![]() ![]() In statistics, linear regression is a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable. Machine learning has the upper hand in Marketing!.But the distinction has become and more blurred, and there is a great deal of “cross-fertilization.”.Statistical learning emphasizes models and their interpretability, and precision and uncertainty.Machine learning has a greater emphasis on large scale applications and prediction accuracy.Statistical learning arose as a subfield of Statistics.Machine learning arose as a subfield of Artificial Intelligence.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: Now being exposed to the content twice, I want to share the 10 statistical techniques from the book that I believe any data scientists should learn to be more effective in handling big datasets.īefore moving on with these 10 techniques, I want to differentiate between statistical learning and machine learning. 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. This experience deepens my interest in the Data Mining academic field and convinces me to specialize further in it. We did a lot of exercises on Bayesian Analysis, Markov Chain Monte Carlo, Hierarchical Modeling, Supervised and Unsupervised Learning. The class covers expansive materials coming from 3 books: Intro to Statistical Learning (Hastie, Tibshirani, Witten, James), Doing Bayesian Data Analysis(Kruschke), and Time Series Analysis and Applications (Shumway, Stoffer). In my last semester in college, I did an Independent Study on Data Mining. Establish the relationship between salary and demographic variables in population survey data.Classify a tissue sample into one of several cancer classes.Identify the numbers in a handwritten zip code.Customize an email spam detection system.Predict whether someone will have a heart attack on the basis of demographic, diet and clinical measurements.Classify a recorded phoneme based on a log-periodogram.Identify the risk factors for prostate cancer.Examples of Statistical Learning problems include: Ultimately, statistical learning is a fundamental ingredient in the training of a modern data scientist. Additionally, this is an exciting research area, having important applications in science, industry, and finance. It is important to accurately assess the performance of a method, to know how well or how badly it is working. One has to understand the simpler methods first, in order to grasp the more sophisticated ones. Why study Statistical Learning? It is important to understand the ideas behind the various techniques, in order to know how and when to use them. So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis. As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them. Data scientists live at the intersection of coding, statistics, and critical thinking. While having a strong coding ability is important, data science isn’t all about software engineering (in fact, have a good familiarity with Python and you’re good to go). With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers - and the companies that hire them - Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. 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. ![]()
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