Meet the Instructors
Dr Tatjana Kecojević is a longtime R user with a doctorate in Statistics from the University of Manchester. Tatjana’s love for teaching applied statistics has brought her to the School of Social Sciences where she’s a lecturer in statistics for social sciences. She spent many years working in the U.K. university sector as a senior lecturer and has published an extensive number of articles and papers in the field of quantile regression. Tatjana is the founder and co-organizer of R-Ladies Manchester, Belgrade and Novi Sad Chapters, leader of the R Forwards team and a Women in DS (WiDS) ambassador. She is currently the founder and director of SisterAnalyst.org, an organisation aiming to empower women from a diverse range of backgrounds through data literacy. Unsurprisingly, Tatjana is an enthusiastic R user and in addition to her involvement supporting women in STEM related activities, she is dedicated to creating an inclusive culture by developing initiatives supporting all underrepresented groups within the DS community.
During my lecturing career I was incredibly fortunate to work with some truly amazing colleagues who helped me explore and develop my own teaching philosophy and practice. I gained valuable experience in developing, designing and teaching data analysis modules at varying levels of undergraduate and graduate courses. In particular I mastered my teaching skills by lecturing with George Rawlings and Ian McGowan on decision modelling modules. They taught me how to teach basic statistical concepts, which theoretically may be perceived as complex, in an effective way by emphasising concepts over formulae, engaging students to reason rather than to memorise.
The material presented is built on those principles and has been enriched by integrating the vast amount of open and inclusive #rstats community resources. This learning resource is free to use. It is written in
Rmarkdownusingblogdownpackage.
Dr Sook Kim received her PhD in Social Statistics at the University of Manchester. Her research centres around gender inequalities at work, and advanced quantitative methods. She has contributed to several government-funded projects on the gender pay gap, theoretical sampling design, and methodological advancements on the use of administrative data in official statistics.
Sook is passionate about promoting greater access to math and data analysis for women and individuals with social science backgrounds, and with less computation skills. Her approach to teaching involves maximising inclusiveness and diversity to reach as wide an audience as possible. Sook emphasises demystifying complex statistical and computational procedures, and presenting them in an accessible way. She is committed to contributing educational resources to the DS community as an ongoing effort to enlighten herself and others.
Niyati Somani is a recent Economics graduate from the University of Manchester. During her time at University, she completed a work placement at the Bank of England which served as her introduction to the world of data. She quickly became proficient in utilising R to extract insights and make data-driven decisions. Throughout her final year, she continued to expand her expertise in R for various modules and personal projects. With a keen eye for detail and a strong aptitude for problem-solving, Niyati is passionate about sharing her knowledge and helping others develop the necessary skills to use data efficiently.
Harry Penford is a recent Economics graduate from the University of Manchester. In his third year, Harry worked a year in the Home Office as a student Economist. During this time, he undertook various data-driven projects in which he realised the importance of being data literate. While he had become familiar with R-studio in his second year, he enrolled in a DataCamp subscription to improve his data analysis skills. As someone with little experience before this, Harry is passionate about making the world of data and programming accessible and exciting for everyone.
Material is released under a Creative Commons Attribution-ShareAlike 4.0 International License.
