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Life Sciences and HealthCare Analytics Program

Launch your career in LSHC Analytics with our certification program in Life Sciences and HealthCare Analytics course. Learn Statistical Analysis, Machine learning, Predictive Analytics, and many more.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 16 Hours of Intensive Classroom & Online Sessions
  • 24+ Hours of Practical Assignments
  • 2 Capstone Live Projects
  • 100% Job Placement Assistance
Life Science and Health Care Analytics course reviews - 360digitmg
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Life Science and Health Care Analytics course reviews - 360digitmg
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"Increased focus on value-based medicine has given ample growth opportunities to the life science industry. The life science analytics market is projected to reach $33 billion by 2024." - (Source). Healthcare analytics is emerging widely and is anticipated to transform the quality of healthcare organizations in the coming years. The adoption of Electronic health records (EHR) will favor healthcare analytics to grow immensely. Many developed countries are using healthcare analytics as solutions to complex issues and for economic growth. Huge investments are being made by industry players in Research and development processes for exclusive solutions with unique features and gain a competitive edge in the healthcare market. Data Science and software developments will attain more profits in medicine than other technologies. IoT and Big data analytics will transform Healthcare and Malaysia is going to register $2.1billions by 2020.

Life Sciences and HealthCare

healthcare course duration in malaysia- 360digitmg

Total Duration

1 Month

healthcare course pre-requisite - 360digitmg


  • Computer Skills
  • Basic Mathematical Knowledge
  • Analytical Mindsets

Healthcare Analytics Programme Overview

Healthcare Enterprises are leveraging AI-powered analytics for enhanced innovation and increased productivity. According to this PWC article, a little less than a third of the CEOs believe that AI will be the biggest disrupter to their industry. Following the trends in the broader Healthcare industry in the US, 360DigiTMG has developed the Certification Program in Healthcare Analytics. This course is meticulously designed to suit both Healthcare practitioners, IT Business Analysts and Data Scientists.

Healthcare Analytics Learning Outcomes

360DigiTMG offers the best training in Life Sciences and Healthcare Analytics by industrial experts. The Healthcare sector is developing rapidly with many innovations. There is a huge generation of data and it has to be analyzed for better insights. Healthcare analytics course is built as per the requirements of the Healthcare sector, it includes all the relevant technologies where students should be well educated to become a Professional Healthcare Analyst or Data Scientist. This certification program is designed specifically for the people who are health care practitioners and have knowledge of Life sciences. Students will be exposed to EHR data types, analyze clinical data. Learn about applications of Machine learning technologies. Able to provide solutions for complicated clinical environments


Work with various data generation sources
Understand and interpret Electronic Health Record (EHR) data types and structures
Become proficient in analysing Clinical Healthcare data
Differentiate among Descriptive, Predictive and Prescriptive Analytics
Apply Machine Learning techniques on Healthcare and other clinical data
Arrive at commensurate clinical and scientific interpretations of the conducted analytics
Foresee some of the challenges faced while implementing analytics solutions in complicated clinical environments

Block Your Time

healthcare analytics course in malaysia- 360digitmg

16 hours

Classroom Sessions.

healthcare analytics course in malaysia - 360digitmg

24 hours

Assignments &

healthcare analytics course in  malaysia - 360digitmg

16 hours

Live Projects.

Who Should Sign Up?

  • Data and Analytics Managers
  • Business Analysts
  • Data Scientists
  • Healthcare Practitioners
  • Clinicians and Physicians
  • Healthcare Administrators
  • Life Science Graduates
  • Research Scholars and Post Doctorates

Life Sciences Healthcare Analytics Course Modules

Advancement in Healthcare data analytics is bringing valuable insights for improving healthcare systems. The modules help the students to learn about different domains. Introduces data in electronic health records and other types of data like images from radiology, pathology, etc. Learn about the prime tools named Python, R, and its applications in analyzing healthcare data. Students will learn to decipher and assess designs of data models using Entity-Relationship (ER) diagrams. Understand about Genomics Exploratory Data Analysis, normalization, and batch effects of the data. Will be able to build MultiLayer Perceptron to build predictive analytic capabilities using the EHR and other clinical data. Learn to use CNN models to design prediction models that help to predict cancer using DICOM images. Will learn the concepts of Deep Learning and Machine learning and can leverage Deep learning networks to analyze panoramic dental x-rays. Learn about Genomics, Proteomics, and Metabolics. Learn about Data modeling, predictive modeling, and clinical Natural language Processing. Through hands-on training, students will be able to learn the concepts and applications comprehensively. Real-time use cases and training by industrial experts help students to gain adequate knowledge and be able to overcome industrial challenges.

