Statistical Analysis with R for Reproducible Research (Cohort 2)

Analyze research data with R and produce publication-ready tables and figures

Instructor-led Live Sessions
Recording available
Lifetime access

27+ hours of hands-on training
Plus, 20+ hours of pre-recorded videos explaining concepts
Networking and collaboration opportunity

20+ hours of Office Hours / TA Hours

Start/End Dates

Starts
Saturday, February 25, 2023

Ramadan break
March 21 - April 21

Classes resume
Saturday, April 29, 2023

Ends
Sunday, May 28, 2023

FREQUENTLY ASKED QUESTIONS

এই কোর্সটি মূলত গবেষকদের জন্য, যারা গবেষণায় নবীন, গবেষণা করছেন, অথবা এক বছরের মধ্যে গবেষণায় পা রাখবেন।

অ্যানালিসিস মূলত স্ট্যাটিসটিক্যাল অ্যানালিসিস।  রিসার্চ পাবলিকেশনে যে ধরনের এনালাইসিস প্রয়োজন হয় সেই ধরনের।  

স্ট্যাটিস্টিকসের বেসিক থেকে এডভান্সড কাভার করা হবে কনডেন্সড ফরম্যাটে।  

স্ট্যাটিসটিক্স এর টপিক একই হলেও মূল ফোকাস থাকবে গবেষণার কাজে এর ব্যবহার।  সেই বিবেচনায় আমি বলব জেনারেল ডেটা সায়েন্স বা মেশিন লার্নিং এর জন্য এই কোর্স নয়।  

যে ডাটা গুলো ব্যবহার করা হবে সেগুলো পাবলিকলি অ্যাভেলেবল ডেটা।  

কোর্সের লিংকে ডিটেল আছে, কি টপিক কাভার করা হবে এবং কোর্স টা কাদের জন্য এবং কাদের জন্য নয়।

Schedule

Course Summary

"Statistical Analysis with R for Reproducible Research" prepares biomedical, public health, and researchers in other fields to perform statistical analysis of research data. This course teaches statistical concepts essential for research and how to perform statistical analysis for scientific publications and reporting.

The reproducibility of research has become an essential requirement for many scientific journals and among the scientific community.

With powerful open-source R tools for statistical computing, you will learn how to project-manage your entire analytic workflow and carry out statistical analysis with the reproducibility of results built into it.

No prior R programming experience is required. But general computer literacy is needed either in Windows or Mac environments. 

To be successful in this course, you should be comfortable installing / uninstalling software, as well as using Windows Explorer or equivalent tools to find and organize files and folders.

What you will Learn

Applied Statistics

Learn enough statistics to understand statistical analyses found in scientific papers

R for Data Analysis

Learn tidyverse ecosystem of R for research data analysis with minimal coding

Reproducible Research

Create reproducible report with publication-quality tables and figures

Learning Outcomes

  • to understand statistical analyses presented in biomedical, public health, and social science journal papers 
  • perform statistical analysis and use appropriate hypothesis testing procedures to answer research questions 
  • be comfortable handling and analyzing data 
  • become familiar with R for data analysis 
  • be able to create reproducible statistical reports 
  • be able to create publication-ready tables and figures learn analytic project management for improved efficiency

Who this course is for

This course is most suitable for those actively working on a research project.

Or someone aiming to become an independent researcher in biomedical, public health, or other fields where statistical analysis is essential.

However, this course can benefit anyone interested provided that they are–

  • Highly motivated and self-disciplined individuals
  • Currently working or will potentially work on hypothesis-driven scientific research projects 
  • Active researchers and students who want to perform statistical analysis on their own 
  • Researchers without formal statistics training who wish to understand statistical analysis performed in published literature 
  • Researchers who need some refresher on statistics 
  • Students/researchers wishing to learn the tidyverse ecosystem of tools for data analysis 
  • Wishing to better manage their data analysis projects for efficiency and reproducibility 

Who the course is not for

This course is not for–


  • Those who want to learn research methodology. This course will not teach you research methodology
  • Researchers looking for Python-based data analysis training 
  • Learning how to build models for the sole purpose of predictions 
  • Not committed to allocating 6-8 hours per week for six to eight weeks 
  • Not disciplined to attend classes and complete the required activities (watching video lectures, homework assignments) on time 

Course Curriculum

About the Instructor

Enayetur Raheem, Ph.D.

Dr. Raheem is a data scientist, statistician, and educator.
 
He is an accomplished data scientist with over 20 years of combined experience in academia and industry. He is currently working as a Principal Data Scientist at an AI company in the US.

He has taught graduate-level statistics courses at Dhaka University and several universities in the US before transitioning to the industry as a Data Scientist.

He is a lifelong learner and passionate about teaching online. He is the founder and principal instructor at dataskool.org.

Dr. Raheem will be your instructor for this course.

Md Mahdi Hasan

Md. Mahdi Hasan is a statistician and is currently a Data Analyst at a start-up company.
 
Mahdi will be the Associate for this course.
 
Mr. Hasan received his Bachelor's and an M.Sc. degree in Statistics from Dhaka College.

Enrollment Fee

Professionals
Regular: 15,000 BDT / 140 USD
 Early registration before Feb 15
10,000 BDT / 95 USD

Students (PhD or under)
Regular: 12,000 BDT / 115 USD
 Early registration before Feb 15
9,000 BDT / 85 USD

BRF/ResearchWay Trainee
Regular: 10,000 BDT / 95 USD
 Early registration before Feb 15
7,000 BDT / 65 USD

Students requesting scholarship
7,000 BDT / 65 USD
Scholarship may be awarded to up to 5 students.
To be considered, send a Statement of Purpose (SOP) to
[email protected]

Financial hardship alone is necessary but not a sufficient reason for a scholarship award.
Best SOPs are 1-2 pages long that describe why you need a scholarship, your background,  your preparation to succeed in this course, and how this course would benefit you in the next one to two years.


Deadline for Scholarship Application
Feb 1, 2023


Payment Options

One-time Credit Card Payment

bKash Payment