Model Curriculum - nsdcindia.org · • Use statistical tools such as statistical integrated...

Bioinformatics Scientist

Model Curriculum


SECTOR: SUB-SECTOR: OCCUPATION:

REF ID: NSQF LEVEL:

LIFE SCIENCES CONTRACT RESEARCH BIOINFORMATICS LFS/Q3903, V1.0 5

(Elective-Model Risk Assessment / Model Business Performance)


Complying to National Occupational Standards of Job Role/ Qualification Pack: ‘Bioinformatics Scientist’ QP No. ‘LFS/ Q3903,V1.0

NSQF Level 5’

Date of Issuance: Aug 16th

, 2019

Valid up to: Aug 01st

, 2023

* Valid up to the next review date of the Qualification Pack

Authorized Signatory (Life Sciences Sector Skill Development Council)


TABLE OF CONTENTS

1. Curriculum 01 2. Trainer Prerequisites 11 3. Assessment Criteria 12

Bioinformatics Scientist 1

Bioinformatics Scientist CURRICULUM / SYLLABUS This program is aimed at training candidates for the job of a “Bioinformatics Scientist”, in the “Life Sciences” Sector/Industry and aims at building the following key competencies amongst the learner

Program Name Bioinformatics Scientist

Qualification Pack Name & Reference ID. ID Bioinformatics Scientist LFS/Q3903, V1.0

Version No. 1.0 Version Update Date 16-08-2019

Pre-requisites to Training

Post Graduate in Life Sciences subjects/ Bioinformatics/ Biotechnology/ Post Graduate Engineer in Biotechnology/ bioinformatics/ Bioengineering/ biomedical/computational science

Training Outcomes After completing this program, participants will be able to: Compulsory: • Outline industry ecosystem, regulations and ethical practice to enable

him/herself for establishing the industry standards in his/her performance • Explain basic and advanced statistical concepts used for data sciences

such as Bayesian concepts, Conditional probability, Prior and Posterior probabilities, etc.

• Apply different methods to import, pre-process and explore data such as importing data from different formats, cleaning data, and summarizing data, dimension reduction and defining correlations.

• Conduct research and design on different algorithms for a variety of data formats such as graphs or strings.

• Design complex algorithms such as deep neural networks, convolutional neural networks and recurrent neural networks for use cases such as image and speech recognition.

• Use statistical tools such as statistical integrated development environments (IDEs), or software packages, libraries and frameworks for importing, pre-processing, exploring data and designing models.

• Explain the concepts of biology, genomics, and proteomics for visualization and evaluation.

• Contribute in research publication, innovative and scientific solutions to complicated problems.

• Assess the most appropriate way to report data such as by identifying the right audience, creating a narrative and selecting suitable visualizations

• Plan their schedules and timelines based on the nature of work. • Demonstrate how to communicate and work effectively with colleagues. • Maintain customer relationships and client satisfaction. Electives: • Predict model risk by identifying the risk factors and define mitigation

measures • Apply different methods to optimize model performance such as mini-batch

gradient descent, RMSprop and Adam.


This course encompasses 14 out of 14 National Occupational Standards (NOS), 2 out of 2 Electives of “Bioinformatics Scientist” Qualification Pack issued by “Life Sciences Sector Skill Development Council”. Sr. No. Module Key Learning Outcomes Equipment Required

1 Orientation for Bioinformatics Occupation Theory Duration (hh:mm) 10:00 Practical Duration (hh:mm) 00:00 Corresponding NOS Code Bridge Module

• Explain the life sciences industry and bioinformatics occupation

• Explain the organizational structure and employment benefits in the life sciences Industry

• Explain the regulatory framework, rules and regulations applicable for bioinformatics in the life sciences Industry

• Explain the role of a Bioinformatics Scientist and required skills and knowledge (as per Qualification Pack) and its career path

Computer system, LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Internet with Wi-Fi

2 Importing Data Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8101

• Identify the various commonly known open source and paid data sources

• Discuss the uses and characteristics of different open source and paid data sources

• Develop knowledge on capturing various types of data such as enterprise data, consumer data, etc.

