(testing signal)

Tag: statistics

DSC Weekly Digest 12 October 2021

Build statistical and analytical expertise as well as the management and leadership skills necessary to implement high-level, data-driven decisions in Northwestern’s Online MS in Data Science. Earn your degree entirely online in classes that are led by industry experts who are redefining how data is used to boost efficiency and effectiveness in a wide range of fields. Learn more

Get to know TIBCO’s enterprise analytics platform that allows data scientists and business…… Read more...

Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv…… Read more...

Four Different Pipes for R with magrittr

By Gregory Janesch, Recent Master’s in Statistics Graduate
The magrittr package is a part of the extended tidyverse – i.e., not one of the ones normally loaded. It is the one that supplies the pipe operator (%%), but it turns out that the package actually contains four pipe operators in total. All are intended to streamline and improve the readability of code, though the three non-basic ones are a bit more situational, and I’ve rarely seen them used, so I thought I would go…

https://www.kdnuggets.com/2021/10/four-different-pipes-r-magrittr.html…

How Artificial Intelligence Is Powering Search Engines

Whether you are a customer searching for your favorite products online, a writer looking for the latest statistics, or a business owner learning SEO skills, you are using a search engine to get answers. And search engines are pretty interesting! You open up your favorite one, add some related keywords and click to search. Within a fraction of a second, you get thousands of results for your entered keyword. It seems like magic. Except that it isn’t!
Search engines can perform the way they……

4 Risks of Storing Large Amounts of Unstructured Data

Click to learn more about author Gary Lyng.

In 2013, the big data headline was the incredible statistic that 90% of all data in the history of the entire human race had been created in the previous two years. The amount of structured and unstructured data we’ve created was so mind-boggling that we deemed it “big data.” Now it’s 2021 and that exponential growth has not slowed down – in fact, it has sped up. In 2020, each person generated an average of 1.7 megabytes of data per second. The sheer volume of data being created can be overwhelming to comprehend for one person – and especially for an enterprise organization. … Read more...


Heterogeneity is defined as a dissimilarity between elements that comprise a whole. When heterogeneity is present, there is diversity in the characteristic under study. The parts of the whole are different, not the same. It is an essential concept in science and statistics. Heterogeneous is the opposite of homogeneous.

Heterogeneous jelly beans!

In chemistry, a heterogeneous mixture has a composition that varies. For example, oil and vinegar, sand and water, and salt and pepper are all heterogeneous mixtures. Multiple samples of these mixtures will contain different proportions of each component.

In statistics, heterogeneity is a vital concept that appears in various contexts, and its definition varies accordingly.… Read more...

From intern to FTE: Four researchers share their journeys with the Facebook Core Data Science team – Facebook Research

Facebook’s Core Data Science (CDS) team is pushing the envelope of what’s possible by exploring and solving novel challenges in science and technology. The CDS internship program was designed to provide researchers with the opportunity to explore career paths in diverse fields of research, such as computer science, statistics, optimization, economics, and social science.

CDS interns are immersed in Facebook culture and see the direct impact their research has on product development, user experience, and the research community as a whole. When transitioning to full-time employees, researchers use passion and curiosity to drive their projects. Here, four researchers share their experiences with the program and what inspired them to transition to their full-time roles.… Read more...

Advanced Statistical Concepts in Data Science – KDnuggets

Credits: https://www.congruentsoft.com/business-intelligence.aspx

In my previous articles Beginners Guide to Statistics in Data Science and The Inferential Statistics Data Scientists Should Know we have talked about almost all the basics(Descriptive and Inferential) of statistics which are commonly used in understanding and working with any data science case study. In this article, lets go a little beyond and talk about some advance concepts which are not part of the buzz.

Q-Q(quantile-quantile) Plots

Before understanding QQ plots first understand what is a Quantile?

A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit.… Read more...

In-depth Introduction of Analysis of Variance (ANOVA) with R Examples | by Amit Chauhan | Sep, 2021

A statistics tool for analysis on features relationship

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A Gentle Introduction to Non-Parametric Tests

What are Non-parametric tests?

Most the statistical tests are optimal under various assumptions like independence, homoscedasticity or normality. However, it might not always be possible to guarantee that the data follows all these assumptions. Non-parametric tests are statistical methods which don’t need the normality assumption and the normality assumption can be replaced by a more general assumption concerning the distribution function.

