Statistics 101: Introduction to the Central Limit Theorem (with implementation in R)

Analytics Vidhya

Introduction What is one of the most important and core concepts of statistics that enables us to do predictive modeling, and yet it often. The post Statistics 101: Introduction to the Central Limit Theorem (with implementation in R) appeared first on Analytics Vidhya.

Statistical Modelling vs Machine Learning


At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. 2019 Aug Opinions Uncategorized Advice Data Science Machine Learning Statistics

Statistics for Data Science: Introduction to t-test and its Different Types (with Implementation in R)

Analytics Vidhya

Introduction “You can’t prove a hypothesis; you can only improve or disprove it.” – Christopher Monckton Every day we find ourselves testing new ideas, The post Statistics for Data Science: Introduction to t-test and its Different Types (with Implementation in R) appeared first on Analytics Vidhya. R Statistics Hypothesis Testing Inferential Statistics statistics t-test

Descriptive Statistics and Data Visualization


Turn Your Statistics Into Something More Interesting Data is quickly becoming a defining thing in the business world. A company which doesn’t pay attention to proper statistics can be at a serious disadvantage from companies who do, especially companies that […].

Quantifying a Culture of Innovation

A Detailed Guide to 7 Loss Functions for Machine Learning Algorithms with Python Code

Analytics Vidhya

Machine Learning Python Statistics loss functions loss functions machine learning loss functions statistics machine learning regression loss statisticsOverview What are loss functions? And how do they work in machine learning algorithms?

Machine Learning Vs. Statistical Learning

Perficient Data & Analytics

Most of the time as a data scientist I get asked the question, what is the difference between Machine Learning and Statistical Learning? To become a data scientist, you are quired to develop knowledge in multiple subjects such as Statistics, Programming, SQL, Linear Algebra and have the domain expertise. Hopefully, you will start your journey with Statistics, and most of the data scientists believe that this is the foundation in Data Science and I cannot disagree with them.

Statistical Thinking for Industrial Problem Solving (STIPS) – a free online course.


2019 Aug Courses, Education JMP Online Education StatisticsThis online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.

How and when to calculate statistical significance

Mixpanel on Data

Few professionals assess the statistical accuracy of their studies. What keeps teams from checking the statistical significance of their results? What is a statistical significance test? There are a wide variety of biases to consider when assessing a statistical test.

UI Alerts and Statistics


How do we manage a very large and complex product that could potentially have hundreds or thousands of entities

Statistics for Google Sheets

The Unofficial Google Data Science Blog

The statistics app for Google Sheets hopes to change that. Editor's note: We've mostly portrayed data science as statistical methods and analysis approaches based on big data. hope to replace R, SAS, or similar packages designed by and for statistics experts. Statistics ?

Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!

Analytics Vidhya

Machine Learning Statistics amazon review system bayes theorem bayesian statistics bayesian stats data science online reviews statisticsOverview Curious how the big product companies like Amazon, Walmart, AirBnb, etc. manage the ratings we see? The core idea behind these ratings systems. The post Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! appeared first on Analytics Vidhya.

Guest Post: Galin Jones on criteria for promotion and tenture in (bio)statistics departments

Simply Statistics

After giving my talk Galin Jones , Professor and Director of Statistics at University of Minnesota, and I had an interesting conversation about how they had changed their promotion criteria in response to a faculty candidate being unique. This is often code for publishing as many articles as possible in the big four journals–JASA, Biometrika, JRSSB, and the Annals of Statistics.

DATAMIN – Unveiling the World’s Biggest Online Data Science Quizzing Platform

Analytics Vidhya

Analytics Vidhya Career Data Science data science questions data science quiz Datamin machine learning machine learning quiz statistics statistics quizWe are thrilled to announce the launch of the world’s biggest online data science quizzing platform: Datamin! Do you feel some of your peers. The post DATAMIN – Unveiling the World’s Biggest Online Data Science Quizzing Platform appeared first on Analytics Vidhya.

What is Descriptive Statistics and How Do You Choose the Right One for Enterprise Analysis?


This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms. What is a Descriptive Statistics? How Does One Choose the Right Descriptive Statistics Algorithm for Enterprise Analysis?

11 Important Model Evaluation Metrics for Machine Learning Everyone should know

Analytics Vidhya

Machine Learning Python Statistics AUC concordant ratio confusion matrix cross-validation discordant ratio error metrics gain and lift charts gini coefficient k fold validation kolmogorov smirnov charts Predictive modeling ROC

Extending Hive Replication: Transactional Tables, External Tables, and Statistics


ACID tables), external tables and statistics associated with all kinds of tables. Statistics. Statistics are vital for query planning and optimization. Query planner uses statistics to choose the fastest possible execution plan for a given query.

What is Poisson Distribution?


2019 Aug Tutorials, Overviews Distribution Probability StatisticsAn solid overview of the Poisson distribution, starting from why it is needed, how it stacks up to binomial distribution, deriving its formula mathematically, and more.

IT 72

Excellent Analytics Tip#1: Statistical Significance

Occam's Razor

Leverage the power of Statistics. Applying statistics tells us that the results, the two conversion rates, are just 0.995 standard deviations apart and not statistically significant. Applying statistics will now tell us that the two numbers are 1.74 standard deviations apart and the results rate 95% statistically significant. Either something is Statistically Significant, and we take action, or we say it is not Significant and let's try something else.

