Statistics across the curriculum: are we preparing young people for the data revolution? The Royal Statistical Society 4th July 2016 1 Statistics in the new qualification landscape Speakers Paul Glaister, Professor of Mathematics and Mathematics Education, University of Reading
Andy Noyes, Professor of Education and Head of School, University of Nottingham Alison Tonkin, Senior Officer Standards, Ofqual 2 Statistics across the curriculum: Are we preparing young people for the data revolution? Policy context: statistics in Englands new qualification landscape Paul Glaister University of Reading/ALCAB/ALMAB/Ofqual/CMSP/IMA/JMC (Chair)
NOTE! For Mathematics read Mathematical Sciences = Statistics + The importance of mathematics Children with high mathematics scores at age 10 earn 7.3% more at age 30 than others, even after pupil characteristics & later qualifications are controlled for
Those with STEM Alevels 8.5% more than similarly qualified workers without STEM A levels 9% wage premium for holding a STEM degree compared to other subjects Earn 19% more than workers in
other occupations Work in a STEM occupation Degree-level STEM STEM A-level London Economics 2015 Maths age 11 Institute of Fiscal Studies, British Cohort Study 2012
Office for National Statistics (2010) Annual Survey of Hours and Earnings (ASHE) Office for National Statistics (2010) Annual Survey of Hours and Earnings (ASHE) Council for Mathematical Sciences: Driving the UK Economy Council for Mathematical Sciences: Driving the UK Economy
Council for Mathematical Sciences: Driving the UK Economy Council for Mathematical Sciences: Driving the UK Economy The Royal Society: Vision for Science and mathematics education for 2030 The Royal Society: Vision for Science and mathematics education for 2030 GCSE Mathematics 2015 statistics content GCSE Mathematics assessment objectives
GCSE Mathematics subject content, conditions, guidance and assessment objectives Mathematics GCSE subject content and assessment objectives Mathematics GCSE subject level guidance Improving standards: GCSE As part of the governments plans to ensure pupils can compete with the top performers in the world and secure the best jobs, a new grading system is being introduced from 2017 at GCSE to
replace the A to U system with a new 9 to 1 scale. Under the new system, a good pass, currently a C grade, will become a grade 5 under the new scale. The new good pass is comparable to a high C or low B under the current system. Post-16 GCSE 16 to 19 funding: maths and English condition of funding The student What a student must
study to meet the condition of funding Full time student (540 hours or more 16 to 17, or 450 hours or more 18+) with GCSE ONLY GCSE grade D Full time student (540 hours or more aged 16 to 17, or 450 hours or more 18+) Either GCSE or stepping with GCSE grade E or below, or no GCSE stone qualification Post-16 GCSE The government intends to align the 16 to 19 maths and English funding condition with the new GCSE good pass in maths and English. A phased approach will be taken. For
students studying in academic years 2017 to 2018 and 2018 to 2019 the funding condition will be based on the new GCSE grade 4. Beyond this, we intend to revise the funding condition to reflect the new GCSE good pass (grade 5). The specific date from which this will take effect will be confirmed closer to the time. Level 3 mathematics progress has been good Sustained growth in the number of students progressing to AS and A level mathematics
Post-16 Mathematics What have A*-C GCSE maths students achieved by age 18? (2011 GCSE cohort) Other L3; 0.95% We estimate that of those entering higher education in any year, some 330,000 students would benefit from recent experience of studying some mathematics (including statistics) at a level beyond GCSE, but fewer than 125,000 have done so. A level FM; 2.12%
A level maths; 11.90% AS maths ; 5.59% (ACME, Mathematical Needs, 2011) No change ; 79.45% Sources: DfE analysis of pupil-level data Many undergraduate students are surprised at the amount of mathematical content in their degree programmes and some struggle to cope with this content..
(Higher Education Academy, Mathematical Transitions, 2014) Qualifications: AS/A levels (from 2017) Key aims: Better support transition to mathematical study at university. Key changes: A level maths Specified in more detail to clarify requirements. 100% prescribed, giving universities confidence about the maths that undergraduates have studied.
