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Data Science Techniques

Data Science
by digiGeek

Data Science Techniques

A huge variety of techniques
Most of the time Data Science is quite simple, repeating the old stuff again and again.
But make more of what you have by variation of the techniques.
Some of the techniques you might have heard one thousand times and more, but others are not so common. Don't forget about the not so common ones.

(Not complete) list of techniques
Data Science Data cleansing activities can be quite intense and complex. Data cleansing has to make sure, the data is:
Valid, complete, consistent, uniform (formats), accurate.

ML is used where designing and programming explicit algorithms with good performance is difficult or infeasible, such as
- Regression (linear vs. logistic)
- Time Series
- Game Theory (see my Uni ZH CAS 2018 written work)
- Test of Hypotheses
- Neural Networks
- Support Vector Machines
- K Nearest Neighbours
- Supervised Learning
- Clustering (unsupervised learning)
- Graphs
- Density Estimation
- Pattern Recognition
- Decision Trees
- Random Numbers
- Bayesian Statistics
- Naive Bayes
- Neural Networks
- Geo Modeling
- many more...

In case of questions, don't hesitate to contact us from www.digiGeek.ch !

John

Matthias Seiler

CEO & Founder of digiGeek