Regression Analysis in Data Science Courses in Bangalore

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You may already be aware, as a fan of data science, that the majority of business choices made today are informed by data. Understanding how to sort through the many sorts of big data and all the available data is crucial, though. Regression Analysis is one of the most significant types of data analysis in this area.

The predictive modeling method known as regression analysis is mostly utilized in statistics. Sir Francis Galton, Sir Charles Darwin’s cousin, is credited with the first use of the word “regression” in this sense. The least squares approach, created by Adrien-Marie Legendre and Carl Gauss, is the earliest type of regression.

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Before getting into the deeper levels of the course let’s first establish the importance of regression analysis and the meaning of R squared before diving into the what and how of regression in data science.

What is regression analysis?

“An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem,” said eminent American mathematician John Tukey. Regression analysis specifically aims to do this.

A dependent (or target) variable and an independent (or predictor) variable are the subjects of regression analysis, which is essentially a set of statistical procedures that examines the connection between them. It aids in determining how strongly the variables are correlated and has the ability to predict how the variables will interact in the future.

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Machine learning and regression analysis are both utilized for forecasting and prediction. On the other hand, it can also be used to model time series and identify causal links between different variables. Regression analysis, for instance, is the best method for determining the connection between rash driving and the number of accidents a driver causes on the road.

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Why is regression important for data science?

Regression analysis is the process of determining how two or more variables relate to one another. This statistical method is used.

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Enterprises can utilize regression analysis to better comprehend the meaning of their data points and make smarter decisions by combining them with other business analytical tools.

Regression analysis enables one to comprehend how, when one of the independent variables is altered, the typical value of the dependent variable changes while the other independent variables remain the same. Because of this, business analysts and other data professionals use this potent statistical method to eliminate irrelevant variables and concentrate on the crucial ones.

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Regression analysis has the advantage of enabling data analysis to assist organizations in making better decisions. The future weeks, months, and years of a firm can be impacted by a better grasp of the variables.

How does regression help in data science?

As its name suggests, the regression method of forecasting is utilized for both forecasting and determining the haphazard relationship between variables. Regression forecasting can be useful for someone dealing with data in the following ways from a business perspective:

Estimating future and current revenues.
Recognizing supply and demand.
Knowing the inventory levels.
Review and comprehend the effects of variables on each of these elements.

Regression analysis can be used by organizations to comprehend the following:

- Why did the number of calls to customer service decline in recent months?
- How will sales fare over the following six months?

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Which marketing promotion strategy should I pick?
Whether to grow the company or develop and sell a new product.

Finding out which independent variables have the biggest impact on a dependent variable is the main advantage of regression analysis. It also aids in deciding which elements should be stressed and which ones can be overlooked.

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When to use regression analysis?

The basic purpose of regression analysis is to explain how a group of independent variables and a set of dependent variables relate to one another. It produces a regression equation whose coefficients reflect the correlation between each independent variable and each dependent variable. Regression further also has its own types, multiple and linear based on the approach towards the data and study the individual chooses which one to operate with.

To learn regression in depth and to gain an understanding of its applications at a professional level, one should get acquainted to the courses they can take up. Bangalore’s 360DigitMG is a reputable and well-known institution that provides data science courses. The full data science life cycle is thoroughly covered in their course offerings. They were founded in 2013 and currently provide excellent courses in a variety of subjects through 7 centers worldwide.

The data science course syllabus includes,

Course Syllabus

CRISP-DM – Project Management Methodology
Exploratory Data Analytics (EDA) / Descriptive Analytics or Descriptive Statistics
Statistical Data Business Intelligence and Data Visualization
Plots & Inferential Statistics
Probability Distributions (Continuous & Discrete)
Hypothesis Testing – The ‘4’ Must Know Hypothesis Tests
Data Mining Supervised Learning – Linear Regression, OLS
Predictive Modelling – Multiple Linear Regression
Lasso and Ridge Regressions
Logistic Regression – Binary Value Prediction, MLE
Multinomial Regression and Ordinal Regression & more

The training course includes modules of regression that include,

Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for the prediction of a continuous dependent variable. Understand about ordinary least squares technique.

- Scatter diagram
- Correlation analysis
- Correlation coefficient
- Ordinary least squares
- Principles of regression
- Simple Linear Regression
- Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression
- Confidence Interval versus Prediction Interval
- Heteroscedasticity / Equal Variance
- LINE assumption
- Linearity
- Independence
- Normality
- Equal Variance / Homoscedasticity
- Collinearity (Variance Inflation Factor)
- Multiple Linear Regression