Compulsory applied content in statistics (and mechanics) and removal of decision maths. An emphasis on problem-solving, mathematical communication, and modelling. Diverse destinations of A level maths students RSS: A World Full of Data, Roger Porkess, 2013
A World Full of Data statistics opportunities across A-level subjects AS/A Levels in Mathematics approach to statistics AS/A Levels in Mathematics statistics content AS/A Levels in Mathematics statistics content AS/A Levels in Mathematics statistics content AS/A Levels in Mathematics statistics content AS/A Levels in Mathematics assessment objectives
AS/A Levels in Mathematics subject level guidance AS/A Levels in Mathematics assessment objectives A level Maths and Further Maths (from 2017) GCE AS and A level subject content for mathematics GCE AS and A level subject content for further mathematics GCE subject-level conditions and requirements for mathematics GCE subject-level conditions and requirements for further mathematics GCE subject-level guidance for mathematics GCE subject-level guidance for further mathematics A Level Mathematics Working Group Report on Mathematical Problem Solving, Modelli ng and the Use of Large Data Sets in Statistics in AS/A Level Mathematics and Further M athematics
Nuffield report Is the UK an outlier?, 2010 Develop alternative models for post-16 mathematics: The creation of an intermediate option or options between basic and advanced mathematics, aimed at those students who have already achieved an A*-C grade at GCSE. This would reflect the different career pathways of students and provide them with an appropriate option in mathematics at post16 level. Reports Reports
British Academy Count us in - quantitative skills for a new generation, 2015 ...offers a vision of how the UK can rise to the potentially transformational challenge of becoming a data-literate nation. ...co-ordinated & continuous effort at improving quantitative skills across all phases of education and employment is therefore now urgently needed. Core Maths Qualifications - objectives Core Maths qualifications will: 1. Deepen competence in the selection and use of mathematical
methods and techniques. 2. Develop confidence in representing and analysing authentic situations mathematically and in applying mathematics to address related questions and issues. 3. Build skills in mathematical thinking, reasoning and communication. Core Maths Qualifications - objectives 2. Students are expected to be able to: Use a variety of mathematical and statistical approaches to represent and analyse relatively well-defined situations, including complex and unfamiliar situations. This includes identifying and understanding quantifiable information and related assumptions in that situation, using mathematical and statistical representations and techniques appropriately, and deriving new information to draw meaningful
conclusions about the situation. Situations and problems should be drawn from physical/technical/scientific and human/behavioural/social domains and reflect a range of contexts including professional and academic settings. Core Maths Qualifications Core Maths qualifications should: consolidate and build on students mathematical understanding and develop further mathematical understanding and skills in the application of maths to authentic problems, thereby offering progression from GCSE mathematics.
provide a sound basis for the mathematical demands that students will face at university and within employment across a broad range of academic, professional and technical fields. Core Maths Qualifications Core Maths qualifications should: prepare students for the varied contexts they are likely to encounter in vocational and academic study and in future employment and life, for example, financial modelling and analysis of data trends. be particularly valuable for students progressing to higher education courses with a distinct mathematical or statistical element such as psychology, geography, business and management, such qualifications
will also be valuable for any student aiming for a career in a professional, creative or technical field. Core Maths Technical Guidance Core Maths Technical Guidance Revised A levels will be introduced for first teaching in 2015 and 2016. A levels being introduced in 2015 include the sciences, history, psychology, business studies, computing and, in 2016, geography - all of which likely to have strengthened mathematical content. The emphasis in these cases will be on subject-relevant mathematics, including statistics. Core Maths topics
Analysis of data Maths for finance Estimation Critical analysis of given data and models (including spreadsheets and
tabular data) The normal distribution Probabilities Correlation and regression
Critical path analysis Graphical methods Rates of change
Exponential functions (STATISTICAL) PROBLEM SOLVING MODELLING Core Maths Qualifications launch Strong maths skills are an essential part of our plan for education and are also vitally important to our economy.
