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Project details

This is a crash course, offering an introduction to “Inferring Causal Effects from Observational Data”. It is perfectly suited for graduate students and practitioners who are switching to biostatistics-related research or clinical investigations, from prior education in the field of engineering or computer sciences. The new concepts are connected with familiar concepts from the domain of technology-centric programs. In this context, it’s a specialized offering to put learners on a fast track!

The course is not set in rock – in fact, I change it to accommodate a client’s requirements. If a learner needs to focus more on one aspect over the other, I can make it happen! The course is delivered one-to-one, in a set of 5 one-hour-long lectures. The client can set the schedule for these lectures, but I request a 5-day notice to align their preferred schedule with mine.

I do not claim to be a biostatistician, nor am I a clinical practitioner. I am a seasoned statistician, with 12+ years of data science and engineering expertise, spanning different application areas. I love cross-disciplinary environments, as they often help expand the horizon and open new avenues of thinking.
Project Type
Training & Development

What's included $400

These options are included with the project scope.

$400
  • Delivery Time 5 days
  • Number of Revisions Unlimited
5.0
8 reviews
100% Complete
1% Complete
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1% Complete
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YK

Yonas K.
5.00
Jun 9, 2026
Inference Statistics tutor

HA

Haya A.
5.00
Jan 10, 2026
Help needed to review research question/statistical analysis plan and structure research paper.

RC

Ryan C.
5.00
Sep 17, 2025
Statistic and probability tutoring Great to work with Prof Adnan

SE

Sarah E.
5.00
Sep 6, 2024
Data visualisation + Descriptive statistics in R or Python Adnan was very professional, extremely good at communicating, and delivered work fast! I appreciated that he went above and beyond on some tasks, pointing out things I had overlooked and suggesting ways we could do things better.

MM

Michelle M.
5.00
Jul 3, 2024
R coding to restructure data (rush job)
Muhammad Adnan S.Status: Offline

About Muhammad Adnan

Muhammad Adnan S.Status: Offline
Spatial Data Science & Remote Sensing expert | Python, R, MATLAB, QGIS
100% Job Success
5.0  (8 reviews)
Lahore, Pakistan - 7:18 pm local time
As a Remote Sensing Engineer with expertise in Spatial Data Science, I specialize in working with different types of remote sensing data, such as Synthetic Aperture Radar (SAR) imagery, multispectral sensors, radiometers, and ground-based sensors. I have developed proficiency in Spatial Statistics and GIS, which are essential for analyzing and interpreting remote sensing data. I also have experience in processing remote sensing data using machine learning and AI techniques.

Throughout my 12+ years of experience, I have worked on numerous projects related to environmental assessment. These include monitoring land surface deformation, assessing air pollution using spaceborne remote sensing and weather data, and using deep-learning techniques for classifying glacial lakes in multispectral imagery.

I hold a degree in Electronics Engineering, and I was awarded the President’s Gold Medal for securing the highest CGPA in my cohort. I also won an Erasmus Mundus Scholarship to pursue a two-year graduate program in Information and Communication Technologies. From 2013 onwards, I pursued academic research as a doctoral candidate and postdoc at the Swiss Federal Institute of Technology, ETH Zurich, Switzerland.

Currently, I serve as an Assistant Professor of Computer Sciences in Lahore, Pakistan. I have conducted research in the past, but I am now also taking up development work. I am committed to providing high-quality solutions and delivering results that exceed my clients' expectations.

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Delivery time starts when Muhammad Adnan receives requirements from you.

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Revisions may occur after the delivery date.

First Meeting

A 15-30 min first meeting is requested to ensure that the problem statement is well communicated, so that subsequent efforts are channelized in the right direction.

Scheduling the lectures

The client communicates a preferred set of dates/times for the lectures. Dates/time-slots are finalized with mutual agreement.

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