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### Statistics for Machine Learning Crash Course.

*Get on top of the statistics used in machine learning in 7 Days.*

Statistics is a field of mathematics that is universally agreed to be a prerequisite for a deeper understanding of machine learning.

Although statistics is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field are required for machine learning practitioners. With a solid foundation of what statistics is, it is possible to focus on just the good or relevant parts.

In this crash course, you will discover how you can get started and confidently read and implement statistical methods used in machine learning with Python in seven days.

This is a big and important post. You might want to bookmark it.

Discover statistical hypothesis testing, resampling methods, estimation statistics and nonparametric methods in my new book, with 29 step-by-step tutorials and full source code.

Let’s get started.

## Who Is This Crash-Course For?

Before we get started, let’s make sure you are in the right place.

This course is for developers that may know some applied machine learning. Maybe you know how to work through a predictive modeling problem end-to-end, or at least most of the main steps, with popular tools.

The lessons in this course do assume a few things about you, such as:

- You know your way around basic Python for programming.
- You may know some basic NumPy for array manipulation.
- You want to learn statistics to deepen your understanding and application of machine learning.

You do NOT need to know:

- You do not need to be a math wiz!
- You do not need to be a machine learning expert!

This crash course will take you from a developer that knows a little machine learning to a developer who can navigate the basics of statistical methods.

Note: This crash course assumes you have a working Python3 SciPy environment with at least NumPy installed. If you need help with your environment, you can follow the step-by-step tutorial here:

## Crash-Course Overview

This crash course is broken down into seven lessons.

You could complete one lesson per day (recommended) or complete all of the lessons in one day (hardcore). It really depends on the time you have available and your level of enthusiasm.

Below is a list of the seven lessons that will get you started and productive with statistics for machine learning in Python:

**Lesson 01**: Statistics and Machine Learning**Lesson 02**: Introduction to Statistics**Lesson 03**: Gaussian Distribution and Descriptive Stats**Lesson 04**: Correlation Between Variables**Lesson 05**: Statistical Hypothesis Tests**Lesson 06**: Estimation Statistics**Lesson 07**: Nonparametric Statistics

Each lesson could take you 60 seconds or up to 30 minutes. Take your time and complete the lessons at your own pace. Ask questions and even post results in the comments below.

The lessons expect you to go off and find out how to do things. I will give you hints, but part of the point of each lesson is to force you to learn where to go to look for help on and about the statistical methods and the NumPy API and the best-of-breed tools in Python (hint: I have all of the answers directly on this blog; use the search box).

Post your results in the comments; I’ll cheer you on!

Hang in there; don’t give up.

Note: This is just a crash course. For a lot more detail and fleshed-out tutorials, see my book on the topic titled “Statistical Methods for Machine Learning.”

### Need help with Statistics for Machine Learning?

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

## Lesson 01: Statistics and Machine Learning

In this lesson, you will discover the five reasons why a machine learning practitioner should deepen their understanding of statistics.

### 1. Statistics in Data Preparation

Statistical methods are required in the preparation of train and test data for your machine learning model.

This includes techniques for:

- Outlier detection.
- Missing value imputation.
- Data sampling.
- Data scaling.
- Variable encoding.

And much more.

A basic understanding of data distributions, descriptive statistics, and data visualization is required to help you identify the methods to choose when performing these tasks.

### 2. Statistics in Model Evaluation

Statistical methods are required when evaluating the skill of a machine learning model on data not seen during training.

This includes techniques for:

- Data sampling.
- Data resampling.
- Experimental design.

Resampling techniques such as k-fold cross-validation are often well understood by machine learning practitioners, but the rationale for why this method is required is not.

### 3. Statistics in Model Selection

Statistical methods are required when selecting a final model or model configuration to use for a predictive modeling problem.

These include techniques for:

- Checking for a significant difference between results.
- Quantifying the size of the difference between results.

This might include the use of statistical hypothesis tests.

### 4. Statistics in Model Presentation

Statistical methods are required when presenting the skill of a final model to stakeholders.

This includes techniques for:

- Summarizing the expected skill of the model on average.
- Quantifying the expected variability of the skill of the model in practice.

