The Top 10 Statistical Techniques That Data Scientists Must Know

Regardless of how you feel about Data Science sexiness, it is impossible to dispute the importance of data and our ability to analyse, organise, and interpret it. Based on their massive libraries of employment data and employee feedback, Glassdoor ranked Data Scientist first among the 25 Best Jobs in America. So the profession is here to stay, but the specifics of what a Data Scientist does will inevitably change. Data Scientists are riding the crest of an unbelievable wave of creativity and technical development, as technologies like as Machine Learning become more widespread and newer fields such as Deep Learning acquire considerable popularity among researchers and engineers — and the firms that hire them.
While significant coding abilities are essential, data science is not solely concerned with software engineering (in fact, a solid understanding of Python is all you need). Data scientists operate at the intersection of programming, statistics, and critical thinking. According to Josh Wills, a data scientist is “someone who is better at statistics than any programmer and better at programming than any statistician.” I personally know far too many software engineers wanting to make the transition to data scientist who are mechanically applying machine learning frameworks like TensorFlow or Apache Spark to their data without a thorough understanding of the statistical theories that underpin them. As a result, statistical learning research emerges, as does a theoretical framework for machine learning based on statistics and functional analysis. Information Transformation Service offers   web scraping Services to improve business outcomes and facilitate intelligent decision making. Their web scraping service allows you to scrape data from any website and transfer web pages into an easy-to-use format such as Excel, CSV, JSON, and many more.

Why should you pursue a degree in Statistical Learning? Understanding the concepts underlying the various techniques is crucial in order to know when and how to employ them. To understand the more complex techniques, one must first master the basics. It is necessary to properly evaluate a method's performance in order to determine how well or poorly it works. Furthermore, this is an appealing study field with important applications in science, business, and finance. Finally, statistical learning is a crucial component of a modern data scientist's training. The following are examples of statistical learning difficulties:

Determine the risk factors for prostate cancer.

Classify a recorded phoneme using a log-periodogram.

Based on demographic, nutritional, and clinical data, predict whether someone will have a heart attack.

Create your own spam detection system for email.

Recognize the letters and numbers in a zip code written by hand.

Sort a sample of tissue into one of various cancer classifications.

Determine the relationship between wage and demographic factors in population survey data.

I conducted an Independent Study in Data Mining during my final semester of college. The course draws heavily on three books: Introduction to Statistical Learning (Hastie, Tibshirani, Witten, James), Doing Bayesian Data Analysis (Kruschke), and Time Series Analysis and Applications (Hastie, Tibshirani, Witten, James) (Shumway, Stoffer). Bayesian Analysis, Markov Chain Monte Carlo, Hierarchical Modeling, Supervised and Unsupervised Learning, and other topics were covered. This occurrence piques my interest in the academic discipline of Data Mining and convinces me to further specialise in it. I recently completed the Stanford Lagunita Statistical Learning online course, which covers all of the subjects covered in the Intro to Statistical Learning book that I read for my Independent Study. Now that I've gone through the material twice, I'd like to share the ten statistical approaches from the book that I believe each data scientist should be familiar with in order to be more productive when dealing with enormous datasets.

Before I get started on these ten tactics, I'd like to make a distinction between statistical learning and machine learning. I previously wrote one of the most popular Medium blogs on machine learning, therefore I feel confident in my ability to justify these differences:

Machine learning emerged as a branch of Artificial Intelligence.

Statistical learning emerged as a branch of statistics.

Machine learning is primarily concerned with large-scale applications and forecast accuracy.

Models and their interpretability, as well as precision and uncertainty, are valued highly in statistical learning.

The line, however, is getting increasingly blurred, and there is a significant amount of "cross-fertilization."

Machine learning has the upper hand in marketing!

The first sort of regression analysis is linear regression.

Linear regression is a statistical technique for predicting a target variable by fitting the best linear relationship between the dependent and independent variables. To get the best fit, make sure that the total distance between the shape and the actual observations at each location is as small as possible. The fit of the shape is "best" in the sense that no other location would generate less inaccuracy given the shape's selection. The two major types of linear regression are simple linear regression and multiple linear regression. A dependent variable is predicted using Simple Linear Regression by fitting the best linear relationship to a single independent variable. Multiple Linear Regression predicts a dependent variable by fitting the best linear connection to more than one independent variable.

Choose any two objects that you use frequently that are linked. For example, I have statistics for the last three years on my monthly spending, monthly income, and monthly travel. Now I have to answer the following questions:

What are my projected monthly expenses for the future year?

Which factor is more important in deciding my monthly expenses (monthly income or number of trips per month)?

What is the connection between monthly income, monthly travel, and monthly spending?

2 — Classification:

Classification is a data mining technique that categorises data in order to make more accurate predictions and analyses. Classification, often known as a Decision Tree, is one of many methods for properly analysing very large datasets. Logistic Regression and Discriminant Analysis are two prominent Classification techniques.

When the dependent variable is dichotomous, logistic regression is the appropriate regression methodology (binary). Like all regression studies, logistic regression is a predictive analysis. Logistic regression is a data analysis technique used to define and explain the relationship between one dependent binary variable and one or more independent nominal, ordinal, interval, or ratio-level variables. A logistic regression analysis can look at the following categories of questions:

How does each additional pound of obesity and each pack of cigarettes smoked per day affect the probability of acquiring lung cancer (Yes vs. No)?

Is it true that body weight, calorie intake, fat consumption, and participant age all have an impact on heart attacks (yes/no)?

Discriminant Analysis is based on the assumption that two or more groups, clusters, or populations are known a priori, and that one or more new observations are classified into one of the known populations based on the measured attributes. Discriminant analysis models the distribution of the predictors X in each of the response classes individually, and then uses Bayes' theorem to turn these estimates into estimates for the likelihood of the response category given the value of X. These models could be linear or quadratic in nature.

Linear Discriminant Analysis determines which response variable class each observation belongs to by computing "discriminant scores" for it. These scores are computed by recognising linear combinations of the independent variables. It is assumed that the observations in each class are drawn from a multivariate Gaussian distribution and that the covariance of the predictor variables is shared across all k levels of the response variable Y.

Quadratic Discriminant Analysis takes a distinct technique. QDA, like LDA, assumes that the observations from each class of Y are Gaussian. QDA, unlike LDA, assumes that each class has its own covariance matrix. To put it another way, the predictor variables are not assumed to have the same variance over all k levels of Y.

3 — Resampling Methods:

Resampling is a procedure in which numerous samples are taken from the original data samples. It is a method of non-parametric statistical inference. In other words, the resampling method does not rely on generic distribution tables to derive approximations of p probability values.