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Self Learn

Become a COVID-19 analyst and contribute towards our mission!

By following these easy steps below, you will be able to set yourself up to contribute more towards some groundbreaking studies in the research of COVID-19

Track 1: Epidemiology of COVID19​

​Introduction to Data Analysis and its Tools
​

Learning Outcome :

  • Use R/Python to clean, analyze, and visualize data.
  • Navigate the entire data science pipeline from data acquisition to publication.
  • Use GitHub to manage data science projects.
  • Perform regression analysis, least squares and inference using regression models.
    • ​

Introduction to Exploratory Data Analysis and Evaluation
​

Learning Outcome :

  • Understand analytic graphics and the base plotting system in R/Python
  • Use advanced graphing systems such as the Lattice system
  • Make graphical displays of very high dimensional data
    • ​
  • Apply cluster analysis techniques to locate patterns in data
​

Introduction to Epidemiology for Viral Infections
​

Learning Outcome :

  • Become familiar with the epidemiological tool set
  • Measure the health of populations
  • Collect and analyze public health surveillance data
    • ​
  • Investigate disease outbreaks and epidemics
​
Literature Review for Identification of Open-Question
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
  • Describe methods to critically assess the risk of bias of clinical trials
    • ​
  • Describe and interpret the results of meta-analysis
​

Independent Research for Tackling Open Questions
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
  • Describe methods to critically assess the risk of bias of clinical trials
    • ​
  • Describe and interpret the results of meta-analysis
​

Track 2: Genomics for SARS-nCoV2019 ​

​Introduction to tools of Machine Learning
​

Learning Outcome :

  • Describe the core differences in analysis enabled by regression, classification, and clustering.
  • Select the appropriate machine learning task for a potential application.
  • Represent your data as features to serve as input to machine learning models.
  • Assess the model quality in terms of relevant error metrics for each task.
  • Utilize a dataset to fit a model to analyze new data.
  • Build an end-to-end application that uses machine learning at its core.

​Introduction to Disease Genomics
​

Learning Outcome :

  • Recognize patterns of Mendelian inheritance of monogenic diseases
  • Understand and describe principles and methods of gene mapping
  • Describe the main steps and principles of genome-wide association studies (GWAS)
  • Give examples of modern technologies that are currently used to find variants underlying human diseases
  • Describe the approaches to finding causative variants underlying complex disorders
  • Describe the possibilities and areas of application of genetic findings.

Introduction to Computational Genomics
​

Learning Outcome :

  • Use the tools that are available from the Galaxy Project
  • Implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets
  • Manage and analyze directories, files, and large sets of genomic data
  • Use tools from the Bioconductor project to perform analysis of genomic data

​Literature Review for Identification of Sub-Domain
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
    • ​
  • Describe methods to critically assess the risk of bias of clinical trials
  • Describe and interpret the results of meta-analyses

Independent Research for Tackling Open Questions
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
    • ​
  • Describe methods to critically assess the risk of bias of clinical trials
  • Describe and interpret the results of meta-analyses

Track 3: Drug Engineering for
SARS-nCoV2019​

Introduction to tools of Deep Learning
​

Learning Outcome :

  • Understand the major technology trends driving Deep Learning
  • Be able to build, train and apply fully connected deep neural networks
  • Know how to implement efficient (vectorized) neural networks
  • Understand the key parameters in a neural network's architecture
Introduction to Drug Engineering Principles
​

Learning Outcome :

  • Understand the Principle of Pharmacokinetics: Bioavailability, Elimination, Therapeutic index
  • Describe about Biopolymers: Natural and Synthetic, biocompatibility, Biodegradation, commonly used biopolymers
  • Understand the key implant associated infections, Route specific delivery: Oral, Subcutaneous, Intramuscular, transdermal, inhalation, intravenous
  • Comprehend the Role of Vaccines, Cancer vaccines, Cell and gene delivery, Smart responsive drug delivery, Targeted drug delivery, Nanotoxicology and market translation
​Introduction to Industry-Level Tools for Drug Engineering
​

Learning Outcome :

  • Understand the use of AlphaFold using PipelineAI
  • Implement Docker alongside AlphaFold functionality
  • Implement Protein Docking mechanisms using Industry-Level tools

​Literature Review for Identification of Open-Question
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
  • Describe methods to critically assess the risk of bias of clinical trials
  • Describe and interpret the results of meta-analyses

​Independent Research for Tackling Open Questions
​

Learning Outcome :

  • Conduct research on identified research gap from literature review
  • Create an outline to plan out the research
  • Write a short annotated bibliography to help you evaluate sources
  • Write a comprehensive research paper
  • Use source material correctly with MLA format

Track 4: Vaccinology and Reverse Vaccinology for SARS-nCoV2019​

Introduction to Data Analysis and its Tools
​

Learning Outcome :

  • Use R/Python to clean, analyze, and visualize data.
  • Navigate the entire data science pipeline from data acquisition to publication.
  • Use GitHub to manage data science projects.
  • Perform regression analysis, least squares and inference using regression models.
Introduction to Vaccinology Approaches
​

Learning Outcome :

  • Implementation of molecular visualization and animation in UCSF Chimera
  • Able to find specific virus-related proteins in the PDB archive
  • Understand the basics of T-cell epitopes and their roles in vaccine development
  • Differentiate between different types of vaccine bases

Introduction to Vaccinology Databases
​

Learning Outcome :

  • Able to search and analyse human viral pathogens using publicly available, NIAID-sponsored bioinformatic databases like ViPR etc.
  • Visualize pathogen entries and information from NIAID-sponsored databases like ViPR etc Able to use tools and API listed under IEDB (Immune Epitope Database)
  • Analyze experimental data on antibodies in humans in content of disease, allergy, autoimmunity and transplantation.

​Literature Review for Identification of Open-Question
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
  • Describe methods to critically assess the risk of bias of clinical trials
  • Describe and interpret the results of meta-analyses

Independent Research for Tackling Open Questions
​

Learning Outcome :

  • Conduct research on identified research gap from literature review
  • Create an outline to plan out the research
  • Write a short annotated bibliography to help you evaluate sources
  • Write a comprehensive research paper
  • Use source material correctly with MLA format

Track 5: Big Data for COVID19

Introduction to Big Data Principles
​

Learning Outcome :

  • Processing big data at scale for analytics and machine learning
  • Creating streaming data pipelines and dashboards
  • Understand the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting.
  • Able to use the architectural components and programming models used for scalable big data analysis.
Introduction to Data Analysis and its Tools
​

Learning Outcome :

  • Use R/Python to clean, analyze, and visualize data.
  • Navigate the entire data science pipeline from data acquisition to publication.
  • Use GitHub to manage data science projects.
  • Perform regression analysis, least squares and inference using regression models.

Introduction to COVID19 databases
​

Learning Outcome :

  • Importing COVID19 dataset and preparing it for the analysis by dropping columns and aggregating rows.
  • Deciding on and calculating a good measure for analysis.
  • Merging two datasets and finding correlations among the data.
  • Visualizing the analysis results using Seaborn

Literature Review for Identification of Open-Question
​

Learning Outcome :

  • Describe the steps in conducting a systematic review
  • Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
  • Describe the process used to collect and extract data from reports of clinical trials
  • Describe methods to critically assess the risk of bias of clinical trials
  • Describe and interpret the results of meta-analyses

Collaborative Research with Others on Open-Questions
​

Learning Outcome :

  • Conduct research on identified research gap from literature review
  • Create an outline to plan out the research
  • Write a short annotated bibliography to help you evaluate sources
  • Write a comprehensive research paper
  • Use source material correctly with MLA format


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