Core Maths teaches pupils how to use and apply maths in real situations, and will help address a 16 to 18 maths gap whereby students who achieve a good maths grade at GCSE currently drop the subject and start to lose their confidence and skills. Thanks to these new high-quality courses more pupils will be able to continue their study of maths, ensuring more young people leave education properly prepared for the demands of university, work and life in modern Britain. DfE advice for 16-19 study programmes (Jan 2016) Core Maths In most other advanced economies, the study of maths is the norm for
students within their 16 to 19 education. Students who have already achieved GCSE A*-C should be encouraged to study maths at level 3 in the light of the value placed on this by employers and HE institutions. Awarding organisations have introduced new core maths qualifications at level 3 which will build on GCSE study. The focus of these is on problem solving, reasoning and the practical application of mathematics and statistics. These new qualifications have been designed with the support and help of employers and universities and suit students with a range of pass grades at GCSE maths. HM Treasury Budget - March 2016 Investing in the next generation Education
We are going to look at teaching maths to 18 for all pupils. Providing great schooling is the single most important thing we can do to help any child from a disadvantaged background succeed. Its also the single most important thing we can do to boost the long-term productivity of our economy, because our nations productivity is no more and no less than the combined talents and efforts of the people of these islands. HM Treasury Budget - March 2016: Smith review Investing in the next generation Education The government will ask Professor Sir Adrian Smith to review the
case for how to improve the study of maths from 16 to 18, to ensure the future workforce is skilled and competitive, including looking at the case and feasibility for more or all students continuing to study maths to 18, in the longer-term. The review will report during 2016. The government has not announced that maths will be compulsory post-16. The review will consider the case and feasibility for this. What happens next? Core Maths AS/A Level Mathematics Prodigious amount of CPD
School workforce in England November 2015 and tables Q Step Centres British Academy Learned societies/subject associations/accreditation HE Statistics across the curriculum: are we preparing young people for the data revolution? Thank you Paul Glaister [email protected]
A dual strategy: adding and embedding ask Professor Sir Adrian Smith to review the case for how to improve the study of maths from 16 to 18, to ensure the future workforce is skilled and competitive, including looking at the case and feasibility for more or all students continuing to study maths to 18, in the longer-term. The review will report during 2016 (Chancellor, March 2016) In each AS and A-level, the assessment of quantitative skills will include at least 10% level 2 or above mathematical skills for biology and psychology, 20% for chemistry and 40% for physics. These skills will be applied in the context of the relevant subject. (DfE, 2014, p. 24) [similar for non-science subjects] Understanding the problem
Rethinking the Value of Advanced Mathematics Participation (REVAMP, Nuffield) Who is doing advanced maths and with what benefits? www.nuffieldfoundation.org/rethinking-value-level -mathematics-participation Embedding Statistics at A level (RSS/ACME) How is statistics embedded in new A level SAMs? Mathematics in A Levels project (with Mary McAlinden) How is mathematics being embedded in new science SAMs and what are the implications? GCSE
Maths Grade A* A B C Year No maths
At least AS maths At least A-level maths At least A-level maths and AS further maths A-levels in maths and further maths Total
2004 6204 (23%) 21131 (77%) 19308 (71%) 6136 (22%) 4308 (16%) 27335
2007 4647 (17%) 22719 (83%) 20956 (77%) 7890 (29%) 5504 (20%) 27366
2010 5474 (15%) 31055 (85%) 27740 (76%) 10023 (27%) 7197 (20%) 36529
