** 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...**

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