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Introduction to Decision Intelligence using Data
- 1. DECISION INTELLIGENCE USING DATA Ngee Ann Polytechnic School of Interdisciplinary Studies
- 2. Official (Closed) - Non Sensitive Data for Decision Intelligence Programme Module 1 Data Wrangling & Statistics for Data (blended with DataCamp) Module 2 Visualization of Data with R & Tableau Module 3 Machine Learning Modelling for Decision Intelligence Module 4 Design Thinking Mindset for Data Science & Capstone Projects
- 3. Data Wrangling & Statistics for Data • Learn the programming language most suitable for exploratory data analysis. • Learn to use a computational tool so that you can easily make sense of the statistics for data. • Enjoy a blended learning approach using premium online content from DataCamp and obtain a Statement of Accomplishment.
- 4. What you will learn • Basics of R. Use of RStudio and RMarkdown. Use R packages and libraries • Manipulate and clean data Use Tidyverse dplyr package • Learn statistics • Be supported by Enterprise licensed learning from DataCamp • Exploratory data analysis. Generate observations and insights from data
- 5. Official (Closed) - Non Sensitive
- 6. Official (Closed) - Non Sensitive
- 7. What you will learn • Basics of R. Use of RStudio and RMarkdown. Use R packages and libraries • Manipulate and clean data Use Tidyverse dplyr package • Learn statistics • Be supported by Enterprise licensed learning from DataCamp • Exploratory data analysis. Generate observations and insights from data
- 8. Visualisation of Data with R & Tableau • Learn from a uniquely different course that allows you to dive deep into both R and Tableau for data visualization. • Transform your data into dashboards and interactive maps. • Create insightful visuals that tell stories.
- 9. Official (Closed) - Non Sensitive Ben Whalley, Layered graphics with ggplot, accessed 23 July 2020, <https://benwhalley.github.io/just-enough-r/layered-graphics.html>
- 10. Official (Closed) - Non Sensitive Tableau, 5 stylish chart types that bring your data to life by Lucas Steward, 30 Dec 2014, accessed 23 July 2020, <https://www.tableau.com/about/blog/2014/12/5-chart-types-youve-never-tried-tableau-35281>
- 11. Official (Closed) - Non Sensitive What you will learn • Data visualisation with R using ggplot2, grammar of graphics and common plot types. • Build visualisation dashboards and interactive plots. • Data presentation with Tableau • Create plots to tell data-driven stories and provide regular insights
- 12. Machine Learning Modelling for Decision Intelligence • Acquire proficiency in machine learning algorithms that can be harnessed in the workplace • Evaluate modelling metrices to improve workplace performance. • Obtain the skills from subject mastery instead of broad-based knowledge.
- 13. What you will learn • Regression modelling • Tree modelling • Other modelling techniques such as KNN, time series, Naïve Bayes etc. • Metrices for model evaluation, tuning and validation
- 14. Design Thinking Mindset for Data Science & Capstone Projects • Learn how to integrate human-centred insights with decision-making insights gained from data. • Build a data science project with machine learning techniques. • Learn how to make robust decisions in complex situations by using data.
- 15. Official (Closed) - Non Sensitive What you will learn • Acquire a design thinking mindset to target your projects towards viable solutioning that meets your user’s needs. • Understand the design thinking process and investigate case studies relevant to data science • Identify the need for decision intelligence in your data science projects • Start building data science projects
- 16. ARE YOU READY?
Editor's Notes
- With the ongoing digital revolution and advancements in technology, organizations can dramatically improve their effectiveness by collecting and analysing insights from relevant data. This Data for Decision Intelligence Programme will equip you with the programming tools used to analyse and visualize data. You will learn about and apply concepts related to statistics for data, machine learning algorithms and design thinking. While computer systems can execute calculations meticulously, this course aims to develop participants who are capable of leading data-led projects responsibly, through the use of decision intelligence to turn information into better actions.
- This programme comprises of 4 modules: 1. Data wrangling & statistics for data blended with Datacamp asynchronous online (90 h) 2. Visualization of data with R & Tableau (90h) 3. Machine learning modelling for decision intelligence (90 h) 4. Design thinking mindset for data science and capstone projects. (60 h) You will obtain a Data for Decision Intelligence Programme Certification upon completion of all four modules.
- The first module of data wrangling and statistics for data will enable you to learn the programming language most suitable for exploratory data analysis. You will learn to make sense of the statistics for data. This module is uniquely designed to offer a blended learning approach using the premium online content from DataCamp to offer asynchronous learning complementary to the instructions given by our trainers.
- You will learn the programming language R.
- Use Rstudio.
- Make use of Rmarkdown to convert your scripts into shareable formats.
- You will also explore the various R packages and libraries. Manipulate and clean data. Statistics will be meaningful at this stage as you obtain the skills to explore and gather insights from data analysis.
- The second module of this course will allow you to obtain inspiration from the data by using suitable visualisation tools. This is a uniquely different course that teaches two of the most versatile visualisation tools used by data analysts.
- Visualisation techniques with R using ggplot 2,
- and Tableau will be taught.
- You will learn to plot interpretable graphs, build them into dashboards so that they can be used to provide regular insights and make interactive maps. You will then be able to create insightful visuals that tell stories.
- The third module prepares you to use data for prediction and forecasting purposes. You will learn the concepts of machine learning and the algorithms used for different classes of data.
- You will learn regression modelling algorithms that include Linear regression, logistic regression, penalised regression We will explain what Random forest is. You will also pick up other machine learning algorithms such as KNN, time series and Naïve Bayes. Acquiring machine learning techniques will enable you to make predictions from data that are both numerical and categorical. You will learn to work with data of various patterns and behaviours, as well as, be able to tune your parameters to obtain the desired targets as you improve your modelling decisions.
- The fourth module of our programme is targeted to bring data exploration back to its intended purposes. The goal of harnessing a design thinking mindset aims to integrate data science projects that emerge in human-centric solutioning.
- You will look at design thinking case studies relevant to data science projects. You will find out what decision intelligence encompasses. Are decisions driven by data or should decision-making takes precedence for data-led projects? In this final module, you will use the knowledge and skills acquired from the previous three modules to start your portfolio of data science projects that is so essential in your commitment to showcase data competency.
- Join the Data for Decision Intelligence programme to widen the scope of your career opportunities.