• Demonstrate how to read data from various file formats and import it

• Describe the purpose of metadata • Develop knowledge on how to organize and

map metadata as per the needs of the analysis

• Use different tools to import data from both public and private databases or data stores and store it in datasets or data frames

• Distinguish between different types of data such as numerical, categorical, etc.

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi

3 Preprocessing Data Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8102

• Explain the difference between unprocessed and processed data

• Describe the various anomalies that may be found in unprocessed data

• Comprehend the impact of unprocessed data on subsequent analytical operations

• Describe the properties of different tools that can be used to pre-process data

• Analyze unprocessed data to discover anomalies such as missing values, incorrect data types, etc.

• Explain different techniques and functions to clean unprocessed data including removing missing values, transforming incorrect data types, etc.



Sr. No. Module Key Learning Outcomes Equipment Required

• Describe different approaches to normalize datasets such as feature scaling etc.

4 Exploring Data Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8103

• Explain the limitations found in exploring data of different types such as numerical or categorical

• Describe the properties of various tools that can be used to explore data

• Select the right tool to explore the data based on its characteristics

• Apply different functions used to summarize data including mean, median, mode, range, variance, frequency

• Apply different approaches to perform dimension reduction on a dataset such as Principal Component Analysis, Linear Discriminant Analysis or Non-negative Matrix Factorization

• Use graphical techniques such as scatterplots or clustering to evaluate correlations between different data points

• Demonstrate the principles of hypothesis testing to draw inferences from the results of a data analysis

• Categorize the various types of prescriptive actions that can be recommended from the results of data analysis)


5 Data Structures and Algorithms Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8104

• Distinguish between different data structures such as arrays, linked lists, stacks, queues, and trees

• Comprehend the properties • of various data structures such as arrays,

linked lists, stacks, queues, and trees • Compare the differences in adding,

removing and editing data from different types of data structures

• Use advanced concepts such as dynamic arrays, priority queues, disjoint sets, and binary search trees to different types of problems

• Use hash tables to store and modify sets of objects and mappings from one type of objects to another

• Distinguish between the pros and cons of efficient and naïve algorithms

• Apply greedy algorithms to different use cases

• Use the divide and conquer technique to solve problems involving large databases

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi Development Software: Linux OS, Python/Perl/ R and C++/Java

6 Graph Algorithms Theory Duration (hh:mm) 15:00

• Apply the basics behind undirected graphs such as representing and exploring graphs, pre-visit and Post-visit orderings, etc.

• Apply the basics behind directed graphs such as acyclic graphs, topological sorting, and computing strongly connected

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with



Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8104

• components • Use different algorithms for decomposing

graphs into parts • Use different algorithms for finding shortest

paths in graphs such as breadth-first search, shortest-path-tree, Dijkstra’s algorithm, and Bellman-Ford algorithm

• Use greedy algorithms such as Kruskal’s algorithm and Prim’s algorithm to solve minimum spanning tree problems

Computer system/Laptop, Internet with Wi-Fi Development Software: Linux OS, Python/Perl/ R and C++/Java

7 String Algorithm Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8104

• Use brute force approaches for pattern matching

• Comprehend the concepts behind algorithms such as suffix trees that are used for pattern matching

• Use algorithms such as suffix arrays and Burrows-Wheeler Transform for approximate pattern matching

• Use algorithms such as Knutt-Morris-Pratt for exact pattern matching

• Apply different techniques to construct suffix trees and arrays


8 Neutral Networks Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 35:00 Corresponding NOS Code SSC/N8104

• Distinguish between the properties of different types of neural networks and their applications

• Build shallow and deep neural networks using techniques such as forward propagation and back propagation

• Apply the foundational layers of convolutional neural networks such as pooling and convolutions and stack them properly in a deep network to solve multi-class image classification problems

