Analytics I Visual Predictive Techniques
AY2019/2020 Semester 1
Most organizations are data rich and information poor. The large volumes of data in an organization are 'oilfields' rich in information content that are pending extraction with the right tools and models. Analytics involves the art of data exploration, visualization, communication and the science of analyzing large quantities of data in order to discover meaningful patterns and useful insights to support decision-making. The primary objective of this course is to introduce students to various techniques available to extract useful insights from the large volumes of data. At the end of the course, students will not only see the substantial opportunities that exist in the business analytics realm, but also learn techniques that allow them to exploit these opportunities. This course focus on the use of open source R software, which is one of the key analytics software used in various industries and a critical skillset required in the job market for analytics and data science professionals. 1. Data Analytic Thinking 2. Data Exploration 3. Statistics for Analytics 4. Data Structures 5. Data Visualization 6. Data Cleaning 7. Data Imputation 8. Linear Regression 9. Classification and Regression Trees 10. Clustering 11. Text Mining
| AUs | 4.0 AUs |
| Categories | CoreBDE |
| Not Available To Programme | ACBS-2ndMaj/Spec(IT)(2014-onwards), ACC-2ndMaj/Spec(PFA) 2, ADM, AERO, ARED, ASEC, BCE 3, BCE 4, BCG 3, BCG 4, BEEC, BIE, BMS, BS, BSB, BSPY, CBE, CBEC, CE, CEE, CEEC, CHEM, CHIN, CS, CSC, CSEC, CVEC, DSAI, ECMA, ECON, ECPP, ECPS, EEE, EEEC, EESS, ELAH, ELH, ENE, ENEC, ENG, ESPP, HIST, IEEC, IEM, LMS, MACS, MAEC, MAEO, MAT, MATH(AMAS), MATH(BA), MATH(PMAS), MATH(STAT), ME(DES), ME(NULL), ME(RMS), MEEC(DES), MEEC(NULL), MEEC(RMS), MS, MS-2ndMaj/Spec(MSB), MTEC, PHIL, PHY, PPGA, PSLM, PSMA, PSY, REP, SCED, SOC, SSM |
| Mutually Exclusive With | BC3404 |
| Exam |
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 830 | 00626 SEM (1) 0830-1230 Tue S3-SR3, FTR2 , Wk9,10 | 00626 SEM (1) 0830-1230 Tue S3-SR3, FTR2 Wk9,10 | |||||||||
| 900 | |||||||||||
| 930 | |||||||||||
| 1000 | |||||||||||
| 1030 | |||||||||||
| 1100 | |||||||||||
| 1130 | |||||||||||
| 1200 | |||||||||||
| 1230 | |||||||||||
| 1300 | |||||||||||
| 1330 | |||||||||||
| 1400 | |||||||||||
| 1430 | 00631 SEM (6) 1430-1830 Mon FTR3, S4-SR13 Wk9,10 | 00631 SEM (6) 1430-1830 Mon FTR3, S4-SR13 Wk9,10, | 00627 SEM (2) 1430-1830 Tue LHS-TR+52, ITL-SA3 , Wk9,10 | 00627 SEM (2) 1430-1830 Tue LHS-TR+52, ITL-SA3 Wk9,10 | 00628 SEM (3) 1430-1830 Wed S3-SR4, ITL-SA3 , Wk9,10 | 00628 SEM (3) 1430-1830 Wed S3-SR4, ITL-SA3 Wk9,10 | 00629 SEM (4) 1430-1830 Wed S3-SR6, FTR3 , Wk9,10 | 00629 SEM (4) 1430-1830 Wed S3-SR6, FTR3 Wk9,10 | 00630 SEM (5) 1430-1830 Thu S3-SR4, FTR , Wk9,10 | 00630 SEM (5) 1430-1830 Thu S3-SR4, FTR Wk9,10 | |
| 1500 | |||||||||||
| 1530 | |||||||||||
| 1600 | |||||||||||
| 1630 | |||||||||||
| 1700 | |||||||||||
| 1730 | |||||||||||
| 1800 | |||||||||||