will Learn in the Programme?

  • Create and implement business strategies leveraging data science.
  • Make data-driven decisions to solve business problems using data insights.
  • Demonstrate how analytics can be combined with experiments to make data-informed recommendations for business growth.
  • Explain the key challenges and risks in data science projects.
  • Evaluate an organisation’s data strategy and recommend ways to achieve a sustainable competitive advantage.
  • Analyse organisational needs and drive business improvement through data science future trends.

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Upon successful completion of the programme, participants will be awarded a verified digital certificate by Singapore Management University.

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All certificate images are for illustrative purposes only and may be subject to change at the discretion of Singapore Management University.

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Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more.