• Build convolutional neural networks and apply it to object detection problems

• Distinguish between different types of recurrent neural networks and commonly used variants such as GRUs and LSTMs

• Use word vector representations and embedding layers to train recurrent neural networks

• Apply attention model intuition and trigger word detection to speech recognition problems


9 Programming for Data Science Theory Duration (hh:mm) 15:00 Practical Duration

• Distinguish between the limitations of different programming, command line or scripting languages to develop machine learning algorithms

• Select the most suitable programming languages to develop or optimize the statistical machine learning algorithm

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi, Latest version of statistical



(hh:mm) 35:00 Corresponding NOS Code SSC/N8104

• Use object-oriented programming concepts such as abstraction, encapsulation, modularity, etc. to write user-defined functions and classes

• Apply dynamic programming concepts to solve complex optimization problems

• Use the streaming model to compute real-time or large amounts of data that cannot be stored in the memory

software packages and IDEs

10 Application of the Biology concepts for visualization and evaluation Theory Duration (hh:mm) 10:00 Practical Duration (hh:mm) 50:00 Corresponding NOS Code LFS/N3904

• Explain the various applications of biology concepts for evaluation of data

• Recall drug discovery and development process and Quantitative Structure Activity Relationship (QSAR) to deliver the project outcomes

• Demonstrate the use of sequence analysis tools

• Make use of the pre-clinical testing data for evaluation and visualization

• Deliver project outcomes by using biological databases and meeting specified standards of Good Clinical Practices (GCP)

• Explain the outcomes of visualization and evaluation by applying concepts of chemoinformatics, molecular docking, and molecular dynamics

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi, Big Data Analysis Server:10MB Cache, 2.5GHz processor, 1 TB RAM, 28 TB storage, MPI Interconnect: 36 port InfiniBand QDR switch Network Interconnect: 48 port Gigabit Ethernet with 4*10G ports Storage Interconnect: 10G Ethernet via GigE switch Tape Library: 45 TB (1.5*30) storage capacity PFS Storage: 60 TB (upgraded with 180 TB and additional 89.32 TB added to overall 330 TB Storage)



11 Application of the Genomics concepts for visualization and evaluation Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 50:00 Corresponding NOS Code LFS/N3904

• Explain the basic and fundamental concepts of human genetics, disease, and human genomics

• Deliver genome projects and genetic studies or lab diagnostics by using the concepts of molecular genomics

• Deliver project outcome by using appropriate coding and non-coding gene regulatory pathways

• Explain the concepts of precision genomic medicines and Next Generation Sequencing (NGS) in genome projects and genetic studies/lab diagnostics

• Describe the application of biostatistics and data modeling

• Recall the concepts of molecular phylogeny to perform phylogenetic analysis


12 Application of the Proteomics concepts for visualization and evaluation Theory Duration (hh:mm) 15:00 Practical Duration (hh:mm) 60:00 Corresponding NOS Code LFS/N3904

• Demonstrate the use of sequence analysis tools

• Perform proteomics research by applying fundamental concepts of proteomics like gel based proteome investigations, sequence-based technologies, protein sequence determination, protein engineering techniques and top down proteomics

• Explain the molecular differences by using molecular markers and transcriptomics in bioinformatics projects

• Visualize and analyze the result of the algorithm by applying the concepts of computational chemistry and interaction proteomics


13 Scientific Competence and

• Explain different methods and approaches for solving complex scientific problems

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer,



Research Publication Theory Duration (hh:mm) 20:00 Practical Duration (hh:mm) 50:00 Corresponding NOS Code LFS/N0108

• Identify and select an appropriate method/ approach to solve a complex scientific problem

• Make use of innovative ideas for problem-solving within the boundaries of the legal and regulatory framework

• Perform the assigned project using artificial intelligence tools wherever possible

• Explain the possible ideas/ solutions to the scientific community or panel for validation and feasibility check

