Sophia Quint brought is the case of forecasting US visitors visiting Berlin.
You researched and identified relevant data.
You installed Metabase on your computer and are able to use it from now on.
You explored the researched data in form of SQLite files using Metabase and created different charts.
You identified the "peak behavior" of Berlin visitors from the US and visualized it.
You learned about Google Trends data and found search terms with similar patterns relating to our US visitor data.
You enriched the US visitor data with Google Trends data showing a peak in searches before actual US visitors arrive in Berlin.
You installed Orange on your computers and learned about its basic widgets/nodes and edges for modeling the flow of data.
You familiarized yourself with Orange and loaded the data in form of CSV files.
You learned about different nodes in Orange, specifically you applied ARIMA (autoregressive (AR) integrated (I) moving average (MA)) and VAR (vector autoregression) models to forecast US visitors.
You performed your (first ever?) statistical hypothesis test for determining whether search terms on Google Trends are predictive for forecasting US visitors. You applied the so called Granger Causality test.
In summary, you did a lot. But actually more importantly, I hope, you learned some meta lessons along the way.
Using a powerful but complicated machine like a computer is challenging but can be rewarding. You need to exact when instructing a computer. It's expected to struggle and to run into problems. When you solve a problem, that's learning. If you run into a problem and solve it yourself, you are much more likely to remember.
Things go wrong. That's expected and normal. This way you gain experience. The more you know, the easier it gets. Also, make sure to reflect on what you are doing and form hypotheses internally to verify your understanding. This way you get better and avoid dead-ends.
The more you do (in and across tools), reflecting when it does not work and trying to fix it in a principled non-random manner, you will gain more insights and confidence. Problems come in similar patterns with similar solutions.
With the Internet, it's very likely that another person ran into the very same problem (and was kind enough to document and provide a solution to it) as you are experiencing right now. Use that to your advantage and search for similar problems online to find solutions.
It's normal to struggle. You saw me struggle myself (more than once in Metabase and more than once in Orange). The more you use a tool the more you become an expert in it. If you are not using regularly, you have the chance for re-learning things you have forgotten.
The next week will basically be the same as this week with
new datasets to explore and
new methods to learn and apply
to gain more experience and confidence. In the end you should be able to solve data science problems with the tools we covered. in class yourself and transfer some of your knowledge and skills to other tools in future.