This might include estimation statistics such as confidence intervals.

### 5. Statistics in Prediction

Statistical methods are required when making a prediction with a finalized model on new data.

This includes techniques for:

- Quantifying the expected variability for the prediction.

This might include estimation statistics such as prediction intervals.

### Your Task

For this lesson, you must list three reasons why you personally want to learn statistics.

Post your answer in the comments below. I would love to see what you come up with.

In the next lesson, you will discover a concise definition of statistics.

## Lesson 02: Introduction to Statistics

In this lesson, you will discover a concise definition of statistics.

Statistics is a required prerequisite for most books and courses on applied machine learning. But what exactly is statistics?

Statistics is a subfield of mathematics. It refers to a collection of methods for working with data and using data to answer questions.

It is because the field is comprised of a grab bag of methods for working with data that it can seem large and amorphous to beginners. It can be hard to see the line between methods that belong to statistics and methods that belong to other fields of study.

When it comes to the statistical tools that we use in practice, it can be helpful to divide the field of statistics into two large groups of methods: descriptive statistics for summarizing data, and inferential statistics for drawing conclusions from samples of data.

**Descriptive Statistics**: Descriptive statistics refer to methods for summarizing raw observations into information that we can understand and share.**Inferential Statistics**: Inferential statistics is a fancy name for methods that aid in quantifying properties of the domain or population from a smaller set of obtained observations called a sample.

### Your Task

For this lesson, you must list three methods that can be used for each descriptive and inferential statistics.

Post your answer in the comments below. I would love to see what you discover.

In the next lesson, you will discover the Gaussian distribution and how to calculate summary statistics.

## Lesson 03: Gaussian Distribution and Descriptive Stats

In this lesson, you will discover the Gaussian distribution for data and how to calculate simple descriptive statistics.

A sample of data is a snapshot from a broader population of all possible observations that could be taken from a domain or generated by a process.

Interestingly, many observations fit a common pattern or distribution called the normal distribution, or more formally, the Gaussian distribution. It is the bell-shaped distribution that you may be familiar with.

A lot is known about the Gaussian distribution, and as such, there are whole sub-fields of statistics and statistical methods that can be used with Gaussian data.

Any Gaussian distribution, and in turn any data sample drawn from a Gaussian distribution, can be summarized with just two parameters:

**Mean**. The central tendency or most likely value in the distribution (the top of the bell).**Variance**. The average difference that observations have from the mean value in the distribution (the spread).

The units of the mean are the same as the units of the distribution, although the units of the variance are squared, and therefore harder to interpret. A popular alternative to the variance parameter is the **standard deviation**, which is simply the square root of the variance, returning the units to be the same as those of the distribution.

The mean, variance, and standard deviation can be calculated directly on data samples in NumPy.

The example below generates a sample of 100 random numbers drawn from a Gaussian distribution with a known mean of 50 and a standard deviation of 5 and calculates the summary statistics.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
# calculate summary stats from numpy.random import seed from numpy.random import randn from numpy import mean from numpy import var from numpy import std # seed the random number generator seed(1) # generate univariate observations data = 5 * randn(10000) + 50 # calculate statistics print('Mean: %.3f' % mean(data)) print('Variance: %.3f' % var(data)) print('Standard Deviation: %.3f' % std(data)) |

Run the example and compare the estimated mean and standard deviation from the expected values.

### Your Task

For this lesson, you must implement the calculation of one descriptive statistic from scratch in Python, such as the calculation of a sample mean.

Post your answer in the comments below. I would love to see what you discover.

In the next lesson, you will discover how to quantify the relationship between two variables.

## Lesson 04: Correlation Between Variables

In this lesson, you will discover how to calculate a correlation coefficient to quantify the relationship between two variables.

Variables in a dataset may be related for lots of reasons.

It can be useful in data analysis and modeling to better understand the relationships between variables. The statistical relationship between two variables is referred to as their correlation.

A correlation could be positive, meaning both variables move in the same direction, or negative, meaning that when one variable’s value increases, the other variables’ values decrease.

**Positive Correlation**: Both variables change in the same direction.**Neutral Correlation**: No relationship in the change of the variables.**Negative Correlation**: Variables change in opposite directions.

