Clinical Data Science

Work with the latest industry tools and learn how to apply them in the domain of clinical data science.

Overview

Why Clinical Data Science

The increasing demand for medicines is pressurising pharmaceutical research and development organizations to adopt computational tools to make research processes more efficient. There is massive demand today for trained professionals in the cross functional domain of Bio-IT.

In This Course

Get introduced to statistics with industry languages, R, SAS, SQL, Machine Learning and Tableau. Receive end-to-end training on all Clinical research areas with a special focus on clinical Trial management processes and documentation.

The Sollers Advantage

Our Certificate in Clinical Data Science program is taught by faculty experienced in the industry. The program includes a comprehensive three-month internship with Cytel where students work on five real-time projects using the latest industry tools.

Learning Outcomes

  • Proficiency in statistics and R
  • Expertise in Tableau, and handling bias issues in data visualization
  • Understand and learn the Structured Query Language (SQL) and RMDBS standards
  • Application of machine learning methodologies
  • Understand the Drug Development Process and Clinical research process
  • Expertise in SAS
  • SAS Clinical with industry CDISC (SDTMand ADAM)
  • SAS Clinical Data Integration

Syllabus

  • Data Mining with R
  • Introduction to R statistical software
  • Data structures in R [Vectors, Matrix, Arrays, Lists, Factors & data frames]
  • Exploratory Data Analysis
  • Introduction to Statistics
  • Visualization in R [ Graphics package, ggplot2, plotly etc.]
  • Sampling and population in Statistics
  • Probabilities
  • Statistical tests
  • Introduction to Machine learning
  • Regression
  • Classification
  • Evaluation of algorithms using Caret Package
  • Time Series Analysis
  • Clustering
  • Introduction to Artificial Neural Networks (ANN) in R using NNET, NEURALNET
  • AND H2O packages
  • Introduction to Machine learning in Python
  • Machine Learning with Python
  • Introduction to Python, data structures, functions, classes & objects
  • Introduction to machine learning
  • Linear Regression
  • SAS clinical and SAS with CDISC
  • Statistical and clinical research concepts
  • Base SAS programming
  • Introduction to working with SAS Clinical data
  • Statistical analysis plan
  • SAS programming environment and language
  • SAS Clinical with industry CDISC (SDTMand ADAM)
  • SAS Clinical Data Integration
  • Structured Query Language (SQL)
  • Accessing and using data with Structured Query Language
  • Data warehousing
  • Requirement gathering
  • Data Visualization with Tableau
  • Importing data, interpreting the chart, graph and table visualizations, editing, modifying and creating reports
  • Real time project on tableau, SAS
  • Clinical Research and Clinical Data Management
  • Good clinical practices
  • Roles and responsibilities of a clinical research associate
  • Drug inventory management, sponsor/FDA Audit preparedness, drug returns
  • Understanding of the process, systems, techniques and documentation using sample study documentation
  • 3 Months Internship (SAS)
  • 1st Project: To work on Clinical / Health Care data and predict the probability of risk for patient to develop a particular disease
  • 2nd Project: Analysis Dataset Development (SAS)
  • 3rd Project: QC of SDTM Dataset Development (SAS)
  • 4th Project: QC of AE and DM Tables
  • 5th Project: Data Import and Export, MapReduce application (SQL)

INTERNSHIP

1st Project

Data Mining with R

To work on Clinical / Health Care data and predict the probability of risk for a patient to develop a particular disease.

2nd Project

SAS

Analysis Data Development – create analysis datasets for laboratory data.

3rd Project

SAS

QC of SDTM Dataset Development – generate Demography and Disposition dataset as per SDTM standards.

4th Project

SAS

QC of AE and DM Tables – generate summary report for Demography and Adverse events data.

5th Project

SQL

Data import from RDBMS to HDFS. Build MapReduce application, and write output to HDFS. Export data to RDBMS.

Work with the latest tools

CDISC Training Clinical Project with STDM TrainingClinical Trials with TableauPython in Clinical ResearchClinical Trials in R LanguageClinical SAS TrainingClinical SAS Training

INSTRUCTORS

Our instructors are not just highly experienced in the industry, they give you the personal attention you need and guide you every step of the way.

Pavan Suryanarayan

Faculty of Clinical Data Science


Dr. Durdant Dave

Dean of Academics


Padma Iyer

Faculty of Clinical Research


Mark Koscin

Faculty of Clinical Research


Dr. Geetasree Alluri

Teaching Assistant for Clinical Research


Course Duration

15- 18 weeks (20+ hours each week)

3-4 Sessions /week

Engagement

300 hours

For information regarding fee and/or reserving your spot, contact our Admissions Team.

OUR STUDENTS WORK AT

Sollers partners with industry-leading corporations and provides them with ready-on-day-one employees. We record an 82% placement rate within three months of graduation.

Financial Options

Sollers has devised viable financial options for you to ensure tuition does not get in the way of your education. Now, you can focus your attention where it needs to be – in the classroom!

Career Guidance

After the completion of program, we assist our students with interview coaching, resume building sessions, conduct mock interviews, job readiness training and make them competent to venture into the corporate world.

Student Testimonials

  • Emmanet T.
    The Clinical Trial Management course is extensive, and the professors are thorough in delivering the course materials using real life experience and insight. Career Services helped me throughout the interview process, and I landed my dream job with the very first interview after graduating from Sollers.
    Emmanet T.
  • Rudolf M.
    Thanks to the academic team at Sollers, I was able to gain quick admission with financial options to pay tuition. Career Services was very helpful with resume and interview prep. They sent me job postings with major hospitals and pharma companies which helped me land my current job as Data Manager in cancer clinical trials.
    Rudolf M.

FAQs

Clinical Data Science is one of the fastest growing career paths and best paid career paths. Data scientists and clinical SAS analysts use a combination of programming, statistical perspective and domain expertise to do so. They are expected to be in large demand as businesses grow in storing the data that they generate to achieve competitive differentiation in their products and services.

The program focuses on the convergence between traditional clinical SAS analytics, and data science that’s been occurring within the healthcare industry for analytics. The curriculum will focus on Clinical research, clinical data management, SAS, SQL, statistical modeling, data visualization with Tableau and business analytics.

Our students come from all different backgrounds, with the most competitive candidates including those with Bachelors, Masters, or PhD Degree in Statistics, and Sciences. Some have similar backgrounds to Clinical Research.

Our 300-hour program can be completed in as little as 15 weeks – All of the programs offered by Sollers have been designed to best suit those seeking to enter the fields of clinical research, healthcare, pharmaceuticals, and IT among other domains.

It is preferable that a student be a Bachelors or a Masters graduate in STEM / Science; including Statistics, Engineering, Computer Science or other sciences. Some students may have sufficient knowledge in some areas.

Clinical Data Science is one of the fastest growing and best paid career paths. According to the Global Clinical Data Analytics Market (2019) Report, by Journal Worldwide, the market will grow to an even stronger $16.58 Billion by the end of 2023 – with a growth rate (CAGR) of 33.07% per year. This represents an incredible opportunity to get in on the ‘ground floor’ of a major revolution in clinical data science!

Core knowledge of Clinical SAS Analytics and Data Science topics; including Clinical research, Clinical data management, statistics, and data visualization, with conceptual and practical application of theories and tools used by the industry

Experience in using Statistical Analysis and Database management tools such as SAS, MySql, Tableau, R, Python

Ability to Dive into the workplace with a competitive skill set by taking large amounts of data and get heavily involved in the life cycle of a full-fledged Clinical SAS Analyst

Anybody with a background in a STEM, computer science or statistics, who would like to broaden or advance their career opportunities.