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IS217

Analytics Foundation

1 CreditsBoth

Description

The term “Analytics” has been around in the business settings for a while now, where past results have been used to guide and improve future performance of business. More recently, enhancements in technology have enabled the business world to produce and store very large amounts of data which needs to be processed, managed and analysed in order to uncover its hidden value. There is a real dearth of analytical talent needed to perform this task. This course aims to introduce students to the fundamental skills needed to get started with analytics. This course will help them build a foundation needed for advanced analytics by introducing them to data exploration techniques, data preparation methodologies, applying key analytics techniques and use them in formulating a business problem and identifying the correct analytical approach to solve it.

Requisites

Prerequisites: IS200/IS111/SMT111/CS101/COR-IS1704 - Pre-req

Co-requisites: STAT101/102/151/COR-STAT1202/CS105 - Co-req

Anti-requisites: IS217/ MGMT108 - Mutually Exclusive

Attributes

Department: SCIS

Course Level: Undergraduate

Tracks: IS/T4BS: Business Analytics Track IS/T4BS: Smart-City Management & Technology Track

Areas: Advanced Business Technology Major Analytics Major Business Options Digital Business Electives Econ Major Rel/Econ Options Grad Req - Dig Tech/Data Ana (Intake 2024 onwards) IS Depth Electives IT Solution Development Electives Smart-City Mgmt & Tech Core (Intake 2019 to 2021) Smart-City Mgmt & Tech Core (Intake 2022 onwards) Smart-City Mgmt &Tech Core (Intake 2018 & earlier) Social Sciences/PLE Major-related Technology & Entrepreneurship

Learning Outcomes

1. Clean the Data given and note data discrepancies 2. Handle Missing information and outliers in the variables given 3. Visualise the data using Tableau with appropriate charts 4. Prepare the data for clustering 5. Develop a K means clustering model 6. Prepare the data for Multiple Linear Regression 7. Develop a MLR model 8. Apply Market Basket Analysis to purchase data 9. Build classification models using Decision Trees 10. Interpret the results of analysis 11. Make appropriate business recommendations 12. Effectively use Python for data exploration and data manipulation 13. Use Machine Learning modules in python to build analytical models

Graduate Learning Outcomes

Disciplinary Knowledge, Critical thinking & problem solving

Competencies

Data Analytics, Data Design, Data Engineering, Data Visualisation, Pattern Recognition Systems

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