Data Analytics Newsletter #17 - April 2020

Data Analytics Newsletter #17
April 2020

The Data Analytics Practice Committee (DAPC), the Young Data Analytics Working Group (YDAWG) and the Actuaries Institute are pleased to bring the latest in the world of Data Analytics to your inbox, and to share some of our recent work with you.

By popular request, this edition of the Data Analytics Newsletter focuses on professional development opportunities during this social distancing period, with a wide range from full courses, virtual conferences and short articles to suit free time availabilities.

1. Learn valuable R and Python skills with DataCamp
2. Get a Harvard education
3. Tableau offer free eLearning course amid COVID-19 shutdown
4. Familiarise yourself with Kaggle during lockdown
5. Conference Videos on Applied Data Ethics
6. 2020 top 25 analytics leaders - nominations now open
7. Data Analytics news corner

Learn valuable R and Python skills with DataCamp

Have you wanted to learn more about data science, machine learning, R or Python but didn’t know where to start? Working from home could be the perfect time to try something new.

DataCamp is a favoured provider of programming courses according to Actuarial employers. DataCamp offers a range of courses: from the simple (how to add numbers in R); to the complex (build a neural net). View the DataCamp course list.

Get a Harvard education

For those who have dreamed of an education at Harvard University, there is now the opportunity to do so virtually.

There are a number of free data science courses ranging from 4 to 10 weeks, covering a wide range of theoretical and practical topics, including probability, statistics, data wrangling, R, and best practices for reproducible data science. View the course selection.

Tableau offer free eLearning course amid COVID-19 shutdown

Popular data visualisation tool Tableau is quite user friendly as it is, but for those looking to maximise their utility with the tool, there is a promotional 90 days free eLearning during the epidemic. Find out more.

Familiarise yourself with Kaggle during lockdown

Kaggle is well known as a place for competitive data scientists to test their mettle, but it can also be a useful learning resource. There are now a micro-courses on the site, covering topics such as Python, SQL, machine learning, geospatial analysis, natural language and even game AI.

The notebooks on the site are also helpful “analytics snippets” for a practical introduction for solving data science problems with code. Datasets available on the Kaggle website can also be quite helpful for practice. Explore Kaggle.

Conference videos on applied data ethics

Ethical data usage is just as important even in an uncertain health and economic environment. The University of San Francisco held a workshop on fairness, diversity, disinformation and government policy in November 2019, and the videos have recently been made available. Read more and watch the videos.

The fast.ai blog has synopsises for all the talks, and links to the full content. For those disappointed in cancellation of conferences due to COVID-19, this may present a fascinating virtual alternative. Read Disinformation: what it is, why it's pervasive, and proposed regulations.

2020 top 25 analytics leaders - nominations now open

Do you know an influential, innovative and impactful leader in analytics? The Institute of Analytics Professionals of Australia is looking for nominations for top analytics leaders in the industry. Please take two minutes to put them forward and recognise the value they add. Make a nomination.

Data Analytics news corner

A Data Scientist’s guide to data architecture

“You should always prepare your own data.”
Good data science is reliant on robust data and often data preparation is the major task requiring most time and attention in a data science project. YDAWG member Zeming Yu shares many valuable lessons learnt from practical experience at a major insurer. Read more.

Optimising sample sizes in A/B testing

A/B experimentation is a cornerstone application for data science. However, a common question is how large should samples be to provide sufficient power. This three part series outlines a framework for guiding the theoretical recommendation. Read part one.

Should (and could) advances in machine learning and AI remain free?

Many of the recent advances in machine learning has been made free and open source – both modelling methods such as XGBoost and PyTorch, and pre-trained models such as VGG-16 or GPT-2. This has democratised data analytics and made it accessible to a wide audience. However, can this idealism persist in the long term? The story of OpenAI is an interesting read that highlights the pitfalls when non-profit principles conflict with commercial considerations. Read more.

Future thinking

For some lighter reading in this time of reflection, Futurism presents new ideas in science and technology. The content is general, but includes articles on machine learning, AI and data science. With applications of data science often working hand-in-hand with other technological advances, the articles may be a potential source of inspiration for the solutions to the problems of tomorrow. Read more.

Ergonomics

Good ergonomics is critical for a safe, healthy and effective work or professional development environment. This article by the ABC highlights proper monitor and chair positioning, the need to keep moving, taking regular breaks, workstation “hacks”, proper lighting and setting appropriate boundaries for being effective while working from home. Read more.

There are further resources on Safe Work Australia. Read them here.

Editor's note

How are you spending free time during this social distancing period? We are on the lookout for useful resources to include in the next Newsletter, so if you have found any not listed above, please do not hesitate to contact me.

Past editions of this Newsletter are now available here.

Jacky Poon
Editor, Data Analytics Newsletter

Disclaimer: The Institute wishes it to be understood that any opinions put forward in this publication are not necessarily those of the Institute.

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