2004 24853 (51%) 23720 (49%) 17185 (35%) 1782 (4%) 902 (2%) 48573
2007 29463 (47%) 33889 (53%) 24031 (38%) 2810 (4%) 1440 (2%) 63352
2010 33454 (44%) 42236 (56%) 26946 (36%) 3266 (4%) 1740 (2%) 75690
2004 81735 (85%) 14404 (15%) 6294 (7%) 229 (0%) 70 (0%) 96139
2007 85276 (85%) 15196 (15%) 6387 (6%) 270 (0%) 76 (0%) 100472
2010 78752 (82%) 17553 (18%) 5577 (6%) 194 (0%) 85 (0%) 96305
2004 89247 (98%) 2054 (2%) 414 (0%) 20 (0%) 5 (0%) 91301
2007 99333 (99%) 16 (0%) 14 (0%) 100885 116405 (99%) 369 (0%) 235 (0%)
5 (0%) 2010 1552 (2%) 1245 (1%) 10 (0%) 117650 Changing patterns of AS/A level maths participation, by GCSE grade, for year 11 leaving cohorts in 2004, 2007 and 2010. Percentages are included in parentheses. N.B. This includes all students at each GCSE
grade, irrespective of whether they progressed to A levels. 60% Not studying maths Studying maths 50% 40% 30% 20% 10% 0% Agree a lot
Agree a little Disagree a little Disagree a lot Percentage responses to the question All students should have to study some maths up to age 18, split according to whether studying advanced mathematics or not in Year 12. [survey of 9255 17 year olds in 110 schools/colleges in January 2015]
Effect of A level mathematics upon probability of 1st class degree outcomes in Biology and Chemistry Embedding Statistics at A Level A few key questions What effects will the embedding strategy have? Can appropriate mathematics/statistics be integrated? Do the new qualifications offer better preparation for further disciplinary study? Can students achieve highly without mathematics/statistics?
What mathematics/statistics qualifications would complement within-discipline mathematics? What are the threats to success and how can they be overcome? REVAMP publications 1. Adkins, M. & Noyes, A. (2016). Reassessing the economic value of Advanced level Mathematics. British Educational Research Journal, 42(1), 93-116. 2. Noyes, A. & Adkins, M. (2016). Reconsidering the rise in A-Level Mathematics participation. Teaching Mathematics and its Applications. doi:10.1093/teamat/hrv016 3. Adkins, M. & Noyes, A. (2016). Do Advanced Mathematics Skills Predict
Success in Biology and Chemistry Degrees? Submitted for review to the International Journal of Science and Mathematics Education. 4. Noyes, A. & Adkins, M. (2016). The Impact of Research on Policy: A Case of Qualifications Reform. British Journal of Educational Studies, 10.1080/00071005.2016.1159654 5. Noyes, A. & Adkins, M. (2016). Studying advanced mathematics in England: findings from a survey of student choices and attitudes. Research in Mathematics Education 10.1080/14794802.2016.1188139 For further information contact [email protected] GCSE, AS and A level Reform Statistics across the curriculum
Alison Tonkin 4th July 2016 Responsibilities for qualifications reform Ministerial responsibilities: Overall policy on qualifications purposes, priorities How the curriculum is developed and subject content for qualifications Link to wider policy agenda Ofquals responsibilities: Standards of qualifications Efficiency and value for money of qualifications Regulatory oversight of the qualifications system
Exam Boards: Design and deliver qualifications Qualification with specific mathematical requirements GCSE qualifications GCE AS and A level qualifications Biology Chemistry
Biology Business Physics Combined Science Chemistry Geography Computer Science
Geography Psychology Sociology Business Economics Physics Design and Technology
Astronomy Geology Accounting Electronics Psychology Electronics Geology
Environmental Science Design and Technology Engineering Mathematics Further Mathematics Mathematics
Statistics Statistics Accreditation For a qualification which is subject to an accreditation requirement, the following criterion must be met: An awarding organisation must demonstrate to Ofquals satisfaction that it is capable of complying, on an ongoing basis, with all of the General Conditions of Recognition that apply in respect of the qualification for which it is seeking accreditation, including all relevant Qualification Level Conditions and Subject Level Conditions.
