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#### Learn more about Summary Statistics with these recommended learning resources

#### Going beyond summary statistics

I recently came across the Datasaurus dataset by Alberto Cairo on TidyTuesday and wanted to create a series of charts illustrating the lessons associated with this dataset, primarily to: never trust…

Read more at Towards Data Science#### Reading and interpreting summary statistics

A typical data science project starts with data wrangling. It is the process of cleaning messy data and transforming them into appropriate formats for further analysis and modeling. The next step in…

Read more at Towards Data Science#### Descriptive statistics

It does exactly as the name suggest ‘describe’ which summarize the raw data with help of graphs and overall summary and is easily interpretable by humans. In short it helps us understand “What has…

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Statistics Basic concepts in statistics for machine learning. References [1] Example

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VaR stands for Value-at-Risk. It’s a hugely important component of any form of trade because it is a straightforward method to quantify the risk of a single asset or entire portfolio at any point in…

Read more at Towards AI#### Appendix: Statistics

These notes are intended to provide a “fast” collection of theoretical definitions, theorems, and concepts for Statistics as a refresher and a collection of definitions, theorems, and corollaries…

Read more at Analytics Vidhya#### Descriptive Statistics — IV

We can understand Percentile with a scenario. Say if a college wants to select students for a course based on entrance exam. They have a cutoff of 70%, any who scores above or equal to 70% will be…

Read more at Analytics Vidhya#### Descriptive Statistics — III

Calculating Median from a range of values is simple. Recall from a range of values [10,12,13,15,17,20,21] = the median is 15 i.e., the centre value Consider the 1st and 2nd column.We have 5 bins of…

Read more at Analytics Vidhya#### A Gentle Introduction to Calculating Normal Summary Statistics

Last Updated on August 8, 2019 A sample of data is a snapshot from a broader population of all possible observations that could be taken of a domain or generated by a process. Interestingly, many obse...

Read more at Machine Learning Mastery#### Descriptive Statistics for Data Science.

Statistics is the building block for data science and it’s important for a data scientist to have a hold on it. Learning and staying up to the mark is a tedious task and is something data scientists…

Read more at Analytics Vidhya#### Complete Guide to Statistics — Descriptive Statistics: Part-1

The Ultimate Guide to Statistics: Part 1— Descriptive Statistics Introduction Welcome to my statistics blog series! We’ll explore several different subjects in this series, including Descriptive Stat...

Read more at Towards AI#### Intro to Descriptive Statistics

Descriptive Statistical Analysis helps you to understand your data and is a very important part of Machine Learning. This is due to Machine Learning being all about making predictions. On the other…

Read more at Towards Data Science#### Descriptive statistics by hand

This article explains how to compute the main descriptive statistics by hand and how to interpret them. To learn how to compute these measures in R, read the article “Descriptive statistics in R”…

Read more at Towards Data Science#### How to derive summary statistics using PostgreSQL

In this article, we’ll discuss how to derive summary statistics of numerical and categorical columns/fields using SQL. We’ll use the Netflix Movies and TV Shows dataset that’s downloaded from Tableau…...

Read more at Towards Data Science#### Telling the full story of Descriptive Statistics with numbers!

Statistics can be made to prove anything even the truth! So it is of paramount importance to really understand it. Statistics is dealing with the collection, analysis, interpretation, and…

Read more at Towards Data Science#### Statistics 2.

I am gonna emphasize the KEY POINTS of Hypothesis and other tests(One-Sample T-TEST, One-Sample Z-test, One –Way ANOVA, Chi-square Goodness of Fit Test) Hypothesis testing refers to the formal…

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Statistics ( scipy.stats ) Introduction In this tutorial, we discuss many, but certainly not all, features of scipy.stats . The intention here is to provide a user with a working knowledge of this pac...

Read more at SciPy User Guide#### Mastering Summary Statistics with Pandas

Pandas is a python library used for data manipulation and statistical analysis. It is a fast and easy to use open-source library that enables several data manipulation tasks. These include merging…

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Undoubtedly, to be a top deep learning practitioner, the ability to train the state-of-the-art and high accurate models is crucial. However, it is often unclear when improvements are significant, or o...

Read more at Dive intro Deep Learning Book#### A Checklist of Basic Statistics

A checklist of basic statistics on statistical inference, experimental design, and hypothesis testing.

Read more at Towards Data Science#### Guide to Using Descriptive Statistics in Data Science

Understand the key concepts to summarize data Photo by Cathryn Lavery on Unsplash Statistics are at the heart of data science and data analysis. Understanding the basic concepts and knowing when to p...

Read more at Towards AI#### Introduction to the Descriptive Statistics

Descriptive statistics summarize, show, and analyze the data and make it more understandable. If the dataset is large, it is hard to make any sense from the raw data. Using descriptive statistics…

Read more at Towards Data Science#### Basic Statistics: A BrushUp

If you are like me, you’ve taken a stats course, most of us have. Often, taking a class does not signify mastery more than skilled retention, however. With this article, I hope to reintroduce some to…...

Read more at Level Up Coding#### Statistics In Data Science

Maths is probably one of the most important topics that are the core of almost all the advances in technology. The filed of data science wouldn’t have existed without maths. All the subfields in data…...

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