Clinical Data Science

Accelerate your career with Clinical Data Science certification from Clin Infotech, is one of the leading Clinical Data Science Training Institute. This Clinical Data Science course is a perfect blend of theory, case studies and capstone projects. The course curriculum has been designed by Industry Experts and considered to be the best in the industry. Get noticed by recruiters across the globe with the international certification. Post certification We Will Provide the Placement Assistance in Top IT Companies with Excellent Salary Packages.

Introduction to Clinical Data Science:

Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist! This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data. By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data.

What is Data Science?

Data science is a field within the software industry that concerns itself with studying how companies can obtain actionable insights from data.

Data science emerged as a field because of the increasingly large volumes of digital data that is produced every day. To some, that amount of data was just too large to be processed and become valuable in some way. But it became apparent that techniques from statistics and computer science could be used to unearth patterns in the data and derive meaningful insights from it. Thus, data science was born.

Scope of Data Science

Data Science is one of the fastest-growing fields in India. Data Science job opportunities in India are increasing as organizations seek to harness the power of data to drive decision-making. Some of the most common job roles in data science include data analysts, data scientists, and big data engineers. A. Data science offers several advantages as a career choice. Firstly, it has a high demand across industries, including finance, healthcare, e-commerce, and technology. Secondly, data scientists enjoy competitive salaries and benefits due to their specialized skills.

Data Analyst vs Data Scientist

We've got a full guide comparing data analysts and data scientists: However, in summary, while both roles work with data, they differ in scope and responsibility:

• Data Analysts: work on interpreting existing data to provide actionable insights. They may use simpler models and tools.
• Data Scientists: Take on a broader role, often formulating the questions that need answering. They use advanced tools and models, including machine learning algorithms, to derive insights.

What Do Data Scientists Do?

Data scientists spend some of their time analyzing datasets and looking for insights that can help businesses improve key metrics. But that’s not the only thing that data scientists do.

Data scientists work with key stakeholders at businesses to find out what their major challenges are. This lays the foundation for the data science process. The challenges of a business become problem statements for data scientists.

They then proceed to build data models and write algorithms that can process data. All of this is done with the goal of finding solutions to the initial problem statement. Data scientists use various techniques to make this happen, including mathematical analysis, predictive modeling skills, natural language processing, regression analysis, deep learning, and analytical thinking.

Data science’s applications are virtually unlimited. For example, in the marketing sector, digital marketing agencies are always on the lookout for ways to enhance the marketing ROI of their campaigns. There are armies of data scientists working on this problem to see how customers can get the most bangs for their buck.

In order to do that, data scientists study different marketing channels, ad types, and the creative’s that are used to promote products. They obtain datasets from marketing campaigns, the cohorts selected for different ad sets, and study aspects of the creative’s used, such as the copy and images. The insights from this analysis are used to develop marketing campaigns that can reach the most people for the lowest price possible.

What Skills Do Data Scientists Need?

We’ve covered the top 15 data scientist skills in more detail in a separate article, so we’ll summarize that here. Essentially, to succeed in data science, you’ll need an assortment of hard and soft skills.

How to Get Into Data Science?

By now, you’re likely eager to get started on your journey to becoming a data scientist. But where to start? Below, we’ve highlighted eight steps you need to take to become a data scientist from scratch.

As we’ve discussed already, the exact data scientist requirements will depend on a variety of factors, but these are some of the most commonly cited steps:

1. Learn Data Wrangling, Data Visualization, and Reporting

As you work to become a data scientist, you’ll often encounter large and complex datasets. To make sense of this information (for yourself and others), you’ll need to learn how to deal with it. This is where skills in data wrangling come in handy. This is where you’ll clean, organize, and transform raw data into a format you can analyze and draw conclusions from.

There are various tools you can use with data wrangling, but libraries like pandas in Python are often the go-to choice. You can check out our data wrangling in Python with pandas cheat sheet for more info.

When it comes to presenting your data with impact, you’ll need to master reporting and data visualization. You can find out more about some of the best data visualization tools out there, giving you a range of options depending on your ultimate goal.

2. Work on Your Statistics, Math, and Machine Learning Skills

While you don’t necessarily need a degree that covers these fields, you do need a functional knowledge of these areas. Generally, you’ll want to cover areas such as calculus, linear algebra, and statistics. However, it’s worth assessing your intent behind learning these things.

For example, learning calculus can help you learn to create optimization algorithms for machine learning, although there are functions in both Python and R that can take care of the calculus for you. As a specific example, understanding a topic like gradient descent can help you measure the change in a function’s output when you tweak the inputs, which can help to refine machine learning models.