Accreditation Exam board submissions must include a specification, an assessment strategy and sample exam papers and mark schemes. Dedicated panels made up of Ofqual staff and external subject experts review the submissions against our conditions Accreditation is not a comparability exercise, but panels do consider the expected level of difficulty of sample exam papers and their potential to
differentiate across the full range of student ability. Closing links Find out here how we use external experts, how we oversee this, and apply to become one: https:// www.gov.uk/guidance/apply-to-become-an-externaladvisor-to-ofqual Breakout discussion Statistics in Englands new qualification landscape
59 Questions What is the opportunity and feasibility of improving statistical literacy within your subject / across a range of subjects? What are key drivers for schools to support this? What is the role of the new curricula, standards and qualifications? How can we improve participation in mathematics (and statistics in particular) among students who would not ordinarily continue with it? Please formulate and share three key
advisory points from your table 60 Implementing change: Statistics across subjects Introductions from panellists, and panel discussion. Panel speakers Chair: Simon Gallacher, Senior Consultant, The Nuffield Foundation Marianne Cutler, Director of Curriculum Innovation, the Association for Science Education Steve Brace, Head of Education and Outdoor Learning,
The Royal Geographical Society Kathleen Maitland, Senior Lecturer in Computing, Birmingham City University Mick Blaylock, Head of the Core Maths Support Programme Sharon Witherspoon, Acting Head of Policy, the Academy for Social Sciences 62 Mick Blaylock, Core Maths Support Programme Key links http://www.core-maths.org/ Bank of teaching resources www.core-maths.org/resources/
Case studies from schools delivering core maths http://www.core-maths.org/why-core-maths/ DfE (2015) Core Maths Qualifications: Technical Guidance (pdf) http ://www.core-maths.org/media/2076/core-maths-techni cal-guidance_-_ july-2015.pdf
63 Steve Brace, Royal Geographical Society Key links Geography GCSE specifications Geography AS and A Level specifications Follow @RGS_IBGSchools & visit www.rgs.org/dataskills for Online resources supporting data skills in GCSE and A Level geography CPD events Open data sources relevant to geography 64
Marianne Cutler (Association for Science Education) The Language of Mathematics in Science a guide for teachers, and teaching approaches http ://www.ase.org.uk/resources/mat hs-in-science / 65 Paul Glaister (University of Reading):
Making the case for mathematics participation Mathematical Sciences: Driving the UK Econ omy A world full of data: Statistics opportunities a cross A-level subjects British Academy Count us in reports on q uantitative skills The Royal Society Vision for science and mat hematics education 66 Paul Glaister (University of Reading): GCSE Mathematics & new developments post-16
Mathematics GCSE subject content and asse ssment objectives Mathematics GCSE subject level guidance 16 to 19 funding: maths and English conditio n of funding Core maths qualifications technical guidance 67 Paul Glaister (University of Reading): AS/A Level mathematics & Further mathematics GCE AS and A level subject content for mathematics GCE AS and A level subject content for further mathematics
GCE subject-level conditions and requirements for mathematics GCE subject-level conditions and requirements for further mathematics GCE subject-level guidance for mathematics GCE subject-level guidance for further mathematics A Level Mathematics Working Group Report on Mathematical Problem Solving, Modelling and the Use of Large Data Sets in Statistics in AS/A Level Mathematics and Further Mathematics 68 Kathleen Maitland (Birmingham City University) data science and computing SAS JMP - recommend for year 5 and up in schools,
http://jmp.com/teach SAS academic resources for UK http://www.sas.com/en_gb/academic/overview.html SAS university edition free. http://www.sas.com/en_us/software/university-edition.html
Qlik known for data visualisation, also a free data analytics tool http:// global.qlik.com/uk/landing/go-sm/download-free-kit.aspx IBM Watson More linguistical, represents future of data analytics software (& also free) https://developer.ibm.com/academic/ 69 Andy Noyes (University of Nottingham) Rethinking the Value of Advanced
Mathematics Participation (REVAMP, Nuffield) Who is doing advanced maths and with what benefits? www.nuffieldfoundation.org/rethinking-value-le vel-mathematics-participation Embedding Statistics at A level (RSS/ACME) How is statistics embedded in new A level SAMs? http:// www.rss.org.uk/Images/PDF/publications/rss-AC ME-embedding-statistics.pdf Alison Tonkin (Ofqual) engagement of subject experts in accrediting qualifications
Find out here how we use external experts, how we oversee this, and apply to become one: https://www.gov.uk/guidance/apply-tobecome-an-external-advisor-to-ofqual 71 Sharon Witherspoon (Academy of Social Sciences) statistics in the social sciences Nuffield Foundation, Quantitative Methods Programme Background Nuffield Foundation (2014) Mathematics after 16 The need to tackle decline in AS level
entries - Ofqual Statistical Release, April 2016 (Provisional Figures) 72 Geography: a real-world context for statistical methods Estimated that 80% of all information collected is geo referenced & geographical approaches and perspectives providing important and new ways of understanding the worlds people, places and environment.