The performance of some algorithms can deteriorate if two or more variables are tightly related, called multicollinearity. An example is linear regression, where one of the offending correlated variables should be removed in order to improve the skill of the model.

We can quantify the relationship between samples of two variables using a statistical method called Pearson’s correlation coefficient, named for the developer of the method, Karl Pearson.

The *pearsonr()* NumPy function can be used to calculate the Pearson’s correlation coefficient for samples of two variables.

The complete example is listed below showing the calculation where one variable is dependent upon the second.

1 2 3 4 5 6 7 8 9 10 11 12 13 |
# calculate correlation coefficient from numpy.random import seed from numpy.random import randn from scipy.stats import pearsonr # seed random number generator seed(1) # prepare data data1 = 20 * randn(1000) + 100 data2 = data1 + (10 * randn(1000) + 50) # calculate Pearson's correlation corr, p = pearsonr(data1, data2) # display the correlation print('Pearsons correlation: %.3f' % corr) |

Run the example and review the calculated correlation coefficient.

### Your Task

For this lesson, you must load a standard machine learning dataset and calculate the correlation between each pair of numerical variables.

Post your answer in the comments below. I would love to see what you discover.

In the next lesson, you will discover statistical hypothesis tests.

## Lesson 05: Statistical Hypothesis Tests

In this lesson, you will discover statistical hypothesis tests and how to compare two samples.

Data must be interpreted in order to add meaning. We can interpret data by assuming a specific structure our outcome and use statistical methods to confirm or reject the assumption.

The assumption is called a hypothesis and the statistical tests used for this purpose are called statistical hypothesis tests.

The assumption of a statistical test is called the null hypothesis, or hypothesis zero (H0 for short). It is often called the default assumption, or the assumption that nothing has changed. A violation of the test’s assumption is often called the first hypothesis, hypothesis one, or H1 for short.

**Hypothesis 0 (H0)**: Assumption of the test holds and is failed to be rejected.**Hypothesis 1 (H1)**: Assumption of the test does not hold and is rejected at some level of significance.

We can interpret the result of a statistical hypothesis test using a p-value.

The p-value is the probability of observing the data, given the null hypothesis is true.

A large probability means that the H0 or default assumption is likely. A small value, such as below 5% (o.05) suggests that it is not likely and that we can reject H0 in favor of H1, or that something is likely to be different (e.g. a significant result).

A widely used statistical hypothesis test is the Student’s t-test for comparing the mean values from two independent samples.

The default assumption is that there is no difference between the samples, whereas a rejection of this assumption suggests some significant difference. The tests assumes that both samples were drawn from a Gaussian distribution and have the same variance.

The Student’s t-test can be implemented in Python via the *ttest_ind()* SciPy function.

Below is an example of calculating and interpreting the Student’s t-test for two data samples that are known to be different.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
# student's t-test from numpy.random import seed from numpy.random import randn from scipy.stats import ttest_ind # seed the random number generator seed(1) # generate two independent samples data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 # compare samples stat, p = ttest_ind(data1, data2) print('Statistics=%.3f, p=%.3f' % (stat, p)) # interpret alpha = 0.05 if p > alpha: print('Same distributions (fail to reject H0)') else: print('Different distributions (reject H0)') |

Run the code and review the calculated statistic and interpretation of the p-value.

### Your Task

For this lesson, you must list three other statistical hypothesis tests that can be used to check for differences between samples.

Post your answer in the comments below. I would love to see what you discover.

In the next lesson, you will discover estimation statistics as an alternative to statistical hypothesis testing.

## Lesson 06: Estimation Statistics

In this lesson, you will discover estimation statistics that may be used as an alternative to statistical hypothesis tests.

Statistical hypothesis tests can be used to indicate whether the difference between two samples is due to random chance, but cannot comment on the size of the difference.

A group of methods referred to as “*new statistics*” are seeing increased use instead of or in addition to p-values in order to quantify the magnitude of effects and the amount of uncertainty for estimated values. This group of statistical methods is referred to as estimation statistics.

