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Logistic Regression Courses

Learn how to predict outcomes using data with upGrad’s Logistic Regression courses. Simple, practical, and beginner-friendly - ideal for anyone looking to step into the world of data science and analytics.

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Overview of Learning Logistic Regression Courses with upGrad

Among the many essential topics covered in data science, logistic regression is one of the important topics that stand out as a core technique for classification problems. It is used for solving classification problems, predicting the probability of a categorical outcome that can often be yes/no, true/false, or 0/1, based on input variables.

Why Learning Logistic Regression is Crucial in 2025

  • Core Component of Data Science:  Logistic regression is a foundational topic included in data science, used for binary classification problems like fraud detection, customer churn, and lead scoring.
  • Essential for Real-World Data Analysis: Logistic regression helps you make informed, data-driven decisions, making it a must-have skill in any data science online course or offline program.
  • Boosts Your Career Versatility: Learning logistic regression strengthens your ability to work across industries such as healthcare, finance, marketing, and tech.
  • High ROI Regardless of Course Fees: Learning logistic regression provides high returns, even if the cost of training programs is low, as it provides access to high-paying jobs.
  • Hands-On Learning Opportunities: A dedicated logistic regression course offers practical experience with tools like Python, R, and real-world datasets, preparing you for job-ready skills.

upGrad provides popular logistic regression courses for aspiring analysts and ML enthusiasts.upGrad has various courses in this field, depending on the level of knowledge and skills you want to gain.

Who Should Take Logistic Regression Online Courses

  • Beginners in Data Science:  Logistic regression will be one of the first techniques you learn if you are a beginner taking a course on data science.
  • Professionals seeking to upskill in Marketing: Analysts, business intelligence professionals, and finance experts can strengthen their analytics skills by taking a data science online course on learning logistic regression.
  • STEM Students:  Computer science, engineering, mathematics, and statistics students will see logistic regression as a common topic in most of the data science course syllabi.
  • ML & AI Enthusiasts:  For anyone who wants to start a career in machine learning or AI. Logistic regression can be a good foundation, as it is important to understand how algorithms classify and predict things.

What Will You Learn in upGrad’s Logistic Regression Online Courses?


upGrad offers a broad variety of online courses, aiming to provide the skills that are highly demanded in the industry, such as logistic regression which is one of the fundamental methods in predictive analytics.

upGrad offers to train you practically with logistic regression during the course itself. Among the highly rated programs that include courses on logistic regression are:

Topics You’ll Learn in Logistic Regression Courses from upGrad:

upGrad courses cover the following key subjects to help you become proficient in logistic regression:

  • Learning the Logistic Regression Model: Get to know how the logistic regression probability formula operates to anticipate binary results and categorize data accurately.
  • Types of Logistic Regression: Discuss variations like binary, multinomial, and ordinal logistic regression to accommodate varying types of data.
  • Logistic Regression Algorithm Steps: Learn step by step how the algorithm is optimized, trained, and validated in actual datasets.
  • Assumptions of Logistic Regression: Study the essential logistic regression model assumptions to be satisfied for legitimate findings and how they can be checked in real-life situations.
  • Benefits of Logistic Regression: Learn how logistic regression continues to be a go-to option for classification issues, particularly where interpretability and performance are crucial.
  • Model Evaluation & Optimization: Learn logistic regression requirements such as multicollinearity checking, choosing the appropriate threshold, and assessing metrics like ROC-AUC, precision, and recall

Data Science Course Instructors

Learn From The Best

Learn Data Science fundamentals from expert instructors who help you excel in your careers by imparting cutting-edge analytics and machine learning skills.

8

Instructors

10

Industry Experts

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Debabrata Das

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Director, IIITB

Dr. Debabrata Das is Director of IIITB. He has received his PhD from IIT-KGP. His main areas of research are IoT and Wireless Access Network 

IIITB Profile
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Chandrashekar Ramanathan

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Dean Academics

Prof. Chandrashekar has a PhD from Mississippi State University and experience of over 10 years in several multinational organisations. 

IIITB Profile
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Prof. Paulo Lisboa

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Head of Dept - Applied Mathematics

Studied mathematical physics at LU and was the chairman of Industrial Mathematics at KMJU in 1996 and Head of Graduate School in 2002 

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Tricha Anjali

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Ex-Associate Dean

Prof. Anjali has a PhD from Georgia Institute of Technology as well as an integrated MTech (EE) from IIT Bombay. 

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Dr. Atif Waraich

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Faculty - Computer Science

A Senior faculty of Engineering and Technology at LJMU who has multiple publications in the health care domain 

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Prof. G. Srinivasaraghavan

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Professor

Prof. Srinivasaraghavan has a PhD in Computer Science from IIT-K and 18 years of experience with Infosys and several other MNCs. 

