Linear Regression and Logistic Regression using R Studio

Post published:1 October, 2025
Post category:StudyBullet-22
Reading time:3 mins read

Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & Classification
⏱️ Length: 6.2 total hours
⭐ 4.61/5 rating
👥 61,420 students
🔄 September 2025 update

Add-On Information:

Get Instant Notification of New Courses on our Telegram channel.

Note➛ Make sure your 𝐔𝐝𝐞𝐦𝐲 cart has only this course you're going to enroll it now, Remove all other courses from the 𝐔𝐝𝐞𝐦𝐲 cart before Enrolling!

Course Overview
This course provides a focused, hands-on introduction to Linear and Logistic Regression, foundational predictive models, entirely within the R Studio environment.
Designed for beginners, it clarifies the critical distinction between regression (continuous) and classification (categorical) problems for effective model application.
You will develop practical skills in using R for essential data analysis, statistical modeling, and interpretative insights, moving from theory to immediate application.
Leverage an updated curriculum (September 2025) and a high-rated structure, guiding over 61,000 students through core data science concepts efficiently.
Requirements / Prerequisites
Basic computer literacy and familiarity with installing software are the primary technical prerequisites.
A fundamental grasp of high-school level math and basic statistics, though core concepts are revisited, will be beneficial.
No prior R programming experience is needed; this course teaches R from the ground up for data science tasks.
An eagerness to learn and apply data-driven problem-solving techniques is the most crucial non-technical requirement.
Skills Covered / Tools Used
Mastering the efficient workflow and debugging capabilities within the R Studio Integrated Development Environment (IDE).
Gaining practical proficiency in core R programming syntax for data manipulation and analytical tasks.
Techniques for effective data loading, cleaning, and preliminary data preparation for modeling in R.
Methods for feature engineering to create impactful variables and enhance predictive model performance.
Applying statistical inference, including hypothesis testing and p-value interpretation, to validate model assumptions and predictor significance.
Constructing and interpreting Linear Regression models (`lm()`) for predicting continuous outcomes in R.
Building and evaluating Logistic Regression models (`glm()`) for binary classification challenges in R.
Utilizing diverse model evaluation metrics (e.g., R-squared, AUC, Confusion Matrix) to rigorously assess model effectiveness.
Implementing cross-validation strategies to ensure model robustness and generalizability to new, unseen data.
Generating insightful data visualizations using R’s powerful libraries to effectively communicate analytical findings.
Translating complex statistical outputs into clear, actionable business intelligence and strategic recommendations.
Benefits / Outcomes
Confidently build, evaluate, and interpret foundational predictive models (linear and logistic) using R on real-world data.
Develop a structured approach to data analysis and problem-solving, from raw data to actionable insights.
Establish a robust analytical foundation, preparing you for more advanced machine learning algorithms and complex data science roles.
Create practical projects for a data science portfolio, showcasing your R Studio and modeling skills to prospective employers.
Empower yourself to make data-driven predictions and strategic decisions across various industries and business functions.
Enhance your ability to clearly communicate complex statistical results to both technical and non-technical audiences effectively.
PROS
Specifically designed for absolute beginners, offering a clear and non-intimidating entry into predictive analytics.
Provides hands-on, practical experience with R Studio, ensuring immediate skill application and development.
Covers universally applicable and in-demand models essential for any data-driven career path.
Benefits from a large, active student community and content consistently updated for relevance (Sept 2025).
Offers a time-efficient learning path (6.2 hours) to acquire core data science modeling competencies rapidly.
Delivers high-value, professional skills at an accessible price point, democratizing data science education.
CONS
True mastery requires consistent self-practice and exploration of additional datasets beyond the course curriculum.