Non-parametric and Distribution-free

Often the terms non-parametric and distribution-free are used interchangeably. However, these two terms are not exactly synonymous. A problem becomes parametric or non-parametric depending on whether we allow the parent distribution of the data to depend on a finite number of parameters or keep it more general (e.g.… Read more...

Let’s learn about Dimensionality Reduction

What is Dimensionality?

Dimensionality in statistics refers to “How many attributes a dataset has.”

For example:- We have data in spreadsheet format and we have vast amounts of variables (age, name, sex, Id, and so on..).

In a simple way “The number of input variables or features for a dataset is referred to as its dimensionality.”

Why Dimensional Reduction?

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable.… Read more...

Important Statistics Data Scientists Need to Know

By Lekshmi S. Sunil, IIT Indore ’23 | GHC ’21 Scholar.

Statistical analysis allows us to derive valuable insights from the data at hand. A sound grasp of the important statistical concepts and techniques is absolutely essential to analyze the data using various tools.

Before we go into the details, let’s take a look at the topics covered in this article:

  • Descriptive vs. Inferential Statistics
  • Data Types
  • Probability & Bayes’ Theorem
  • Measures of Central Tendency
  • Skewness
  • Kurtosis
  • Measures of Dispersion
  • Covariance
  • Correlation
  • Probability Distributions
  • Hypothesis Testing
  • Regression

Descriptive vs. Inferential Statistics

Statistics as a whole deals with the collection, organization, analysis, interpretation, and presentation of data.


Best Data Science Certifications In 2022

Over a span of the recent few years, data science has become an integral part of all the major industry sectors, ranging from agriculture, marketing analytics, public policy, to fraud detection, risk management, and marketing optimization. One of the goals of data science is to resolve the many issues that preside within the economy at large, and its other branches and individual sectors, through the use of machine learning, predictive modeling, statistics, and data preparation.

Data science emphasizes the utilization of the general methods but without changing its application, no matter what its domain is. In this way, this approach is a lot more different from the other traditional statistics scenario that usually tends to focus solely upon seeking specific solutions to particular domains or sectors.


How Data Scientists Can Compete in the Global Job Market – KDnuggets


The job market for data scientists is more active than ever and on track for rapid growth over the next few years. The U.S. Bureau of Labor Statistics predicts that the number of available positions will rise about 28% through 2026.

Companies are investing significant amounts of money into market research and business analysis, creating new opportunities for long-time data scientists and those new to the field. At the same time, the job market is also becoming more competitive. The average compensation for data science positions is rising as these jobs become more important to businesses, encouraging hiring managers to more carefully vet new hires.


How to be a Data Scientist without a STEM degree

1. Learn the fundamentals of all pillars of data science

“Data Science” is a vague term—it can mean different things to different companies, and there are a plethora of skills that are relevant to data scientists.

That being said, there are a few core skills that I recommend that you learn. The following skills are pivotal for any data scientist: SQL, Python, Statistics, Machine Learning. I also recommend that you learn these skills in that order. It may sound like a lot, but it’s no different than when you had to complete 4–6 courses per semester in college!


How to Find Weaknesses in your Machine Learning Models

By Michael Berk, Data Scientist at Tubi

Any time you simplify data using a summary statistic, you lose information. Model accuracy is no different. When simplifying your model’s fit to a summary statistic, you lose the ability to determine where your performance is lowest/highest and why.

Figure 1: example of areas of the data where model performance is low. Image by author.

To combat this problem, researchers at IBM recently developed a method called FreaAI that identifies interpretable data slices where a given model has poor accuracy. From these slices, the engineer can then take the necessary steps to ensure the model will perform as intended.


Hypothesis Tests Explained


A quick overview of the concept of Hypothesis Testing, its classification in parametric and non-parametric tests, and when to use the most popular ones.

According to Jim Frost, Hypothesis Testing is a form of inferential statistics that allows us to draw conclusions about an entire population based on a representative sample [..] In most cases, it is simply impossible to observe the entire population to understand its properties. The only alternative is to collect a random sample and then use statistics to analyze it [1].