Estimation statistics is a term to describe three main classes of methods. The three main

classes of methods include:

**Effect Size**. Methods for quantifying the size of an effect given a treatment or intervention.**Interval Estimation**. Methods for quantifying the amount of uncertainty in a value.**Meta-Analysis**. Methods for quantifying the findings across multiple similar studies.

Of the three, perhaps the most useful methods in applied machine learning are interval estimation.

There are three main types of intervals. They are:

**Tolerance Interval**: The bounds or coverage of a proportion of a distribution with a specific level of confidence.**Confidence Interval**: The bounds on the estimate of a population parameter.**Prediction Interval**: The bounds on a single observation.

A simple way to calculate a confidence interval for a classification algorithm is to calculate the binomial proportion confidence interval, which can provide an interval around a model’s estimated accuracy or error.

This can be implemented in Python using the *confint()* Statsmodels function.

The function takes the count of successes (or failures), the total number of trials, and the significance level as arguments and returns the lower and upper bound of the confidence interval.

The example below demonstrates this function in a hypothetical case where a model made 88 correct predictions out of a dataset with 100 instances and we are interested in the 95% confidence interval (provided to the function as a significance of 0.05).

1 2 3 4 5 |
# calculate the confidence interval from statsmodels.stats.proportion import proportion_confint # calculate the interval lower, upper = proportion_confint(88, 100, 0.05) print('lower=%.3f, upper=%.3f' % (lower, upper)) |

Run the example and review the confidence interval on the estimated accuracy.

### Your Task

For this lesson, you must list two methods for calculating the effect size in applied machine learning and when they might be useful.

As a hint, consider one for the relationship between variables and one for the difference between samples.

Post your answer in the comments below. I would love to see what you discover.

In the next lesson, you will discover nonparametric statistical methods.

## Lesson 07: Nonparametric Statistics

In this lesson, you will discover statistical methods that may be used when your data does not come from a Gaussian distribution.

A large portion of the field of statistics and statistical methods is dedicated to data where the distribution is known.

Data in which the distribution is unknown or cannot be easily identified is called nonparametric.

In the case where you are working with nonparametric data, specialized nonparametric statistical methods can be used that discard all information about the distribution. As such, these methods are often referred to as distribution-free methods.

Before a nonparametric statistical method can be applied, the data must be converted into a rank format. As such, statistical methods that expect data in rank format are sometimes called rank statistics, such as rank correlation and rank statistical hypothesis tests. Ranking data is exactly as its name suggests.

The procedure is as follows:

- Sort all data in the sample in ascending order.
- Assign an integer rank from 1 to N for each unique value in the data sample.

A widely used nonparametric statistical hypothesis test for checking for a difference between two independent samples is the Mann-Whitney U test, named for Henry Mann and Donald Whitney.

It is the nonparametric equivalent of the Student’s t-test but does not assume that the data is drawn from a Gaussian distribution.

The test can be implemented in Python via the *mannwhitneyu()* SciPy function.

The example below demonstrates the test on two data samples drawn from a uniform distribution known to be different.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
# example of the mann-whitney u test from numpy.random import seed from numpy.random import rand from scipy.stats import mannwhitneyu # seed the random number generator seed(1) # generate two independent samples data1 = 50 + (rand(100) * 10) data2 = 51 + (rand(100) * 10) # compare samples stat, p = mannwhitneyu(data1, data2) print('Statistics=%.3f, p=%.3f' % (stat, p)) # interpret alpha = 0.05 if p > alpha: print('Same distribution (fail to reject H0)') else: print('Different distribution (reject H0)') |

Run the example and review the calculated statistics and interpretation of the p-value.

### Your Task

For this lesson, you must list three additional nonparametric statistical methods.

Post your answer in the comments below. I would love to see what you discover.

This was the final lesson in the mini-course.

## The End!

(Look How Far You Have Come)

You made it. Well done!

Take a moment and look back at how far you have come.

You discovered:

- The importance of statistics in applied machine learning.
- A concise definition of statistics and a division of methods into two main types.
- The Gaussian distribution and how to describe data with this distribution using statistics.
- How to quantify the relationship between the samples of two variables.
- How to check for the difference between two samples using statistical hypothesis tests.
- An alternative to statistical hypothesis tests called estimation statistics.
- Nonparametric methods that can be used when data is not drawn from the Gaussian distribution.

