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Life Sciences and HealthCare Analytics Training
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"Innovation starts with insight and life science companies strive to pursue innovative solutions to address today’s challenges. A HealthCare analyst in Price Waterhouse Coopers (PWC) earns 6 lakhs per annum." - (Source). Analytics is an essential part of Life Sciences and HealthCare because it increases the potential for better treatment and medication. Phenomenal progress in the field of drug development, gene therapy, and wearable fitness trackers are some of the transformations seen in the life sciences sector. Today Patients are increasingly involved in their HealthCare and demand transparency, convenience, and personalized services. With Analytics in Life Sciences and HealthCare Course, companies are trying to keep up with the pace of the customer by leveraging the insights derived from the data strategically to their advantage. The amount of data available to the pharma companies today is like a treasure trove of information used to create value and new metrics from the wealth of data at their disposal.
Life Sciences and HealthCare
HealthCare Analytics Course Overview
The training in Life Sciences and HealthCare Analytics course will equip you with research and analytical skills that will change the HealthCare landscape. Data has been around us for centuries but the skill to collect, organize, and visualize this data is making all the difference. Analytics when used in the Life sciences and HealthCare arena can result in numerous benefits like enhancing the health delivery system, improving the quality and safety of patients, lowering costs, and optimizing business operations. Launch your career in the booming HealthCare analytics market with the pioneering Life Sciences and HealthCare Analytics Certification Program. Designed for HealthCare practitioners, IT business analysts, and data scientists interested in the HealthCare domain provides industry-leading training in Data Analytics and AI.
This program is designed to have the right blend of Statistical Analysis, Predictive Modelling, and Artificial Intelligence in making human health better by taking proactive decisions. Following the trends in the broader HealthCare industry in the US, 360DigiTMG has developed a certification program in Life Sciences and HealthCare Analytics.
What are the Life Sciences and HealthCare Analytics?
Life sciences and health care analytics is a process where clinical data is studied and analyzed to provide tools for expert advice and proactive care of the patients. It is considered to be an integral part of the health care management system. Companies today are adopting data analytics and leveraging insights to manage a patient’s health outcomes. Huge databases are created with the information of the patient’s current health issues and using Analytical tools extensive study is implemented to prepare a predictive model. This model facilitates the future HealthCare of the patient and allows better decision making based on the evidence of his past HealthCare reports. This also helps pharmaceutical companies predict the future trends of the treatment and bring efficacy to the life science industry by moving from treatment to preventive measures to early intervention.
HealthCare Analytics Course Learning Outcomes
Analytics in Life Sciences and HealthCare has become an important part of HealthCare management. It is helping the HealthCare sector blend the booming data collected from various sources like bedside sensors, bio-monitor and other Internet of Things devices to generate data insights into a refined easy to understand model that can predict the future health of the patient and allow better decision making for doctors and health care professionals and to produce knowledge-based solutions for real-world challenges. But, a widening gap between the demand-supply of skilled professionals in the field of Life Sciences and HealthCare Analytics is creating challenges for public and private HealthCare systems. This program provides students an opportunity to explore a wide variety of careers in the HealthCare and Life Sciences field. Learn more about HealthCare and life sciences and how it impacts our world. Progress in the field technology, science, digital data, and augmented intelligence are transforming the traditional health care models. The curriculum provides extensive knowledge of the new elements of the HealthCare industry. Identify the various data generation sources and understand the different types of analytical tools used to develop the ability to foresee the challenges in a clinical environment. Students will gain expertise in areas like predictive and prescriptive analytics, data acquisition, data mining, and HealthCare information management systems.
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Who Should Sign Up?
- IT Engineers
- 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 and Healthcare Analytics Course Modules
This course is aimed to highlight the importance of Life Sciences and HealthCare Analytics. It covers concepts dealing with HealthCare data including IoT sensor data. Learn to analyze and interpret HealthCare data effectively and understand the rich data structures contained in Electronic Health Records (EHR). This module will also help the learners to gather the requisite Python, R skills to analyze HealthCare data such as EHR, MIMIC, etc. This module also gives an insight into various data models that exist in Life Sciences and HealthCare Data. Gain knowledge on how to decipher and assess designs of data models using Entity-Relationship (ER) diagrams. You will also get into the heart and soul of the Data Science program along with predictive modeling for hospital management. This course in Life Sciences and HealthCare Analytics will fill in the gap for those who are interested in this challenging and ever-evolving field.
