*Recently, I completed the Statistical Learning online course on Stanford Lagunita, which covers all the material in the Intro to Statistical Learning book I read in my Independent Study.*

*Recently, I completed the Statistical Learning online course on Stanford Lagunita, which covers all the material in the Intro to Statistical Learning book I read in my Independent Study.*

Now I need to answer the following questions: Classification is a data mining technique that assigns categories to a collection of data in order to aid in more accurate predictions and analysis.

Also sometimes called a Decision Tree, classification is one of several methods intended to make the analysis of very large datasets effective. Logistic Regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).

Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it.

Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list.

Before moving on with these 10 techniques, I want to differentiate between statistical learning and machine learning.

I wrote one of the most popular Medium posts on machine learning before, so I am confident I have the expertise to justify these differences: is done by making sure that the sum of all the distances between the shape and the actual observations at each point is as small as possible.

As Josh Wills put it, I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as Tensor Flow or Apache Spark to their data without a thorough understanding of statistical theories behind them.

So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis. It is important to understand the ideas behind the various techniques, in order to know how and when to use them.

Types of questions that a logistic regression can examine: In Discriminant Analysis, 2 or more groups or clusters or populations are known a priori and 1 or more new observations are classified into 1 of the known populations based on the measured characteristics.

Discriminant analysis models the distribution of the predictors X separately in each of the response classes, and then uses Bayes’ theorem to flip these around into estimates for the probability of the response category given the value of X.

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