• Deliver the scientific presentations to the clients/stakeholders

• Categorize the difference in general report writing versus scientific writing

• Write the scientific research papers and reports of the project independently or in a team

White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi

14 Create Visualizations Theory Duration (hh:mm) 20:00 Practical Duration (hh:mm) 50:00 Corresponding NOS Code SSC/N8108

• Explain how the results of an analysis can contribute to meeting business outcomes

• Categorize the different business outcomes that can be met from the results of a data analysis

• Identify the right target audience to report the results of a data analysis

• Identify the right delivery mode and format to report the results of a data analysis

• Comprehend how content might change based on the target audience

• Summarize the results of data analysis into a clear narrative

• Identify the different visualizations that can be used to support the reporting of analysis results

• Distinguish between the pros and cons of using a specific visualization to represent certain types of data

• Select the right tool to create the visualizations

• Comprehend the importance of version control and uploading the report in a knowledge base

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi, Popular Software Tools BI Software: IBM Cognos Impromptu, Oracle Business Intelligence Enterprise Edition Analytical software tools: IBM SPSS Statistics, SAS, StataCorp Stata, MathWorks Data mining tools: IBM InfoSphere Warehouse, RapidMiner Development Software: Python, R, C++, Java Development Libraries or Platforms: OpenCV, TensorFlow, Theano, Knime, Scikit-learn, Torch, Keras Data Management PaaS: AWS, Hortonworks, Cloudera, Azure Visualization tools: QlikView, Tableau, Power BI

15 Manage Your Work to Meet Requirements Theory Duration (hh:mm)

• Define the scope of work and working within limits of authority

• Summarize the details of the work and work environment

• Recognize the importance of maintaining confidentiality

LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with



08:00 Practical Duration (hh:mm) 22:00 Corresponding NOS Code SSC/N9001

Computer system/Laptop, Internet with Wi-Fi

16 Work Effectively with Collogues Theory Duration (hh:mm) 08:00 Practical Duration (hh:mm) 22:00 Corresponding NOS Code SSC/N9002

• Use different methods and mechanisms for effective communication

• Recognize the importance of working effectively


17 Build and Maintain Relationship at Workplace Theory Duration (hh:mm) 08:00 Practical Duration (hh:mm) 22:00 Corresponding NOS Code SSC/N9006

• Recognize the importance of open and effective communication

• Apply different approaches for conflict management

• Apply different approaches to boost recognition and motivation


18 Build and Maintain Client Satisfaction Theory Duration (hh:mm) 08:00 Practical Duration (hh:mm) 22:00

• List different client requirements and use different approaches to gather them

• Demonstrate how to incorporate client feedback to improve quality of service




Corresponding NOS Code SSC/N9007

19 Persuasive Communication Theory Duration (hh:mm) 08:00 Practical Duration (hh:mm) 22:00 Corresponding NOS Code SSC/N9010

• Identify different requirements and how to adapt to each distinct requirement

• Demonstrate how to use evidence to support arguments


Compulsory NOS Total Duration: Theory Duration 250:00 Practical Duration 650:00

Unique Equipment Required: LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi Development Software: Linux OS, Python/Perl/ R and C++/Java BI Software: IBM Cognos Impromptu, Oracle Business Intelligence Enterprise Edition Analytical software tools: IBM SPSS Statistics, SAS, StataCorp Stata, MathWorks Data mining tools: IBM InfoSphere Warehouse, RapidMiner Development Libraries or Platforms: OpenCV, TensorFlow, Theano, Knime, Scikit-learn, Torch, Keras Data Management PaaS: AWS, Hortonworks, Cloudera, Azure Visualization tools: QlikView, Tableau, Power BI

ELECTIVES (Mandatory to select at least one title) ELECTIVE 1: Model Risk Assessment


1 Identifying Model Risk Theory Duration (hh:mm) 30:00 Practical Duration (hh:mm) 70:00

• Describe the various factors that contribute to algorithmic risk such as flawed data or assumptions, coding errors, insufficient sample sizes