- Challenges and Opportunities in Life Sciences and Healthcare Analytics
- Overview of AI Applications in Life Sciences and Healthcare
In this module we will be discussing the CRISP-ML (Q), it is a research methodology used in the life sciences to integrate statistical and machine learning methods with clinical reasoning in order to improve the accuracy of medical diagnoses and treatments. The goal of CRISP-MLQ is to develop quantitative models that can assist healthcare professionals in making more informed and efficient decisions, while also taking into account the uncertainties and biases inherent in medical data.
This module covers the essentials of Exploratory Data Analysis (EDA) and Data Preprocessing in the context of Life Sciences and Healthcare data. The goal is to familiarize participants with techniques for cleaning, transforming, and preparing data for analysis in order to obtain meaningful insights and inform decision making.
This module explores the application of K-means algorithm, a popular unsupervised learning technique, in the Life Sciences and Healthcare domain. Participants will learn how to use K-means for data clustering, gain hands-on experience in implementing the algorithm, and understand its potential for solving real-world problems in the field.
This module focuses on the K-Nearest Neighbors (KNN) algorithm, a popular supervised learning method. The module covers how KNN works, its implementation in the Life Sciences and Healthcare domain, and its applications for solving various problems in the field, such as diagnosis and prognosis. Participants will gain hands-on experience in using KNN algorithms for data analysis and prediction
In this module, the working principle of Decision Tree algorithms for supervised learning in Life Sciences and Healthcare is explored, with a focus on TNBC relapse prediction. Participants will gain a thorough understanding of how Decision Trees operate and their practical applications, through hands-on work with a real-world TNBC relapse prediction use case
This module focuses on the working principle and application of Linear Regression, a supervised learning technique, in Life Sciences and Healthcare. A simple use case of predicting Adipose tissue using waist circumference is used to demonstrate the implementation and potential of Linear Regression in the field. Participants will gain hands-on experience in using Linear Regression for data analysis and prediction.
This module discusses the usage of Multilayer Perceptrons (MLP) in Life Sciences and Healthcare, through the exploration of a practical use case. Participants will learn about MLP, its working principle, and how it can be applied to solve real-world problems in the field, and gain hands-on experience in using MLP for data analysis and prediction.
This module examines the application of Natural Language Processing (NLP) in Life Sciences and Healthcare, using the use case of sentiment analysis on drugs. Participants will learn about NLP, its potential and challenges in the field, and gain hands-on experience in using NLP techniques for sentiment analysis on drug-related data.
Life Sciences and HealthCare Analytics Course Trends
The HealthCare and Life-sciences sector in India has transformed due to the development of traditional technologies. With the help of data mining, clinical diagnoses, and self-monitoring devices we can maintain a healthy lifestyle. The HealthCare world has now transcended from treatment to preventive measures to improving patient outcomes. The intervention of Machine learning and deep learning in various applications of life sciences, health care, pharmaceuticals, food processing, biotech, and e-health has enabled companies to provide more efficient customer service, enhanced logistics, and better equipment.
Artificial intelligence is not far behind and is making a large scale impact on the life sciences industry. It is helping analyze vast data sets collected from clinical trials, health records, and genetic profiles and is assisting doctors make better, faster, precise diagnosis and treatment strategies. Analytics in this field has also become an essential part of evidence-based medication and treatment which is changing the landscape of HealthCare in our country. It is used for giving accurate results which also helps in the development of drugs and better analysis of patient’s treatment and medication. New Analytics Techniques are improving clinical and business outcomes in the life science industry and are also contributing to sales and marketing applications. The Indian export pharmaceutical sector is estimated to reach the size of USD 20.1 billion by 2020.
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