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Statistical Thinking for Data Science

创建日期：2024-09-06 16:23:51

所属分类：经济Economics

标签： Statistical

Statistical Thinking for Data Science

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Statistical Thinking for Data Science

Subject description

Statistical thinking is the foundational mindset in data science, emphasizing the use of statistical principles and methods to understand, analyze, and derive meaningful insights from data. It serves as the core of data science. This subject equips students with essential skills and concepts for applying statistical thinking in the context of applied data science. Initially, students are introduced to fundamental statistical principles, developing a simultaneous understanding of modern methods for statistical inference, and gaining valuable hands-on experience with real-world data. Subsequently, they delve into a range of statistical models and estimation techniques, applying their acquired knowledge to engage in a complete data science research cycle. Collaborating in teams, students learn how to formulate research inquiries, employ formal statistics and real-world datasets to address them, and effectively communicate their findings through both oral presentations and written reports.

The progression of this subject starts with more teaching-intensive methods such as workshops and lectures to give students the technical and conceptual know-how to work as practicing data scientists. As the subject progresses, students increasingly move towards an individually driven learning mode, allowing both teams and individuals the flexibility to enhance their statistical thinking and skills.

Upon completion of the subject, students possess a robust foundation in technical, conceptual, and practical aspects, empowering them to continue their development as Data Scientists.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Manage the complexity of real data science projects and their inevitable compromises

2. Formulate authentic data science questions precise enough to be answered by valid statistical techniques

3. Justify the use of different statistical concepts and tools to audiences from a wide range of backgrounds

4. Find, clean, and merge datasets from a range of sources to answer real world data science problems

5. Apply statistical methods that are appropriate to a dataset and stakeholder requirements

6. Interpret the results of a statistical analysis correctly, visualizing and reporting upon them in ways that create value for, and are sensitive to the needs of, a wide range of stakeholders

7. Collaborate with and contribute to the professional community of data scientists, both local and global

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following course outcomes:

Exploring and testing models and describing behaviours of complex systems

Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)

Making the invisible visible

Use transdisciplinary approaches to seeing and doing to uncover underrepresented, or misrepresented, elements of a system (1.4)

● Exploring, interpreting and visualising data

Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)

. Designing and managing data investigations

Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components (2.4)

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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