The use of Ordnance Survey data underpins 100 billion of the UKs economy. 73 Geography as a quantitative subject HEFCE recognised the part-STEM nature of the subject in 2010, QM is within QAA benchmark statement In the RGS 2012/13 Quantile report: Of 580 2nd year & above students:46%
disagreed with the statement that their course had too much maths & stats 17% agreed. Heads of HE teaching 92% QM fundamental part of geography degree 74 Examination reform Sept 2016 GCSE Geography new appendix the use of maths and stats in geography: Cartographic, Graphical, Numerical and Statistical. 15% specifically on fieldwork skills A Level. What makes data geographical, collect and use, purpose and difference between techniques and approaches. 20% specifically on individual investigation
define a question, observe and record, collect and analyse data, critically examine data to make a well argued case Across both geography (be it exploring flood risk, the diversity of our communities or climate change) provides a real world context where the application of QM provides the means to interrogate and understand 75 Opportunities School geography an important carrier of QM within the social sciences/humanities, with rising numbers of GCSE and A Level entries (250K and 37K at a 5 year high) A doubling of undergrad geographers carrying A Level
maths (+20%) Recently launched Data Skills in Geography www.rgs.org/data. It is providing CPD, classroom resources and also the intellectual underpinning for the use of QM in school geography. We are working across schools and HE (in Q-Steps centres) & getting good response from teachers 76 Issues The issue of teaching to or just the statistical test without the reason why
Initial teacher training more joint and cross-curricular training initiatives needed Disciplines are separated by a lack of common language eg gradient. 77 The Language of Mathematics in Science developed with funding from the Nuffield Foundation Marianne Cutler The Association for Science Education Statistics across the curriculum: are we preparing young people for the data revolution?
The Royal Statistical Society 4 July 2016 The aim of this book is to enable teachers, publishers, awarding bodies and others to achieve a common understanding of important terms and techniques related to the use of mathematics in the science curriculum for pupils aged 11-16. This publication provides an overview of relevant ideas in secondary school mathematics and where they are used in science. It aims to clarify terminology, and indicate where there may be problems in student understanding. The publication includes explanations of key ideas and terminology in mathematics, guidance about good practice in applying mathematical ideas in science, along with a glossary of terms. The main part of the book consists of ten chapters, each focusing on a
particular aspect of scientific activity, such as data collection, analysis, looking for relationships, presenting data, using scientific models, and so on.
