Lisa Charlotte Rost of DataWrapper often writes about data visualization and lately she has focused on the (im)proper use of color in visualization. In this recent blog, she gives a bunch of great tips and best practices, some of which I copied below:
I stumbled across this open access book by Rob Hyndman, the god of time series, and George Athanasopoulos, a colleague statistician / econometrician at Monash University in Melbourne Australia.
Hyndman and Athanasopoulos provide a comprehensive introduction to forecasting methods, accessible and relevant among others for business professionals without any formal training in the area. All R examples in the book assume work build on the fpp2 R package. fpp2 includes all datasets referred to in the book and depends on other R packages including forecast and ggplot2.
Some examples of the analyses you can expect to recreate, ignore the agricultural topic for now ; )
One of the example analysis you will recreate by following the book (Figure 3.3)You will be forecasting price data using different analyses and adjustments (Figure 3.4)
I highly recommend this book to any professionals or students looking to learn more about forecasting and time series modelling. There is also a DataCamp course based on this book. If you got value out of this free book, be sure to buy a hardcopy as well.
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Useful base functions
str() – explore structure of R object
trimws() – trim trailing and/or leading whitespaces
dput() – dump an R object in form of R code
cut()– categorize values into intervals
intersect() – returns similar elements in two vectors
union() – find intersecting items in two vectors
setdiff() – returns different elements in two vectors
interaction() – computes a factor which represents the interaction of the given factors
formatC()can be used to round numbers and force trailing zero’s
formatC() and sprintf() can be used to add leading/trailing characters
expand.grid() – create a data frame from all combinations of the supplied vectors or factors
seq_along(myvec) – generates a vector of 1:length(myvec)
Generate distributions in ggplot2 using the stat_function function. Normal distributions, student t-distributions, beta distributions, anything. See also here.
Why do groups of people act smart, dumb, kind, or cruel? People behave in strange ways, particularly when they are able to influence one another. Both good and bad things can happen when people interact and behave in network structures. On the bright side, you must be familiar with the wisdom of the crowd, where the aggregated knowledge of a group is more valuable than its sum? Ensemble algorithms – like random forest analysis – rely on this positive principle.
On the dark side, are you familiar with the phenomenon called the tragedy of the commons, where shared resource-systems collapse because individuals behave in their self-interest? Or psychological phenomena such as groupthink, where groups of people make irrational decisions due to social issues? The recent spread of fake news and misinformation is also stimulated by network interactions. In these cases, we could speak of the madness of the crowd.
Nicky Case made a great interactive walkthrough explaining why and when networks of people become wise or mad. You are tasked to change and simulate network interactions while Nicky explains concepts such as (complex) contagion, the majority illusion paradox, bonding and bridging, and small world networks. In the references, Nicky provides links to scientific papers explaining these concepts in more detail. I highly suggest you check out her website here.
Screenshot of one of the explanations/simulations Nicky offers.
Tilburg University has set up a masterclass Predictive HR Analytics. In 3 days, the Professional Learning program will teach you all you need to know to implement predictive analytics and take HR to the next level. More information can be found here.
What makes this program unique?
The masterclass Predictive HR Analytics goes beyond HR analytics and focuses on transformational people predictions. You learn how to embed predictive HR analytics into your HR Strategy and how to use your findings to convince others.
The masterclass is developed at the prestigious Human Resources department at Tilburg University, which has obtained international recognition with its high-quality academic research in the HRM field.
The mix of professors in conjunction with leading HR professionals leads to a strong academic program with a practical approach.
Your peer participants will make sure that the class opens up a high-quality network of HR specialists. The diversity of leading companies from different sectors in the classroom creates new insights for all the participants.
The program is like a 3-day pressure cooker. By combining online and offline components, we can create more in-depth discussions in the classroom.
You will experience a high impact on your daily practice, since the program is focused on direct implementation.
Your profile
This course is ideal for anyone in HR seeking to become more adept in using quantitative data for decision making. Typical participants are (future) HR analysts, HR managers, HR business partners, HR consultants and (financial) business analysts with a strong link on people resources. Participants are from various sectors, such as financial services, healthcare institutions, government agencies and business services.