• Comprehend the impact that risk factors might have on the outcome of the algorithmic model

• Compute deviation from expected outcomes of the model by testing it with multiple inputs




Corresponding NOS Code SSC/N8106

• Apply different techniques to estimate the risks involved when the model deviates from expected outcomes

• Categorize the various mitigation measures that can be introduced to counter each type of model risk

• Select suitable checks and mitigation measures to counter the risk

• Translate mitigation measures into a structured corrective action that can be communicated to the rest of the organization

Elective 1 Total Duration: Theory Duration 30:00 Practical Duration 70:00

Unique Equipment Required: LCD Projector & Screen/ LCD Monitor, Mic, Sound System, Laser Pointer, White, White Board Marker, Lab equipped with Computer system/Laptop, Internet with Wi-Fi

Popular Software Tools: Data mining tools: IBM InfoSphere Warehouse, RapidMiner Development Software: Python, R, C++, Java Development Libraries or Platforms: OpenCV, TensorFlow, Theano, Knime, Scikit-learn, Torch, Keras

ELECTIVE 2: Model Business Performance


1 Measuring Model Performance Theory Duration (hh:mm) 30:00 Practical Duration (hh:mm) 70:00 Corresponding NOS Code SSC/N8107

• Categorize the different performance metrics for algorithms based on different business outcomes

• Compute the performance of the model with regards to meeting the specified business outcome

• Describe different hyperparameters that can maximize model performance

• Apply different techniques to identify hyperparameters such as grid search, random search, Bayesian optimization

• Use different optimization algorithms such as mini-batch gradient descent, RMSprop, Adam, etc.

• Apply the concepts behind hyperparameter tuning, batch normalization, etc.




Elective 2 Total Duration Theory Duration 30:00 Practical Duration 70:00



Grand Total

Duration Minimum Duration for the QP=1000 Theory Duration 280:00 Practical Duration 720:00 Maximum Duration for the QP=1100 Theory Duration 310:00 Practical Duration 790:00



Grand Total Course Duration: minimum 1000 hours and maximum 1100 Hours (200 hours of OJT is recommended) (This syllabus/ curriculum has been approved by Life Sciences Sector Skill Development Council)


Trainer Prerequisites for Job role: “Bioinformatics Scientist” mapped to Qualification Pack: “LFS/ Q3903, V1.0”

Sr. No. Area Details 1 Description To deliver accredited training service, mapping to the curriculum detailed above,

in accordance with the Qualification Pack “LFS/Q3903, V1.0”. 2 Personal

Attributes Aptitude for conducting training, and pre/ post work to ensure competent, employable candidates at the end of the training. Strong communication skills, interpersonal skills, ability to work as part of a team; a passion for quality and for developing others; well-organized and focused, eager to learn and keep oneself updated with the latest in the mentioned field.

3 Minimum Educational Qualifications

Post Graduate in Life Sciences subjects/ Bioinformatics/ Biotechnology/ Post Graduate Engineer in Biotechnology/ bioinformatics/ Bio engineering/ bio medical/computer science/computational science

4a Domain Certification

Certified for Job Role: “Bioinformatics Scientist” mapped to QP: “LFS/Q3903, V1.0”. Minimum accepted score is 80% as per LSSSDC guidelines.

4b Platform Certification

Recommended that the trainer is certified for the job role: “Trainer”, mapped to the Qualification Pack: “MEP/Q02601”. Minimum accepted score is 80% as per LSSSDC guidelines.

5 Experience Minimum Four (4) years’ experience in life sciences/Biology Subjects for non-trained and non-qualified talent with graduation education qualification Or Minimum Two (2) years’ experience in life sciences/Biology Subjects for non-trained and non-qualified talent with post-graduation education qualification


Annexure: Assessment Criteria Please refer to the QP PDF for the Assessment Criteria.

Model Curriculum - nsdcindia.org · • Use statistical tools such as statistical integrated...

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