IIITB Profile
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Dr. Gabriela Czanner

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Faculty - Engineering and Technology

A Senior lecturer at Department of Applied Mathematics at LJMU. Her research focus is advanced statistics for decision support 

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Prof. Dhiya Al-Jumeily

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Professor - Artificial Intelligence

A senior member of the IEEE and a chartered IT Professional, he is a fellow of the UK Higher Education ACademy 

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Data Science Projects

Learn by Doing

Our data science online certification programs have projects to apply theoretical knowledge in practical scenarios, helps you to tackle real-world problems.

16+

Industry projects to choose from

Slide 1 of 17

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers and use EDA to analyse the patterns present in the data. This will ensure that the applicants are capable of repaying the loan are not rejected.

Data Visualisation

Data Analysis

Data Interpretation

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Credit EDA Assignment

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers and use EDA to… Know More

Skills learned

Data Cleaning

Data Visualisation

Data Analysis

Data Interpretation

MySQL Queries

Data Manipulation

Data Analysis

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RSVP Case Study

In this assignment, you will work on a movies dataset using SQL to extract exciting insights about popular films and the factors that drive a film. 

Skills learned

MySQL

MySQL Queries

Data Manipulation

Data Analysis

ML Modelling

Model Evaluation

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Bike Sharing Assignment

Build a regression model to understand the factors on which the demand for bike sharing systems vary on and help a company optimise its revenue. 

Skills learned

Linear Regression

ML Modelling

Model Evaluation

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavioral data of the leads (potential buyers), it will be able to identify the ones most likely to convert.

Decision Trees

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Lead Scoring Case Study

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavioral data of t… Know More

Skills learned

Logistic Regression

Decision Trees

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a business analyst.

Tree Models

Model Selection

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Model Selection Case Study - Telecom Churn

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customer… Know More

Skills learned

Logistic Regression

Tree Models

Model Selection

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a data scientist.

Tree Models

Boosting

Model Selection

Regularization

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Advanced ML Case Study - Telecom Churn

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customer… Know More

Skills learned

Logistic Regression

Tree Models

Boosting

Model Selection

Regularization

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Lexical Processing

Regex

POS Tagging

Dependency Parsing

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Syntactic Processing Assignment

Use the techniques such as POS tagging and Dependency parsing to extract information from unstructured text data. 

Skills learned

Natural Language Processing

Lexical Processing

Regex

POS Tagging

Dependency Parsing

Sqoop

Redshift

Spark

ETL Pipeline

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ETL Project

Make use of Sqoop, Redshift & Spark to design an ETL data pipeline. 

Skills learned

AWS

Sqoop

Redshift

Spark

ETL Pipeline

Analyse movie data from the past 100 years and find out various insights to determine what makes a movie do well. Use data manipulation, slicing, and various other dataframe operations to successfully find usable insights from the movies.

Data Processing

Data Visualisation

Data Analysis

Dashboarding

Data Storytelling

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IPL Visualization Assignment

Analyse movie data from the past 100 years and find out various insights to determine what makes a movie do well. Use data manipulation, slicing, and … Know More

Skills learned

Tableau/Power BI

Data Processing

Data Visualisation

Data Analysis

Dashboarding

Data Storytelling

Linear Regression

ML Model Evaluation

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Advanced Regression

Build a regularized regression model to understand the most important variables to predict 

Skills learned

ML Modeling

Linear Regression

ML Model Evaluation

Hadoop

MapReduce Programming

mrjob

Linux

Sqoop

Apache HBase

SQL

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MapReduce Programming Assignment

Perform MapReduce Programming in a big data environment on a large dataset. 

Skills learned

AWS

Hadoop

MapReduce Programming

mrjob

Linux

Sqoop

Apache HBase

SQL

Big Data with Spark

PySpark

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Big Data Case Study

Understand how a big data project in the industry is taken up and solved through a comprehensive big data case study on cloud. 

Skills learned

AWS

Big Data with Spark

PySpark

Python

Data Analysis

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SQL & Python Lab

Solve intensive programming questions in a competitive lab environment to showcase and evaluate your SQL and Python coding skills. 

Skills learned

SQL

Python

Data Analysis

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI tools.

Data Storytelling techniques

Data Visualisation

Tableau

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Airbnb Case Study

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI to… Know More

Skills learned

Data Analysis

Data Storytelling techniques

Data Visualisation

Tableau

Spark

Spark Streaming

Apache Kafka

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Retail Project

Build an end-to-end real-time data processing application using Spark Streaming and Kafka. 

Skills learned

AWS

Spark

Spark Streaming

Apache Kafka

Domain understanding

Data Analysis

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Business Case Study

Understand how data projects are taken up in the industry and implement the steps required to successfully manage and complete a data project. 

Skills learned

Business Problem Solving

Domain understanding

Data Analysis

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, loss computation, backpropagation, weight updates etc. You will use the MNIST dataset to train your model to classify handwritten digits between 0-9.

Neural Networks

PyTorch

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NN Assignment

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, … Know More

Skills learned

Deep Learning

Neural Networks

PyTorch

Career Paths After Completing a Logistic Regression Course

After gaining top skills in a Logistic Regression Course, you’ll be equipped to explore high-demand roles across the data ecosystem. Because logistic regression is a foundation for many data science courses, learning it will allow one to pursue several lucrative career paths.