P-values Explained By Data Scientist


2019 Jul Tutorials, Overviews Data Science Data Scientist P-value StatisticsThis article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.

Inside the Mind and Methodology of a Data Scientist

Birst BI

A foundational data analysis tool is Statistics , and everyone intuitively applies it daily. Statistics provides the mathematical foundation to determine how data behaves and when it is exceptional.

Variety is the Secret Sauce for Big Discoveries in Big Data

Rocket-Powered Data Science

Big Data Data Science Analytics Machine Learning StatisticsWhen I was out for a walk recently, I heard a loud low-flying aircraft passing overhead. This was not unusual since we live in the flight path of planes landing at a major international airport about 10 miles from our home.

Hackers beware: Bootstrap sampling may be harmful

Data Science and Beyond

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics.

As Nice as Pie

Peter James Thomas

While the history is not certain, most authorities credit the pioneer of graphical statistics, William Playfair , with creating this icon, which appeared in his Statistical Breviary, first published in 1801 [2]. data visualisation Statistics bar chart pie chart

Sales 81

Convergent Evolution

Peter James Thomas

Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. No this article has not escaped from my Maths & Science section , it is actually about data matters.

More Definitions in the Data and Analytics Dictionary

Peter James Thomas

big data business analytics business intelligence chief data officer dashboards data governance data management data quality data science data visualisation data warehousing Statistics

Fact-based Decision-making

Peter James Thomas

Integrity of statistical estimates based on Data. Having spent 18 years working in various parts of the Insurance industry, statistical estimates being part of the standard set of metrics is pretty familiar to me [7]. This article is about facts.

Glossaries of Data Science Terminology

Rocket-Powered Data Science

Here is a compilation of glossaries of terminology used in data science, big data analytics, machine learning, AI, and related fields: Glossary of common Machine Learning, Statistics and Data Science terms. 100’s of Statistical Concepts Explained in Simple English.

Why The Future of Finance Is Data Science


Statistics vs. Data Analytics. Statistics are a vital part of learning customer basis and seeing exactly what is occurring within the finance company and how it can be improved. There is a difference between analytics and statistics.

Defining data science in 2018

Data Science and Beyond

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. Like other authors, they argue that causal inference has been neglected by traditional statistics and some scientific disciplines. Data science analytics artificial intelligence business data science machine learning statistics

The most practical causal inference book I’ve read (is still a draft)

Data Science and Beyond

Causal inference Data science causality data science statisticsI’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work.

Customer lifetime value and the proliferation of misinformation on the internet

Data Science and Beyond

My main problem with the Kissmetrics infographic is that it helps feed an illusion of understanding that is prevalent among those with no statistical training. The rise of data science increases the availability of statistical and scientific tools to small and large businesses.

8 Useful R Packages for Data Science You Aren’t Using (But Should!)

Analytics Vidhya

I have relied on it since my days of learning statistics back in. Introduction I’m a big fan of R – it’s no secret. The post 8 Useful R Packages for Data Science You Aren’t Using (But Should!) appeared first on Analytics Vidhya. Data Science Data Visualization Machine Learning R data science data visualization machine learning R package

Data Analysis 101: Seven Simple Mistakes That Limit Your Salary

Occam's Razor

In this case for my data it is not statistically significant (more on that later in this post), but there is no way you would know that (or not know that) just from the data in front of you. Statistical Significance is Your BFF. Is that data statistically significant?

Extracting and Analyzing 1000 Basketball Games using Pandas and Chartify

Analytics Vidhya

Introduction I love descriptive statistics. Visualizing data and analyzing trends is one of the most exciting aspects of any data science project. But what. The post Extracting and Analyzing 1000 Basketball Games using Pandas and Chartify appeared first on Analytics Vidhya. Machine Learning Python machine learning python web scraping

The Benefits of Using Cloud-Based Platforms for Data Science

Perficient Data & Analytics

The most straightforward approach for a data scientist is to keep everything as is, stay within the realm of Statistical Learning, continue working with small datasets. Advanced Analytics artificial intelligence AWS azure cloud Cloud-Based Platforms data science GCP ibm watson Machine Learning Statistical LearningAs everything in this world matures and goes through the steps of evolution, Data Science is not much different.

Tukey, Design Thinking, and Better Questions

Simply Statistics

Roughly once a year, I read John Tukey’s paper “The Future of Data Analysis” , originally published in 1962 in the Annals of Mathematical Statistics. In light of this, discussions about p-values and statistical significance are very much beside the point.

Interview with Abhi Datta

Simply Statistics

A lot of my work on spatial statistics is driven by applications in environmental health and air pollution. SS: How did you get into statistics? I had the option of going for engineering, medical or statistics undergrad. I chose statistics persuaded by my appreciation for mathematics and the reputation of the statistics program at Indian Statistical Institute (ISI), Kolkata.

Recent top-selling books in AI and Machine Learning

Rocket-Powered Data Science

Being Human in the Age of Artificial Intelligence” “An Introduction to Statistical Learning: with Applications in R” (7th printing; 2017 edition).

Top Data Science Tools That Will Empower Your Data Exploration Processes


To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. It helps to automate and makes the usage of the R programming statistical language easier and much more effective.