Statistics Archives - Liwaiwai

Top 10 Data Scientist Skills to Develop to Get Yourself Hired

In my previous story Data Scientist — 12 Steps From Beginner to Pro I described how to master a profession from scratch. In this article, I will focus on the key skills required to become a Data Scientist. ? Hard Skills ? 1. Mathematical base Knowledge of machine learning techniques is an integral part of the Data Scientist job. Working with machine learning algorithms requires an understanding of the basics of calculus (for example, partial differential equations ), linear algebra, statistics (including Bayesian theory), and probability theory. Knowledge of statistics helps the Data Scientist to critically assess the significance of data. The mathematical…

The 5 Best Job Choices In AI By Salary And Job Prospects

Artificial intelligence (Al) paychecks benefit from a scarce talent and high demand. It is as per the law of supply and demand, and currently, everything related to Al is in very high demand. The salaries of Al professionals are reaching the sky height and beyond it. It is also essential to note that one Japanese company, Start Today, posted the requirement of a “genius” Al professional with a salary as high as 100 million yen — that is equal to almost 1 million USD. Al is in demand because of the ambitious organizations considering their monetary rise due to its…

Data

Here Are 13 Subtle Ways Statistics Can Deceive You

No one can argue with statistics because they are hard facts. — are they really? By this time, a lot of us have done a great job realising that not everything we see on the internet is true. Some of the information we receive is deliberately manipulated in order to influence our behavior or our perceptions With this, we become highly skeptical. We look for reputable sources. We turn to the hard facts. For instance, figures and statistics, right? No. Statistics is a tool used to navigate in this world filled with uncertainty and risk. The figures it produces are…

Data

Statistics For Dummies: Indexing And Subsetting In R [Part 2 of 2] : Lists And Data Frames

Last time, we discussed how to index or subset vectors and matrices in R. Now, we will deal with indexing the other commonly used R objects: lists and data frames. Typically, we will not be dealing with data with the level of simplicity of vectors and matrices. Most of the time, more structure with the information we collect. With this, most objects that you will encounter will actually come in the form of lists and data frames. Data frames are highly used especially in the context of statistical analysis. If you are not yet familiar with R objects, you may…

Data

Statistics For Dummies: Indexing and Subsetting In R [Part 1 of 2] : Vectors And Matrices

Previously, we talked about the objects in R and how they are created. Now, we will discuss how information can be extracted from these objects. This process is also known as indexing or subsetting. The ability to access information is important since there are times that we only need a specific data point or subset of a collection of measurements for further processing or analysis. Luckily in R, doing so is fairly systematic. In this lesson, we will go over two of the object types we have discussed — vectors and matrices. If you’re not yet familiar with these objects…

Data

What Is It To Be Bayesian? The (Pretty Simple) Math Modelling Behind A Big Data Buzzword

If you’ve ever tripped up over the term ‘Bayesian’ while reading up on data or tech, fear not. Strip away the jargon and notation, and even the mathematics-averse can make sense of the simple yet revolutionary concept at the core of both machine learning and behavioural economics. As this video from the YouTube channel 3Blue1Brown skilfully explains, at its most basic, Bayes’s theorem is a tool for assessing degrees of probability based on prior conditions. And there are ways to make it altogether more intuitive than the statistical formulas might suggest. Although the theorem dates back to its 18th-century namesake,…

Data

A Short Primer On Generalized Linear Models (GLM)

Generalized Linear Models (GLM) refers to a large class of models which include the familiar ordinary linear regression — ordinary least squares (OLS) regression — and the analysis of variance (ANOVA) models. A bag loaded with tricks (models, rather) Both OLS regression and ANOVA deal with continuous response variables. However, there are times that we need to predict a categorical response variable, for example, yes/no responses and count data. For this purpose, other models like logit, log-linear, and probit model, just to name some, will be appropriate. Yes, there’s a lot of models in the GLM domain. It is easy…

Data

Statistics For Dummies: Type I And Type II Errors

You often hear Type I and Type II errors in statistics classes. There is good reason for that — minimizing either of these two errors is pretty much the core of statistical theory. Preliminaries Type I and Type II errors are related to the concept of hypothesis testing. In hypothesis testing, we have two hypotheses: 01. Null hypothesis – the hypothesis of “no effect,” the status quo. example: Income and gender are independent of one another. This is a conjecture of no effect since it states that income and gender has nothing to do with each other. 02. Alternative hypothesis …

Data

The Weird Ones: How To Handle Outliers In Your Data

Outliers in data are the weird ones in a set. Their values are way off the rest of the values of the sample. They can really ruin your analysis, especially if you are using methods which are sensitive to the presence of outliers. Given this, a lot are inclined to remove these observations. While this may make things convenient, this approach may end up yield false claims. How exactly do we deal with these troublemakers? Understanding outliers For starters, we have to identify why these values occur at the first place. Some candidates for inclusion are cases where the outliers…

How To: Perform Tests for Normality in R

Normality is one assumption that you will typically encounter in statistical methods that you will employ. A lot of the tests that were created have an underlying assumption that your data is normal. A large number of parametric tests assume normality of data. Ordinary least squares regression assumes it for its error terms, too. You can diagnose your data for normality in the statistical programming language R. The companion software, RStudio, will make everything easier for you. The good news is, you can do it for free. If you are an Ubuntu user, here’s how to install R. Requirements To perform…

Input your search keywords and press Enter.