Healthcare Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and Predictive Analysis,  Supervised and Unsupervised Machine Learning, ML Model Development, Hyperparameter tuning,  Evaluation and Selection,  Ensemble Learning, Explainable AI methods in the Healthcare industry.

  • icons final-02 33 Courses
  • icons final-03 10 Projects & Case Studies
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    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended

  • Asset 1
    Duration: Approximately 3 months

    Suggested learning pace is 5hr/week

Course Overview

  • Learn various Data Management and Data Transformation techniques to fetch data from different tables and convert them to useable format and  basics of Data Visualization,  Univariate & Multivariate data analysis to gain insights on Chronic Kidney patients, COVID 19 patients, medical symptoms, Pharmaceutical sales etc.
  • Understanding of the concepts and various techniques of Supervised Machine Learning, Unsupervised Machine Learning and Ensemble Learning focussing on building an ML model, Hyperparameter optimization, Model Evaluation and Selection to fulfil various objectives like survival probability of cancer patients, prediction of Heart disease, forecasting of drug sales etc.
  • Develop a thorough understanding of Text analytics,  and its application in analyzing drug usage, sentiment analysis of customer review of medicine and forecast of medicine sales etc.
  • Learn how to apply Explainable AI methods like LIME and SHAP to deeply understand the model predictions and perform efficient model selection for further use in real-world problems in the Healthcare industry.

What’s included


Shareable Certificate

Earn a sharable certificate upon completion


Lifetime Access

Access this case study for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this learning track on a desktop

Skills You Will Learn

Data Pre-processing

Data Transformation

Data Visualization

Feature Engineering & Reduction

Descriptive Analysis

Model Building

Model Evaluation

Model Selection

Predictive Analytics

Explainable AI



Natural Language Processing


  • Fundamentals of Data Analytics
  • Fundamentals of Data Preprocessing
  • Data Mining Concepts and Techniques
  • Basic Data Visualization Methods- I
  • Advanced Feature Engineering techniques
  • Machine Learning – Linear Regression
  • Model Evaluation Techniques – Regression Models
  • Model Selection Techniques
  • Machine Learning – Logistic Regression
  • Model Evaluation Techniques – Classification Models
  • Basics of Hyperparameter Tuning – Linear & Logistic Regression
  • Getting Started with Naive Bayes Classifier
  • Support Vector Machines in ML
  • Hyperparameter Tuning in SVM
  • Understanding Decision Trees
  • Hyperparameter Tuning in Tree-Based Models
  • Bagging & Random Forest in Machine learning
  • Introduction to Gradient Boosting Classification
  • Introduction to Extreme Gradient Boosting Classifier
  • Introduction to AdaBoost Classifier
  • Concepts and Application of Objective and Subjective Segmentation
  • Understanding Principal Component Analysis (PCA)Fundamentals of Time Series Analysis
  • Introduction to Natural Language Processing (NLP)
  • Mining Text Data Cleansing, Treatment, Structural Representation & Visualization
  • Text Analytics – Classification and Clustering
  • Sentiment Analysis – Using Unstructured Text Data
  • Introduction to Explainable AI (XAI) using LIME
  • Introduction to Explainable AI (XAI) using SHAP
  • Introduction to Explainable AI (XAI) for Text using LIME & SHAP

  • Analyzing the impact of COVID 19 on student behavior using Hypothesis Testing
  • COVID-19 Data Exploration & Visualization
  • Feature Engineering on Chronic Kidney disease dataset
  • Analyzing body composition to predict body fat percentage by using Supervised learning methods
  • Predicting Heart Disease with Logistic Regression
  • Using Naive Bayes Classifier to predict Water Potability
  • Detection of Breast Cancer in A Clinical Trial – Application Of SVM
  • Recognizing human activity – An application of supervised machine learning
  • Predicting the Survival of patients with Hepatocellular carcinoma (HCC)Predicting Carbon Dioxide Emission by Cars Using Boosting Techniques
  • Application of Advanced supervised ML techniques for fetal health classification
  • Forecasting Pharma Product Sales
  • Analyzing Drugs usage Reviews using NLP Techniques

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.

Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.

Learner Outcomes

Complete courses to earn shareable certificates and badges. These awarded items will look great in your portfolio as you showcase your skills and project experience to employers and colleagues.

  • Application of Descriptive Analysis and Visualization to analyze problems related to COVID 19 and Chronic Kidney Disease.
  • Application of Unsupervised Lerning techniques like Clustering algorithms to group patients having similar medical symptoms.
  • Application of various Supervised ML techniques to solve problems related to Body Fat Percentage Prediction, fetal health prediction, Breast Cancer Detection, and HCC Patients Survival prediction.
  • Application of Time Series & Text Analytics to solve problems related to Pharma Sales Forecast and Sentiment Analysis to Understand Customers Opinion of medical drugs and forecast of medicine sales.

“Rolai provides contextual upskilling opportunities … on one single platform.”

Sundar Ramamoorthy
Managing Director of Solutions.AI, Global Products & Delivery Lead at Accenture

“An excellent tool for anyone who wants to quickly learn the ropes.”

Sanket Kawde
Head Data and Analytics at CitiBank India

“Rolai is the best program available for someone looking to enhance their skills”

Connor McEachron
Planning & Analytics @ Brooks Brothers

“Great way to learn data analytics and data science”

Balaji Reddy
Manager – Applications Development

“The courses were excellent and covered topics that I didn’t expect”

Aadarsha G
Student At Ohio Wesleyan University
All the Most Frequently Asked Questions

What People Are Asking About Data Education

Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.

Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.

No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.

  • We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
  • We also work with enterprises and universities to develop new content directed towards their industry and expertise.