Basic Data Analytics
This course will be run as a live simulcast online course presented through Microsoft Teams. You will be required to use Excel 2016 or later during this course.
A spreadsheet such as Microsoft Excel is one of the most used software applications of all time. Excel allows us to enter all sorts of data and perform analysis such as financial, mathematical or statistical analysis. Excel contains many powerful tools for advanced operations such as calculation and computation, data and record sorting, optimisation, regression, and generating graphs and plots. In this short course, some of the basic operations of Excel will be introduced, those that are useful when carrying out basic data analysis for decision making.
The course includes hands-on training with Excel and Excel based tools for data analysis and decision making.
Prior Knowledge: No prior knowledge is assumed.
Duration: Three days
Delivery mode: Online
What you will receive:
- Comprehensive course notes and exercises
- UNSW Canberra certificate of completion/attendance
- Masters credit: UNSW Canberra allows students who have successfully completed a minimum of 12 days of approved professional education short courses to use those courses as credit in eligible postgraduate programs.
WHO SHOULD ATTEND
The course is valuable for anyone interested in basic data and decision analysis.
This three-day course addressing Spreadsheet based Data Analytics. The course addresses the concept of data elements, different classifications of data and levels of measurement. Data sources are briefly mentioned and the different modes of sampling are highlighted. Basic EXCEL worksheet operations are covered such as SUM, SUMIF, SUMPRODUCT, COUNT, COUNTIF, VLOOKUP and HLOOKUP and illustrated through hands-on exercises. Data visualisation using EXCEL charting techniques will be addressed. Data Tables, Pivot Tables, Regression and Trend Analysis will be covered.
Testimonials: 'I found this course to be truly fantastic; the presenter was knowledgeable and excellent at explaining the concepts and content. I learnt more on this course than any other course I can remember attending. I have recommended it to all my colleagues at work. Thank you!' Alison, November 2019.
Prof. Charles S Newton obtained his PhD in Nuclear Physics from the ANU in 1975. He is an Emeritus Professor in the School of Engineering and IT, UNSW Canberra. He was the Head of the former School of Computer Science (currently a part of School of Engineering and IT) from 1993 to 2003. He was the President of National Committee of the Australian Society for Operations Research (ASOR) in 1995-96. He is the co-author of the book Optimization Modelling: A Practical Approach, Taylor & Francis /CRC Press, Boca Raton. Prof. Newton is well-known, both nationally and internationally, for his practice in Operations Research specifically for defence related problems.
Prof. Ruhul A Sarker obtained his Ph.D. in Operations Research from Dalhousie University (former TUNS), Halifax, Canada in 1992. He is currently a Professor in the School of Engineering and IT, co-ordinator of the Master of Decision Analytics program in the school, and the Director of Faculty Postgraduate Research at UNSW Canberra (located at ADFA), Australia. He was the Deputy Head of School (Research) from 2011 to 2014. Prof. Sarker’s broad teaching and research interests include decision analytics, computational intelligence, operations research, and applied optimization. He is the lead author of the book Optimization Modelling: A Practical Approach, Taylor & Francis /CRC Press, Boca Raton. Prof. Sarker has successfully obtained more than $1.5million external research grants. He was a member of the national executive committee of the Australian Society for Operations Research (ASOR) and Editor-in-chief of ASOR Bulletin from 2000 to 2011. As recognition of Prof. Sarker’s contributions to ASOR and Operations Research, ASOR awarded him an ASOR Medal (2011) and Special Service Certificate (2009). Currently, he is an associate editor of three international journals.
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