DATA SCIENCE & MACHINE LEARNING IN PHARMA
It takes around 10 to 12 years and several billion dollars to develop a new drug, and it takes even more years to launch that drug in a market. Data Analytics in the pharmaceutical industry is here to put and acceleration to this discovery and development. Data science in a pharmaceutical company can reduce making these drugs and speed up trial and development. These data analytics can provide pharmaceutical companies to identify their competitors and plan actionable data strategies in a highly demanding market. Companies are highly investing in data science programming languages such as Python and Biopython, which showcases the necessity of data analysis in bioinformatics
KEY USES IN PHARMA
Accelerate drug discovery and development
During the past few years of pandemics and increasing technological advancement, the pharmaceutical industries have changed. Now companies need data like various conventions, scientific journals, clinical trials, and patents. Data science and analytics helps these companies to acquire these data cost-effectively, which will help their development and discovery. It also helps companies to compare the results of old scientific experimentation with new trials and tests. You can dive deep into research and relevant parameters for deeper insights into pharma and bioinformatics.
Improve safety and risk management
The mindset and sentiments of people are essential for companies when they release
or put certain drugs on trial. This analysis of the sentiments of people through the
internet and getting feedback for their companies and competitor companies can also
be done through Data Science and Analytics.
Machine Learning for Clinical Studies and R&D
One of the common challenges faced by data researchers and developers is distinguishing between relevant and irrelevant data for the intended target audience. Some of the most suitable algorithms like Neural Networks, Deep learning, Bayesian Machine Learning are the best-known methods for optimal audience targeting through Data Science and Machine Learning.
Target specific patient populations more efficiently
Since every individual’s capacity and character are different based on their genomic structure, they require different medications and biology. But personalising every medicine according to each individual is a hell of a lot of tasks. Data science and analysis can help you to ease up that process. It combs through the data of genomic sequences and devices and monitors the physical change in every individual through electronic medical records.
Better insight into the patient journey and patient behaviour
Data science and Data Analytics can monitor unstructured genomic characters of a patient and can analyse the patterns to create more personalised medications for patients. They can build strong insights for the development of their medicine by the feedback of the customers. They can learn what factors push patients to discontinue their medications or what patients will not follow their prescriptions. With the help of machine learning clustering and scoring models, companies can figure out the best
suitable medication for their subjects. The customers’ sentiments, views, and feedback are also essential for reacting to their
particular product. Data Science and Machine Learning can also achieve this.
Improve the efficacy of clinical trials
Lengthy and dreary processes of clinical trials and drug development can cause a hell of a lot of money and waste a lot of time. This dreary process also cannot assure efficient data and cannot distinguish between relevant and irrelevant data. This is where the data scientist can provide efficient and cost-worthy data. Through the real-time monitoring of data, data scientists can actively deal with the risks and the problems during drug development and trial beforehand in the early stages and pinpoint the safety options to the company. Data scientists can also deep dive into various researches and data and multiple social media sources to identify the target audience based on different criteria like genomic structure, type of medicine needed etc.
Gain deeper insight into sales and marketing performance
Medical representatives are equipped with modern devices and skilled teams to carry out data science analytics on target clients. Through the data analysis of social media, medical records, population records, and other reliable sources, you can easily hike your business sales and promote them among the large masses of people. The statistical data and data sales can keep your company ahead of your competitors in the market. It can also provide insights into the market capabilities and market conditions.
Curriculum
Module 1: Python Fundamentals and Programming
Module 2: Data Handling with NumPy and Pandas
Module 3: Data Visualization with Matplotlib
Module 4: Advanced Plotting with Seaborn
Module 5: Introduction to Statistics for Data Science
Module 6: Sampling methods in Statistics
Module 7: Exploratory Analysis and Distributions
Module 8: Advanced Statistics for Data Science
Module 9: Machine Learning (ML) Fundamentals
Module 10: ML Regression and Classification Algorithms
Module 11: ML Advanced Algorithms & Techniques
Module 12: Deep Learning & Neural Networks
Module 13: Applications of ML in Bigdata, Could Computing etc.
Contact for Course
Enrol in Data Science with Python for Pharmacy course. Every batch starts every Monday.
