Programme Outline
Learning Objectives
- Data Management
- Business Case and Data Acquisition
- Data Preparation
- Data Analytics
- Data Modelling
- Data Visualisation
- Decision Making
Part 1 – Data Science and Business Case Study
- This segment deals with the why and what to get participants up to speed with common terms used in Data Science and aims to answer:
- What is Data Science exactly?
- Steps in a typical Data Science lifecycle
- Who are the stakeholders?
- Where does data come from?
- How can one create a Data Dictionary from a common tool like spreadsheet?
- How can I apply Data Science at work?
Clearly stating the Business Case and the objectives for the Data Science task is a prerequisite for efficient and effective data analytics. It serves to avoid unnecessary work and frustration during data acquisition and during consecutive steps. The Business Case drives the selection of appropriate data acquisition methods and sources. This also helps to keep Data Preparation, Data Analysis, Data Science Modelling and Algorithm and Data Visualisation in scope.
Part 2 – Data Acquisition
- The art of acquiring data have never been so important than before, in particular when big data sets in the new paradigm for data-centric business. From multi-channel data acquisition to defining various sources of data such as commercial or public or even governmental dataset, never have we faced such an explosive growth of data, yet we have not been able to potentially set foot onto a new planet full of data that is untapped. Businesses with the inclination towards a data-first approach as part of their drive towards efficiency would definitely help them to envision a new way of conducting their traditional business whether is capitalising on public tweets from social media to understand the consumer trend or the amalgamation of various types of data such as structured and unstructured data.
Part 3 – Data Preparation
- Data comes in all shapes and sizes, it can be unstructured or structured. Data pre-processing helps to format the existing data set from a raw form into a more interpret-able form of data set consumable for Data Analysis and Algorithm.
Part 4 – Data Analytics
- Data analytics is the core of Data Science. Participants will understand the involvement of Analytics in Data Science. By understanding the science of analysing raw data to make well-informed choices around the meaningful information, it can help professionals and aide front-liners in understanding how their roles can make an upstream impact in within the Data Science pipeline, even when they are not directly part of the Data Analytics team. Participants will learn Data Analysis and Visualisation with Excel.
Part 5 – Concepts of Machine Learning and Algorithms
- Participants will appreciate the role of Machine Learning and Algorithms within a mature organisation which capitalize on the strengths of Machine Learning. Participants will learn simple Data Science Modelling and Algorithm such as Simple Linear Regression in Excel only.
Part 6 – Data Visualisation
- Data visualisation is beyond just graphs and charts. It’s about communicating data in a visual, concise and meaningful manner to stakeholders. Good data visualisations should help them draw conclusions faster and more solid. Participants will learn how to apply data visualisation with Excel.
Part 7 – Decision Making
- Data Analysis and Data Science Modelling and Algorithm as well as the visual presentation of these results are the basis for decision making. Translating these results and visuals into business language and using it for data storytelling are vital tasks for every Data Scientist. After that, stakeholders are able to make data-driven decisions easily and confidently.
Assessment
- Participants to complete a simple assessment to evaluate course understanding and augment learning outcomes.