Here are some popular career paths you can apply for:

  • Logistic Manager: ₹5 to ₹14 LPA
  • Logistic Coordinator: ₹2 to ₹4 LPA
  • Data Analyst – ₹4 to ₹9 LPA
  • Business Analyst – ₹6 to ₹13 LPA
  • Data Scientist – ₹7 to ₹17 LPA
  • Data Engineer – ₹6 to ₹14 LPA
  • Product Analyst – ₹8 to ₹16 LPA
  • Analytics Consultant – ₹11 to ₹18 LPA
  • Statistician: ₹3 to ₹8 LPA
  • Quantitative Analyst (Quant): ₹12.5 - 24 LPA

Source: Glassdoor

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Product Analyst, Head Infotech

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My online data science course's content was so well-structured that it helped me grasp concepts that were entirely new to me. Thanks to upGrad's placement drive, I was able to secure a job at Kantar Analytics.

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Being online, it was like a university experience

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Why Choose upGrad for Logistic Regression Online Courses?

upGrad provides various courses in the domain of data science, and such courses include logistic regression courses too. Here’s a comparison table showing why upGrad stands out for logistic regression courses when compared to other institutes.

Feature/Criteria

upGrad

Other Institutes

Course Structure

In-depth logistic regression course embedded in a complete data science course syllabus

Mostly theoretical with minimal focus on real-world applications

University Collaborations

Tie-ups with IIIT Bangalore, LJMU,O.P.J. and other reputed institutes

Limited collaborations, often with local or unranked universities

Certifications

Industry-recognized certifications are valid globally in data science and analytics

Basic certificates with less value in professional settings

Industry Projects

Real-world capstone and mini-projects applying logistic regression models

Fewer or classroom-only projects

Career Support

1:1 mentorship, mock interviews, and resume assistance

Little or no structured career guidance

Tools & Languages

Hands-on training using Python, R, SQL—relevant for logistic regression modeling

Limited tool usage, often Excel-focused

Faculty Expertise

Taught by experienced industry professionals and academic leaders in data science

Mostly academic faculty, less industry exposure

Learning Format

Fully online logistic regression course with flexible learning options

Rigid schedule, less suitable for working professionals

Specialization

Covers logistic regression, classification metrics, ROC curves, and model tuning techniques

Focused only on theory without specialization in logistic regression

Best For

Beginners, tech professionals, and job switchers who want to learn data science

Suitable mainly for academic or theory-focused learners

FAQs about Logistic Regression

1. What are the various forms of logistic regression?

There are three types of logistic regression:

  • Binary Logistic Regression (when the outcome consists of two categories, such as Yes/No)
  • Multinomial Logistic Regression (for outcomes consisting of more than two unordered categories)
  • Ordinal Logistic Regression (for outcomes consisting of ordered categories).

These are well-taught in any good online course on logistic regression.

2. What is the logistic regression probability formula?

The logistic regression probability formula is:

P(Y=1) = 1 / (1 + e^-(β0 + β1X1 + β2X2 +. + βnXn))

This formula computes the probability of the target variable being 1 (true) from the given features and coefficients.

3.  What is the purpose of a logistic regression model?

A logistic regression model applies to classification problems with categorical output. It estimates the probability of a class label (e.g., spam or not spam, disease or no disease) and is a staple in any data science course syllabus.

4. What are the main assumptions of logistic regression?

The primary assumptions of logistic regression are:

  • The outcome has to be binary (in the case of binary logistic regression)
  • No multicollinearity between independent variables
  • A straight line relationship between the logit and the independent variables
  • Large sample size

These logistic regression model assumptions are essential for reliable predictions.

5. Why are assumptions necessary in logistic regression?

Adhering to each logistic regression assumption guarantees the model remains interpretable, stable, and statistically valid. Breaking any of them can lead to deceptive results and poor performance.

6. What are the data requirements for logistic regression?

Important logistic regression data requirements are:

  • Clean, labeled categorical target data
  • Interval or binary independent variables
  • No outliers

These are typically covered in an organized online logistic regression course.

7. What are the advantages of logistic regression?

Some major advantages of logistic regression include:

  • It’s easy to implement and interpret
  • Requires less computational power
  • Performs well when the data satisfies assumptions
  • Useful for binary classification tasks


8. What are the basic logistic regression algorithm steps?

The core logistic regression algorithm steps are:

  1. Collect and prepare the data
  2. Choose a logistic function
  3. Estimate model parameters using maximum likelihood
  4. Validate assumptions
  5. Evaluate model performance using accuracy, AUC-ROC, etc.


9. Who should take a logistic regression online course?

Students, working professionals, and aspiring data scientists looking to learn the logistic regression model, explore real-time projects, and understand the advantages of logistic regression should enroll in such courses.

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  1. The above statistics depend on various factors and individual results may vary. Past performance is no guarantee of future results.

  2. The student assumes full responsibility for all expenses associated with visas, travel, & related costs. upGrad does not .