This is just the beginning of your journey with statistics for machine learning. Keep practicing and developing your skills.

Take the next step and check out my book on Statistical Methods for Machine Learning.

## Summary

How did you do with the mini-course?

Did you enjoy this crash course?

Do you have any questions? Were there any sticking points?

Let me know. Leave a comment below.

Why not R ?

Great question, I explain why here:

http://machinelearningmastery.com/python-growing-platform-applied-machine-learning/

To understand how the ML algorithms work behind the scenes.

To understand how to the Machine Learning algorithms work behind the scenes.

Descriptive – Median, Standard Deviation, Mode

Inferential – AUC, Kappa-Statistics Test, Confusion Matrix, F-1 Score

Hey Aradhika.. Thanks for the valuable input. Could you let me know the URL for the course. I am unable to access the same.

This page is the course.

Inspired. Thank you for the deep description with practical codes. I really learnt a lot. Appreciate your work.

I’m glad it helped.

Why I want to learn statistics:

– I’d like to understand what I’m doing while training a model and whether it makes sense: bias, assumptions, that matters a lot;

– I’d like to understand the difference between classical statistical and bayesian methods;

– I’d like to learn to compare models in more detail than just by looking at accuracy figures.

Thanks Nadya!

@ Jason: I am unable to access the link for the mini course. Could you let me know the correct URL.

Would really appreciate it!

What link?

Can we also check correlation among input features using statistical hypothesis test?

Sure.

I would like to learn statistics to deepen my understanding of ML and have a fair background on statistics

Great, you’re in the right place!

Hello Jason,

In response of task of lesson 02, I found:

– as descriptive statistics normal (or Gaussian), binomial and Poisson distributions.

– as inferential methods we have ANOVA, t-tests and regression analysis.

Is it correct?

Nice!

Hi Jason,

I did the task of lesson 03 and here’s my code to calculate from scratch a sample mean.

Hope that’s the task you asked for.

Nice work!

Hi Jason,

I used local iris dataset for the task of lesson 4.

Bellow is my code to calculate correlation between each pair of sepal and petal variables

Well done!

Hi Jason,

In replay to lesson 5 task, I found as statistical hypothesis test the following method:

– The Wald test (also called the Wald Chi-Squared Test) is a way to find out if explanatory variables in a model are sognificant. “Significant” means that they add something to the model; variables that add nothing can be deleted without affecting the model in any meaningful way.

– The Kolmogorov-Smirnov Goodness of Fit Test (K-S test) compares your data with a known distribution and lets you know if they have the same distribution.

– Granger causality test is a way to investigate causality between two variables in a time series

Well done!

Hi Jason,

For lesson 6 task I found that there are more than 70 effect size measures mainly grouped into two groups:

– correlation family or measures of association, a.k.a r family. E.g:

Pearson’s r or correlation coefficient to measure correlation between dependent variables.

Eta-squared to describe the ratio of variance within dependent variables.

– difference family or difference between groups, a.k.a d family. The computation resembles to t-test statistic without being affected by the sample size. Which is not the case of t-test statistic. E.g:

Cohen’s d defined as the difference between two means for two independent samples divided by standard deviation for the data.

Nice work!

Hi Jason,

The three additional nonparametric statistical methods, in reply to lesson 7 task, that I found are:

Anderson-Darling test: tests whether a sample is drawn from a giving distribution

Cochran’s Q: tests whether k treatments in randomized block designs with 0/1 outcomes have the identical effects

Kendall’s tau: measures statistical dependence between two variables

Thanks.

Thanks to you Jason. I really enjoyed your mini course. It give me the quick introduction that I look for to that field.

Thanks again.

Thanks.

I have done all the basic Machine Learning and Deep Learning from Andrew Ng’s courses, but now I’ve got an internship and it is more focusing on data analytics and getting insights from the dataset. Hence I want to learn the statistics.

Thanks!

1) I have a specific business problem I’d like to solve that involves ML and I know statistics is important for this (not just because you said so, Jason).