1. Data Science Foundation Module

Get about, CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance. Get introduced to the principles of big data and learn about the opportunities being created. Understand about how Data is generation and explosion of data, Innovations in the space of analytics. Learn how to distinguish between data types, Exploratory data analysis, the Various moments of Business decisions and various Graphical techniques. Learn about probability and probability distribution namely Z distribution and Student's t-distribution.

Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples

Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.

2. Life Sciences and HealthCare Analytics Module

This module serves as an interesting primer for the uninitiated into the world of LSHC domain. Learn about the different sub-domains and how data science is empowering each of these domains. Get ready to deep dive into Life Science and Healthcare Industry, Sectors and Domains.

Welcome to the eclectic world of healthcare data. Understand the rich data structures contained in Electronic Health Records (EHR), work hands on MIMIC II data and other types of data like images from radiology, pathology, etc. Also Understand about healthcare IoT sensors data.

This module will give students the opportunity to understand the 4Ws of the healthcare data life cycle: What, Who, When, Why. Get a deeper understanding of the lifecycle to prepare for analyses that follow in subsequent modules.

As the name suggests, this module will help the learners to gather the requisite Python, R skills to analyze healthcare data such as EHR, MIMIC, etc.

This module will provide insights into various data models that are existing in Life Sciences and Healthcare Data. Learners can expect to gain knowledge on how to decipher and assess designs of data models using Entity-Relationship (ER) diagrams to contrast their individual merits and enunciate how to utilize them in providing Healthcare and Bio-statistics models, understanding the Genomics Exploratory Data Analysis, differential expression levels, normalization, and batch effects of the data.

  • a. Genomics - DNA, RNA (Next Generation Sequencing Data Analysis), Gene MicroArray Gene Expression Data Analysis
  • b. Proteomics - Deal with Proteomics Data Analysis, Drug Resistance and Drug Repurposing Data Analysis
  • c. Metabolomics - Molecular-level (Abundance and Intensity values) Data Analysis, Identifying Biomarkers, etc.

This module gets into the heart and soul of the Data Science program. We start from basic statistics and progress to regression models, unsupervised and supervised learning models.

  • a. Basic Statistics, Hypothesis Testing and Regression Models
  • b. Unsupervised Machine Learning Techniques
  • c. Supervised Machine Learning Algorithms

Utilize patient historical data, current diagnosis and treatment and other life style and behavioral indicators to predict whether a patient is likely to get re-admitted to hospital and take preventive actions.

This module gives a basic introduction to the Natural Language Processing (NLP) on clinical trials data. Learners can expect to gain pragmatic toolsets to extricate data stored in the EMRs (Electronic Medical Records).

Use MultiLayer Perceptron to build predictive analytic capabilities using the EHR and other types of healthcare data. Use CNN models to build a prediction model to predict cancer using DICOM images. Understand about limitations of Deep Learning models in Life Science & Healthcare.

This module describes how we can leverage Deep Learning networks to analyze panoramic dental x-rays and automatic segmentation of mandibles. This is a fast and accurate method and will help in diagnosis and registration of dental records.

How We Prepare You
  • Life Science and Health Care training with placements
    Additional Assignments of over 60+ hours
  • Life Science and Health Care course with placements
    Live Free Webinars
  • Life Science and Health Care training institute with placements
    Resume and LinkedIn Review Sessions
  • Life Science and Health Care course with certification
    Lifetime LMS for 6 Months
  • Life Science and Health Care certification
    Job Assistance in Life Sciences and HealthCare Fields
  • best Life Science and Health Care course
    Complimentary Courses
  • best Life Science and Health Care course training
    Unlimited Mock Interview and Quiz Session
  • best Life Science and Health Care training with placements
    Hands-on Experience in Live Projects
  • Life Science and Health Care course
    Life Time Free Access to Industry Webinars

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