When performing Hypothesis Testing, firstly, a hypothesis must be formulated. An example of a hypothesis is “there is a correlation between height and gender in a population,” or “there is a difference between two groups of a population.”


Want to make money? Become a mathematician. Seriously.

Walk along any city street and you’re overwhelmed by signs proclaiming the importance of banks, greengrocers, fast food, and a thousand other professions. But it’s easy to imagine that maths is irrelevant to today’s world – supermarkets don’t sell mathematics in a can.

Actually, maths underpins our daily lives in thousands of ways. The equations of aerodynamics are vital to aircraft design. Navigation depends on trigonometry. The development of new medicines relies on statistics.

We seldom notice the maths because nearly all of it goes on behind the scenes, but an awful lot of people do need to know the maths to make these things work.


Mathematics Hidden Behind Linear Regression

Exploring statistics using Calculus


Fine-tuning? How Bayesian Statistics Could Help Break a Deadlock

In the earlier part of podcast episode 150, “Ours is a finely tuned — and No Free Lunch — universe,” Swedish mathematician Ola Hössjer and University of Miami biostatistician Daniel Andrés Díaz-Pachón discussed with Walter Bradley Center director Robert J. Marks the many ways in which the universe is finely tuned for life. Many theorists are not happy with the idea of fine-tuning because they are uncomfortable with its theistic implications. In this second portion of the episode, they discuss how a method of estimating probability called Bayesian statistics or Bayes theorem could help break a deadlock around fine-tuning:

This portion begins at 13:00 min.


Linear Regression — The Behind the Scenes Data Science ! (Part-2)

Section-3 of the image-2 (above), gives us the data related to parameter estimates or coefficients for our regression model. Let’s understand this in detail.

Please see below table 2.3 representing this part for quick reference.

table-2.3 | Output statistics of Simple Linear Regression — Parameter Estimates (dummy data) (image by author)

Our regression model equation is give by : y-pred=B0 + B1*X1 + B2*X2..

Specifically for this model, y-pred = 0.209 + 0.001 * X


Parameter estimates OR regression coefficients are the values of B1, B2 .. etc. They can be thought of as the weightage or importance of independent variables (i.e.


3 Motivation Breakers That Aspiring Data Scientists Face

2. Is it too much to learn?

Data science is an interdisciplinary field that consists of 3 main components. These components are statistics, programming, and math. Each of these components have several concepts and topics that are related to data science.

After I made some progress, I felt overwhelmed by the amount of material to cover. It was literally impossible for me to have enough time and energy to learn all of them.

Was it too much to learn? Yes, absolutely. However, I did not have to learn all of them. Nobody does.

The entire scope of data science is just extreme.


85% of data science projects fail – here’s how to avoid it

Here are a few common traps that data scientists can avoid to NOT be one of the 85% of data science projects that fail.

Sponsored Post.

85% of data science projects fail. So how do you avoid being part of that statistic? Here are a few common traps that data scientists can avoid.

1. Move beyond predictions

There’s no doubt that predictive modeling is a big upside of data science — especially during those frequent instances when we know that the result is out of our control so predicting it is all we can do. But why only limit data science to predictions?


Range of a Data Set

The range of a data set is the difference between the maximum and the minimum values. It measures variability using the same units as the data. Larger values represent greater variability.

The range is the easiest measure of dispersion to calculate and interpret in statistics, but it has some limitations. In this post, I’ll show you how to find the range mathematically and graphically, interpret it, explain its limitations, and clarify when to use it.


To find the range in statistics, take the largest value and subtract the smallest value from it.

Range = Highest value – Lowest value

It cannot be a negative value because the formula takes the larger value and subtracts the smaller value.


These 9 Insights From Helping UK Government Handle COVID-19 Will Change Your Mind About Data…

Is it statistics or ML? Wait, isn’t ML just advanced statistics? I have come across several versions of these questions in my 14 years career working with data. There are debates between high-profile experts, articles, and even peer-reviewed articles in prestigious journals on this topic. It’s crazy.

Honestly, this is a useless, (seemingly) inconclusive debate. ML is by definition concerned with learning from data. A key component of learning from data often requires transforming raw data into summary variables. A good chunk of statistics is all about summarising data. We now have an increasingly vast amount of data and require ingenious algorithmic approaches.