2) I’ve always found statistics dry due to the way its taught in classrooms, with little context and requiring a lot of procedural memorization. I’m encouraged to learn a deeper understanding will give me the opportunity to solve a relevant problem, increasing my motivation to learn more.

3) I want to be able to better speak the language of data for business intelligence reasons

Thanks Sean.

Such a beautiful article. Thanks jason for helping the machine learning community.

Thanks, I’m glad it helped.

Day1 task: list three reasons why you personally want to learn statistics.

1) I am interested in learning machine learning and its implementation in the real-world scenarios

2) As you mentioned in 1st day, how the statistic is used in all phases of machine learning

3) Only knowing ML algorithms is not enough, according to me statistics is also important to get useful insights from data.

Thanks!

3 Reasons that made me want to learn statistics:

1. I currently have a deep learning project for an internship. Statistics are essential for machine learning and machine learning is essential for deep learning. See how it goes? ;D

2. I study computer science, learning what statistics is all about (in general) will help me broaden my mind in other scientific fields out of programming.

3. I currently suck at math, learning a subset field of math will gradually make me one step better at them.

Thanks Floris!

I will like to this book or a down load

You can learn more about the book here:

https://machinelearningmastery.com/statistics_for_machine_learning/

1. I have to prepare model presentation to stackholders

2. I need to sell sw solution that include machine learning models

3. I’m an engineer

Thanks!

Answer to your lesson 2. Descriptive: frequency, central tendency, variation.. Inferential: Variance (ANOVA), Analysis of Covariance (ANCOVA), regression analysis

Nice work!

Answer to your lesson 3 (i hope this is right):

Nice!

Hi Jason, this is the core of code for your question number 4 (i only include the final calculation considering in datas al the informations already structured.

Nice work.

Jason, my answer for lesson 05:

Z-test that use sample and population mean and sample and population standard variation to verify the null Hipothesys, is the sample mean the same than the population mean?

Anova compare differences between three or ore sample. Null hipothesys is all smaple means are equal

Chi square test compare categorical variables and if a sample match a population. Null hipothesys is variable a and b are independent (a sample match a population)

Nice work.

Task for lesson 06:

There are two types of statistics that describe the size of an effect.

The first type is standardized, this type remove the units of the variables in the effect.

The second type is simple and describe the size of the effect, but remain in the original units of the variables.

Comparing the mean temperature under two different conditions.

The simple effect size would be the difference in the mean temperature in degrees Celsius.

The standardized effect size statistic would divide that mean difference by the standard deviation.

So if you have two conditions for temperature simple effect size would result in the mean temperature in condition 1 is 23 degres higher than in condition 2.

Standardized effect size would result in the mean temperature in condition 1 is 1.8 standard variation higher than in condition 2.

I hope this is the correct

Thanks.

Lesson 07:

1) Kruskal–Wallis test of the hypothesis that several samples are from the same

population. This test is a multisample generalization of the two-sample Wilcoxon (Mann–Whitney) rank-sum test.

2) Cusum graphs the cumulative sum (cusum) of a binary (0/1) variable, yvar, against a (usually) continuous variable, xvar.

3) Trend test performs a nonparametric test for trend across ordered groups

There any many others methods. Thanks for this course that has been very useful for me. I was searching for something that helps me to understand basic for machine learning

Very well done, thanks for posting all of your answers!

Lesson 1:

1) I have always had some curiosity on AI and how it work.

2) Machine learning has such a big field for its uses.

3) This is one of the fields of computer science that I like the most.

4) Knowing that there are some things you can really predict with certain amount of accurary is something that I would definitely want to know (bonus)

Nice work!

Lesson 2:

Descriptive statistics:

* Dispersion

* Standard Deviation

* Kurtosis and Skewness

Inferentia statistics:

* Analysis of Covariance (Ancova)

* Factor Analysis

* Cluster Analysis

Well done!

I am interested in learning statistics as I was always fascinated by how statistics can be made use of in machine learning.

Thanks!