If you’re looking to brush up on your linear algebra skills, our Linear Algebra for Data Science in R course can help you work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses, and use principal component analysis to do dimension reduction on real-world datasets. These areas are all useful for applied mathematics and data science.

Similarly, Intermediate Statistical Modeling in R covers some of the essential machine learning statistics topics, helping you make progress in key areas.

3. Learn to Code

In our article on the top programming languages for data scientists, we explore in detail some of the best languages you should consider learning right now. Two of the most obvious choices for data scientists are Python and R, given their versatility and ubiquity.

Of course, working with data also means working with databases, so SQL is another essential programming language. Thankfully, it’s a relatively straightforward language once you’ve learned Python and R.

Julia is a good choice once you’ve learned Python, R, and SQL. With Julia, you get a language that’s built from the ground up for data science, making it fast and intuitive. If you start working with very big data sets, you may need other languages, but until then, these four will be sufficient.

With a language like Java, you get an open-source language known for its efficiency and performance. For data science, Java Virtual Machines provide a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala.

Other coding languages worth learning for data science with very large data sets include Scala, C/C++, JavaScript, Swift, Go, MATLAB, and SAS.

4. Understand Databases

We mentioned SQL in the topic above, and it’s a point that bears repeating. Relational databases allow data scientists to store structured data in a fast and efficient way. When collecting and organizing data, you’ll often find that SQL is your preferred tool here.

SQL allows you to handle structured data, query databases, wrangle, prepare, and experiment with data, and more. What’s more, SQL is often deployed alongside Python, with libraries such as SQLite, PostgreSQL, and MySQL helping you connect different data sources.

5. Learn to Work with Big Data

We’ve already touched briefly on the fact that, as a data scientist, you’ll often be working with large sets of data. In an era where everything and everyone is producing more data than ever before, these data sets are increasingly huge, making them harder to collect, maintain, and process.

Yet from these huge data sets, a skilled data scientist can extract new and detailed insights. So, learning to use cloud platforms such as AWS, Microsoft Azure, and Google Cloud can all benefit your career as a data scientist. Similarly, tools like Apache Spark can help with big data processing, analysis, and machine learning.

You can learn the big data fundamentals with PySpark with our course, which covers some of the essentials when dealing with large amounts of data for analysis.

6. Get Experience, Practice, and Meet Fellow Data Scientists

As with any career, to become a data scientist, you’ll need as much experience and practice as possible. Luckily, there are many ways that you can get involved with communities, work on projects, and develop your data science skills.

For example, Data Camp Workspace provides a collaborative cloud-based notebook that allows you to analyze data, collaborate with others, and share insights. Workspace is designed to take you from learning data science to doing data science. It also has built-in datasets, so you can start analyzing data within minutes.

You can also apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser.

7. Take an Internship or Apply for a Job

Once you’ve developed all of the skills mentioned above, you’ll want to start applying them in more professional settings. Once you’re confident you have the data scientist skills needed to meet the expectations of a role, you can start applying for internships or jobs.

Check out our full guide on applying for data science jobs and learn how to set yourself apart from other candidates. You’ll likely need a fairly thorough portfolio demonstrating a range of skills, and you’ll also need to prepare for the data scientist interview ahead of time.

8. Follow and Engage with the Community

If you want to become a data scientist, you’re going to need to keep up-to-date with a fast-paced industry. There is no better way to stay informed about developments in data science than by engaging with what can often be a generous and dedicated community.

As well as social media sites such as LinkedIn, Twitter, Discord, and Reddit, there are all kinds of niche sites, blogs, and data science leaders you can follow. Look for people who are interested in the same areas as you, reach out for advice, contribute to discussions, and get involved with what’s going on. And, of course, check out the Data Framed Podcast to get industry news from a host of data professionals.

Educational Paths to Become a Clinical Data Scientist

Becoming a data scientist is a journey that can start from various educational backgrounds. As we've discussed, a bachelor's degree in data science, computer science, or a related field is often the first step that people take, but in reality, there are multiple paths to gain the necessary skills:

• Online courses and certifications: Starting with online resources such as Data Camp’s data science tracks is an excellent way to gain the skills you need. As well as building your knowledge in various areas, you can also work towards becoming a certified data professional with our recognized certification.
• Undergraduate degree: A bachelor's degree in data science, computer science, statistics, or a related field provides a strong foundation.>
• Data science boot camps: Data science boot camps offer intensive training to help you quickly acquire practical skills. You can read more about the top data science boot camps and some alternative options in our separate article.
• Master's degree. For those looking to specialize or gain advanced skills, a master's degree can be beneficial.

