What programming language should one learn to get a machine learning or data science job? That’s the silver bullet question. It is debated in many forums. I could provide here my own answer to it and explain why, but I’d rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let’s look at some data. I will use the trend search available on indeed.com. It looks for occurrences over time of selected terms in job offers. It gives an indication of what skills employers are seeking. Note however that it is not a poll on which skills are effectively in use. It is rather an advanced indicator of how skill popularity evolve (more formally, it is probably close to the first order derivative of popularity as the latter is the difference of hiring skills plus retraining skills minus retiring and leaving skills).
According to industry estimates, only 21% of the available data is present in structured form. Data is being generated as we speak, as we tweet, as we send messages on Whatsapp and in various other activities. Majority of this data exists in the textual form, which is highly unstructured in nature. Few notorious examples include – tweets / posts on social media, user to user chat conversations, news, blogs and articles, product or services reviews and patient records in the healthcare sector. A few more recent ones includes chatbots and other voice driven bots. Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system. In order to produce significant and actionable insights from text data, it is important to get acquainted with the techniques and principles of Natural Language Processing (NLP). So, if you plan to create chatbots this year, or you want to use the power of unstructured text, this guide is the right starting point. This guide unearths the concepts of natural language processing, its techniques and implementation. The aim of the article is to teach the concepts of natural language processing and apply it on real data set.
We’re approaching the end of this series on empirical Bayesian methods, and have touched on many statistical approaches for analyzing binomial (success / total) data, all with the goal of estimating the “true” batting average of each player. There’s one question we haven’t answered, though: do these methods actually work?
I have a function that takes a list and does some stuff to it and then returns it. I then take that output and run it through the same function again. But I obviously don’t want to repeatedly type the function out, because I want the number of function replications to be a declared argument. I had little luck with functionals, although they seemed like an obvious choice.’
The assumptions of simple linear regression include the assumption that the errors are independent with constant variance. Fitting a simple regression when the errors are auto-correlated requires techniques from the field of time series. If you are interested in fitting a model to an evenly spaced series where the terms are auto-correlated, I have given below an example of fitting such a model.
The R programming language has a multitude of packages that can be used to display various types of graph. For a new user looking to display data in a meaningful way graphing functions can look very intimidating. When using a statistics package such as SPSS, Stata, Minitab or even some of the R Gui’s such R Commander sophisticated graphs can be produced but with a limited range of options. When using the R command line to produce graphics output the user has virtually 100 percent control over every aspect of the graphics output. For new R users there are some basic commands that can be used that are easy to understand and offer a large degree of control over customisation of the graphical output. In part one of this tutorial I will discuss some R scripts that can be used to show typical output from a basic correlation and regression analysis. For the first example I will use one of the datasets from the R MASS dataset package. The dataset is ‘UScrime´ which contains data on certain factors and their relationship to violent crime. In the first example I will produce a simple scatter plot using the variables ‘GDP’ as the independent variable and ´crimerate´ the dependent variable which is represented by the letter ‘y’ in the dataset.