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# Data Science Project on President Heights

If you are a beginner in Data Science you must solve this project, as you will learn a lot about working on Data, that comes from a csv file or any other formats.

This data is available in the file heights.csv, which is a simple comma-separated list of labels and values:

`print(data.head())`

We’ll use the Pandas package to read the file and extract this information (note that the heights are measured in centimeters):

`height = np.array(data["height(cm)"])`

`print(height)`

Now that we have this data array, we can compute a variety of summary statistics:

`print("Standard Deviation of height =", height.std())`

`print("Minimum height =", height.min())`

```
print("Maximum height =", height.max())
```

Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values. We may also wish to compute quantiles:

`print("Median =", np.median(height))`

```
print("75th percentile =", np.percentile(height, 75))
```

We see that the median height of US presidents is 182 cm, or just shy of six feet. Of course, sometimes it’s more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib:

`import matplotlib.pyplot as plt`

`import seaborn as sns`

`sns.set()`

`plt.title("Height Distribution of Presidents of USA")`

`plt.xlabel("height(cm)")`

`plt.ylabel("Number")`

`plt.show()`

These aggregates are some of the fundamental pieces of exploratory data science that we’ll explore in more depth in later coming projects.

# Python Example for Beginners

## Two Machine Learning Fields

There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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