Introduction Chapter 1 Collecting data Chapter 2 Doing calculations and representing values Chapter 3 Choosing how to represent data Chapter 4 Drawing charts and graphs Chapter 5 Working with proportionality and ratio Chapter 6 Dealing with variability Chapter 7 Looking for relationships: line graphs Chapter 8 Looking for relationships: batches and scatter graphs Chapter 9 Scientific models and mathematical equations Chapter 10 Mathematics and the real world Glossary
1 Collecting data Key words: quantitative data, qualitative data, quantity, value, unit, data set, resolution, scale, significant figures, range, variable, continuous data, discrete data, categorical data, integer, experiment, survey, independent variable, dependent variable, control variable, factor, time series, raw data, primary data, secondary data. 1.1 Measuring and counting
1.2 Measurement and resolution 1.3 Characteristics of different types of data 1.4 Naming different types of data 1.5 Where do data come from? 6 Working with proportionality and ratio Key words: proportional, directly proportional, line graph, origin, gradient, slope, horizontal axis, vertical axis, x-axis, y-axis, x-coordinate, y-coordinate, rate, constant, constant of proportionality, reciprocal, inverse, inversely proportional, ratio, percentage, scale, scale drawing, scale factor, linear dimension.
6.1 Proportionality and visual representation 6.2 Interpretation of gradient 6.3 Proportionality and algebraic representation 6.4 Ratios 6.5 Proportional reasoning and ratios 6.6 Percentages 6.7 Scale drawings and images
8 Looking for relationships: batches and correlation Key words: population, sample, data set, batch, univariate data, stem-and-leaf diagram, histogram, box plot, median, quartile, range, interquartile range, outlier, percentile, bivariate data, scatter graph, correlation, probability, independent events, combined events.
8.1 Different kinds of relationship 8.2 Populations and samples 8.3 Analysing a batch of data 8.4 Dealing with more than one batch of data 8.5 Comparing batches of data 8.6 Scatter graphs and correlation 8.7 Drawing a line of best fit on a scatter graph 8.8 Basic ideas in probability Using teachers accounts, this outlines different ways that science and mathematics departments have worked together, and illustrates different approaches to teaching mathematical terms and applications.
This publication gives examples of how children respond to different learning activities intended to promote understanding of mathematics within a science context. Introduction Commentaries and teacher accounts on:
Cross-curricular approaches to graph drawing Deriving quantities from gradients Using a literary approach to interpreting graphs Introducing terms used to describe data types Joint mathematics and science day to teach equations and graphs The vocabulary of graphs an example of departmental collaboration Molar calculations in chemistry Interpreting graphs Acknowledgements The project team, Marianne Cutler, Richard Boohan and Richard Needham, would like to thank the following for their valuable contributions:
Our steering group chaired by Professor Robin Millar Our mathematics advisors Our mathematics and science review panel Our teacher review panel AQA, Edexcel, Eduqas and OCR The Nuffield Foundation
These publications and further information about the project are available from www.ase.org.uk/resources/maths-in-science/ [email protected] Statistics and Data Analytics in Computing Dr Kathleen Maitland Computing is a CORE skill Regardless of the subject area or career there is a need for computing skills
Where is computing concepts used? Developing Financial systems websites Order processing Word processing Logistic Storing data Analysing data Gaming Social media Business systems Data Analytics and Statistics is a CORE skill
Regardless of the subject area or career there is a need for data analytics and statistics Where is data analytics and statistics concepts used? Developing Financial systems websites Order processing Word processing Logistic Storing data Analysing data
Gaming Social media Business systems Understanding data and statistics Computer programmers write the programs which enables the statistics to be calculated and produce graphical outputs. Data analytics tools should be the enabler to gain statistical understand of data. Software should not be a barrier to understanding data There are many data
analytics software tools Employability - Careers - Jobs On the 1st July 2016 Monster: 746 Data Analyst jobs 1000+ Data Science jobs 132 Statistician jobs Reed: 4416 Data Analyst jobs 235 Data Scientist jobs 33 Statistician jobs What difference a job title makes and the difference between students gaining software enable statistical knowledge and skills careers.
Employers employing and recruiting data analysts Computing and Statistics should be CORE Skills for all students Statistics skills and knowledge is a core part of data analytics. There needs to be an awareness of the new job titles which include statistical knowledge and
skills ICT has been recognised as a Core skill and it is time that Statistics/Data analytics is recognised as a Core skill for all students.