Descriptive Statistics:

1. Mean, median, mode

2. Skewness and kurtosis

3. Variance and standard deviation

Inferential Statistics:

1. Estimation

1. Maximum likelihood estimation

2. Density estimation

2. Hypothesis testing

3. Confidence intervals

Well done!

Hi Jason, thanks for spreading the knowledge.

Day 1:

1. I like to work across different disciplines and stat is the crux to understanding or discover insights from any data. for one descriptive stat, central tendency and much more

2. to understand data interpretability at depth. As stat is the interpretive language of understanding data.

4. understand to apply right method to the right kind of data.

Thanks for sharing!

#Lesson 2

#For this lesson, you must implement the calculation of one descriptive statistic from scratch in #Python, such as the calculation of a sample mean.

#I applied this sample with Iris dataset:

import numpy as np

import math

from sklearn import datasets

iris = datasets.load_iris()

#Attributes

#1. sepal length in cm

#2. sepal width in cm

#3. petal length in cm

#4. petal width in cm

X = iris.data

print(X.size)

print(X.shape)

#column 0..all lines

sepal_lenghts = X[: , 0]

print(sepal_lenghts.size)

print(sepal_lenghts.shape)

#same thing was done above

sepal_width = X[:,1]

petal_lenght = X[:,2]

petal_width = X[:,3]

#Calculate the mean, variance and standard deviation “by hand”! ————-##

#Mean “by hand” ——————-##

def mean_by_hand(data):

i_arr_summation = 0

for x in np.nditer(data):

i_arr_summation += x

size_data = data.size

mean_data = i_arr_summation / size_data

return mean_data

#Variance “by hand” ——————————————————-###

def variance_by_hand(data, mean_data, n_data):

sum_var = 0

for x in np.nditer(data):

i_var = x – mean_data #variance (xi – mi)

i_var *= i_var # ^2

sum_var += i_var #summation

variance = (1/n_data) * sum_var

return variance

#Standard deviation “by hand”. Are you serious?! ————————–####

def standard_dev_by_hand(variance):

standard_dev = math.sqrt(variance) #or variance**0.5

return standard_dev

#Calling the functions to calculate mean, var and std ———–##############

#Mean ————————————————####

mean_sepal_lenghts = mean_by_hand(sepal_lenghts)

print(“mean sepal_lenght:”, mean_sepal_lenghts)

print(“NUMPY mean sepal_lenght:”, np.mean(sepal_lenghts))

#Variance ————————————————####

n_sepal_lenghts = sepal_lenghts.size

var_sepal_lenghts = variance_by_hand(sepal_lenghts, mean_sepal_lenghts, n_sepal_lenghts)

print(“var sepal_lenght:”, var_sepal_lenghts)

print(“NUMPY var sepal_lenght:”, np.var(sepal_lenghts))

#Standard deviation————————————–####

std_sepal_lengths = standard_dev_by_hand(var_sepal_lenghts)

print(“std sepal_lenght:”, std_sepal_lengths)

print(“NUMPY std sepal_lenght:”, np.std(sepal_lenghts))

Corrected: #Lesson 03: Gaussian Distribution and Descriptive Stats

Nice work!

#lesson 4: Correlation between variables

#I applied this sample in Iris dataset, specifically in atts sepal_lenght and sepal_width to

#discover if they are correlated or not

import numpy as np

from sklearn import datasets

iris = datasets.load_iris()

# calculate correlation coefficient

from numpy.random import seed

from numpy.random import randn

from scipy.stats import pearsonr

#Attributes

#1. sepal length in cm

#2. sepal width in cm

#3. petal length in cm

#4. petal width in cm

X = iris.data

print(X.size)

print(X.shape)

#column 0..all lines

sepal_lenghts = X[: , 0]

sepal_width = X[:,1]

print(sepal_lenghts)

type(sepal_lenghts)

print(sepal_lenghts.shape)

print(sepal_lenghts.size)

print(sepal_width)

type(sepal_width)

print(sepal_width.shape)

print(sepal_width.size)

# calculate Pearson’s correlation

corr, p = pearsonr(sepal_lenghts, sepal_width)

# display the correlation: in this case, NEGATIVE CORRELATION

print(‘Pearsons correlation: %.3f’ % corr)

Thanks for sharing!