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The shortage of data scientists is a hindrance to the widespread adoption of analytics across many industries.
At the same time, the preponderance of multiple tools, techniques and knowledge base along with the rapidly changing data science landscape make it difficult for analytics leaders to hire the right talent for their data science team. The attrition rate in this industry is also high due to which employers end up spending a lot of time and money to hire a data…Continue
Python, R and SAS are the three most popular languages in data science. If you are new to the world of data science and aren’t experienced in either of these languages, it makes sense to be unsure of whether to learn R, SAS or Python.
Don’t fret, by the time you’re done reading this article, you will know without a doubt which language is the right one for you.
This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I
In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct answer as mentioned in the …Continue
Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we’ll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees and Support Vector Machines [SVM].
All of the above listed algorithms are used in classification [ SVM and Decision Trees are also used for regression, but we are not discussing that today!]. Time and again I have seen people asking which…Continue