We are excited to show you extracts of our work here:
The book Data Driven Dealings Development (click on this link to read an extract on Amazon) is addressed to everyone who wants to analyze Sales Data and Market Baskets, and create Product Recommender per Customer with Python. The Book covers both the theoretical aspects about Analytics, as well as the practical coding part, including complete code and data. Both unsupervised and supervised Machine Learning techniques are applied using the Pandas, Scikit-Learn, Tensorflow and Turicreate stack (amongst others).
Learn how to analyse market baskets regarding sales items per sales transaction id (click here to read on Medium).
If under a high number of cycles and level of automation you have to provide a stable high quality, statistical process controll(SPC) can help you do the job (Jesko Rehberg for Additive Academy: Quality Control Charts for stable processes in the food industry). Quality Control Charts and Process Capability Analysis help you keep your process under controll und be informed in time when systematic changes occur to your process.
Especially newbies might be overwhelmed by the many different languages, libraries and tools in data science. For the sake of an easier entry into the topic data science we are hereby giving you an easy overview. Our focus here is really on explaining the main idea behind data science, not going into the highest details. For more details on the categories mentioned in brackets () below you can click on the specific bubbles.
What do you need to conduct data science? Imagine you are a writer and you are trying to write a book. To be able to write something you must have a good command of a language, as a basic requirement. That`s no difference with regards to data science: you must know how to code (Language).
Okay, now you could start writing your book. But certainly you do not want to write your book standing. Instead, for working properly you will work at a desk. In Data Science this desk could be a Notebook (Notebook). In case your desk is going to be too small for your work some day (or too slow) you can conduct your Big Data projects on huge desks in the Cloud (Cloud Platforms).
An empty desk alone will not fill any book, for sure. So you will equip yourself with a pencil, paper, scissors etc. As a Data Scientist your most important Frameworks (Libraries) corresponds to these, which will help you receiving professional results in shorter time.
Now you have all the utensils available for starting writing your book. So now the real work starts: You have to make up a story. Translated into data science this means you need data (Data), to analyze anything at all.
And data alone is not sufficient if you do not know anything about it (Domain Knowledge). However, you would not waste your time reading a book that only includes nonsense. If you satisfy all the conditions required you can start writing. After your first few lines you realize that - beside a language - you also need to handle the right grammar. Every Data Scientist must be aware of statistics, at least more than a coder and a data scientist must have better coding skills than a pure statistician (Statistics).
After all pre-conditions are fulfilled you as a data scientist can start "writing your story".
If you are looking for support for the sake of discovery, interpretation and communication of meaningful patterns in your data we look forward to hearing from you. We are here to help you gathering new insights from your data via spotting unknown trends, seasonality or patterns. We offer both analysis as a service as well as trainings.
Benefit from our expertise! We are looking forward to getting to know you!