The goal of this class is to develop a working knowledge of data science and its use in earth systems research and practice. The course is split evenly between key concepts / theory and practical experience writing code and analyzing outputs. Some introductory knowledge of statistics is assumed, and some familiarity with programming is helpful but not required.
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | ES2001, MH1800, MH1802, CY1601, CY1602 |
| Not Available To Programme | ACBS, ACC, ADM, AERO, AISC, ARED, ASEC, BACF, BASA, BCE, BCG, BEEC, BIE, BMS, BS, BSB, BSPY, BUS, CBE, CBEC, CE, CEE, CEE 1, CEEC, CHEM, CHIN, CMED, CNEL, CNLM, COMP, CS, CSC, CSEC, CVEC, DSAI, ECMA, ECON, ECPP, ECPS, EEE, EEE 1, EEEC, ELAH, ELH, ELHS, ELPL, ENE, ENE 1, ENEC, ENG, HIST, HSCN, HSLM, IEEC, IEM, LMEL, LMPL, LMS, MACS, MAEC, MAEO, MAT, MATH, ME 1, ME(DES), ME(IMS), ME(NULL), ME(RMS), MEEC(DES), MEEC(IMS), MEEC(NULL), MEEC(RMS), MS, MS-2ndMaj/Spec(MSB), MTEC, PESC, PHIL, PHY, PLCN, PLHS, PPGA, PSLM, PSMA, PSY, REP, ROBO, SCED, SOC, SPPE, SSM |
| Not Available To All Programme With | Yr1, Yr2 |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | |
| Not Offered As BDE | |
| Not Offered As Unrestricted Elective | |
| Exam |
Total hours per week: 3 hrs
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |
Other offerings
AY24/25
AY23/24
AY22/23
AY21/22
AY20/21
AY19/20
AY18/19