Job Roles in Clinical Data Science

• Data Analyst
• Data Engineer
• Database Administrator
• Machine Learning Engineer
• Statistician
• Business Analyst

What You Will Learn

Describe how each type of clinical data are generated, specifically outlining who creates the data, when and why the data are generated.

• Write SQL code to combine two or more tables using database joins.
• Write R code to manipulate and tidy data including:
  • Selecting columns
  • Filtering rows
  • Joining data sets
• Write markdown formatted text and combine with R code in R Markdown documents.

Eligibility

Data Scientist Eligibility

• Candidates must complete class 12th in the science stream with Physics, Computer Science, and Mathematics as their compulsory subjects.
• Bachelor's degree in computer science, statistics, or information technology is required.
• Participate in the Internship Program for Data Scientists.
• Your career can be advanced with a Master's degree and certification.

Selection Procedure

• To maintain high academic standards, Clin Infotech gives due importance to the quality of students enrolled. To ensure this, the selection of individual students will undergo the following procedure:
  • Personal Interview
  • Screening and Selection of Students

Why Clin Infotech for Clinical Data Science?

• Complete course material will be provided, including detailed information about the entire workflow of a Clinical Research Associate.
• 2 months internship available after rigorous training in Pharmacovigilance.
• Own CRO live projects.
• Highest placement record across all training institutes.
• Industry-based SOPs training.
• Trainers with a minimum of 7+ years of experience.
• Comprehensive teaching by medical doctors.
• Total 150 hours of exposure to theory, case studies, and practicals.
• Hands-on training will be provided on clinical research sites.

About Clinical Data Science Prepbytes Placement Preparation Program

We make Pharma IT Aspirants job ready for placements in IT/Software companies by making their Clinical Science fundamentals strong with emphasis on improving coding & problem-solving skills through Mentorship, Internship driven Placement Preparation Program having personalized content, regular mock tests, doubt sessions, performance review & mock interview sessions by Our Industry Experts.

Integrated Specialized Course

• Certification in Personality Development + Communication Skills & Soft Skills – 1 month (Complete Pre Recruitment Drive)
• Dual Specialized Course:
  • Certification in Corporate Training + Personality Development & Soft Skills – 1 month 15 days
  • Certification in Spoken English + Corporate Training + Personality Development + Communication Skills & Soft Skills – 2 months

Our team of experienced drug safety specialists also provides the following upon successful completion of training:

• Resume Preparation
• Interview Preparation with real-time questions and answers
• Mock Interview Sessions
• Communication Skills and Personality Development Sessions
• Placement Assistance
• Placement Assurance

It's time to comprehend and put standard trail management ideas into practice in order to build a solid methodology for the evaluation and boost the timely completion of crucial trials for the benefit of patients. To plan, produce, monitor, and complete all project duties, you must acquire specific skills and knowledge. Through its comprehensive certification program, Clin Infotech offers thorough instruction in clinical research throughout the whole clinical trial process. With the help of this clinical trial management course, instructors will make sure that students have a procedural and administrative understanding of the clinical trial process as well as the capacity to advance their knowledge and abilities. This curriculum will teach you the whole Regulatory responsibilities involved in Document Creation; Planning, Organizing, Monitoring, Recording, Analysis, and Reporting of Human Clinical Trials, as well as the most recent discoveries and specialties in e-Clinical Technology.

Through its comprehensive certification program, Clin Infotech offers thorough instruction in clinical research throughout the whole clinical trial process. With the help of this clinical trial management course, instructors will make sure that students have a procedural and administrative understanding of the clinical trial process as well as the capacity to advance their knowledge and abilities.

Enroll Now!

Take advantage of this opportunity to engage with cutting-edge industry technologies and learn how to apply them in the domain of Clinical Trial Management.

Certification

Certificate will be provided for this Course on successful completion of Assignments & Projects.

Certificate would be awarded at the end of the program by Clin Infotech. A fast track weekend course will also be conducted for employees & aspirants of Clinical Research Industry.

Placement Opportunities

Clin Infotech is a pioneer in providing Clinical Data Science training and Placement program. We are india's first ISO 9001:2015 certified institute with placement cells being operated in Hyderabad, Bangalore Pune and Mumbai. Our well-integrated training cum Internship programs integrated With Real Time Project based Learning and ensure our outstanding placement record of 100% genuine placement assistance and lateral placements, 600 plus campus drives and 2000 plus offers every year from top MNCs IT Pharma and Biotech companies, service and product-based companies, and start-ups through virtual hiring, on-campus or of-campus recruitments.

Our Placement Companies :

For more information,please contact Clin Infotech at

Email: careers@clininfotech.com

Phone: 8374457387