This document outlines the course content for an Information Technology Solutions specialization. The specialization covers Python programming fundamentals through 6 modules, including Python basics, data structures, programming concepts, working with data, and model development and evaluation. It also covers Biopython with Machine Learning through 17 modules, including sequence analysis, annotation, alignment, BLAST, databases, structure analysis, population genetics, phylogenetics, motifs, genome analysis, plotting, and machine learning techniques. Completing all modules and passing a performance evaluation earns a certificate of completion.

1. Information Technology Solutions
The course covers the
concepts and tools you need
throughout the entire pipeline.
In the final project, you’ll apply
the skills learned by building a
product using real-world data.
At completion, students will
receive completion certificate.
PERFORMANCE
EVALUATION
Every specialization includes a
hands-on performance
evaluation. You’ll need to
successfully clear the
evaluation process to complete
the specialization.
EARN A CERTIFICATE
When you finish every modules
and complete the performance
evaluation, you’ll earn a
certificate.
PYTHON PROGRAMMING
Module 1 - Python Basics
 Your first program
 Types
 Expressions and Variables
 String Operations
Module 2 - Python Data Structures
 Lists and Tuples
 Sets
 Dictionaries
Module 3 - Python Programming Fundamentals
 Conditions and Branching
 Loops
 Functions
 Objects and Classes
Module 4 - Working with Data in Python
 Reading files with open
 Writing files with open
 Loading data with Pandas
 Working with and Saving data with
Pandas
Module 5 - Model Development
 Simple and Multiple Linear
Regression
 Model
Evaluation Using Visualization
 Polynomial Regression and
Pipelines
 R-squared and MSE for In-Sample
Evaluation
 Prediction and Decision Making
Module 6 - Model Evaluation
 Model Evaluation
 Over-fitting, Under-fitting and
Model Selection
 Ridge Regression
 Grid Search
 Model Refinement
BIOPYTHON WITH MACHINE LEARNING
Module 1 – Introduction and Installation
 What is Bio-Python
 Understanding the Packages
 Features and Advantages
 Installing Bio-Python
 Creating Simple Application
 Parsing Sequence file formats
o Simple FASTA Parsing
o Simple GenBank Parsing
 Connecting with biological databases
Module 2 - Sequence
 Introduction
 Concatenating
 Changing Case
 Nucleotide sequences and (reverse)
complements
 Transcription
 Translation
 Comparing Sequence Objects
 Mutable sequence objects and Unknown
sequence objects
Module 3 – Sequence Annotation
 SeqRecord object
 Creating a SeqRecord
 Feature, Location and Position objects
 Comparison
 Format methods
 FASTA
 GenBank
Module 4 – Sequence Input / Output
 Parsing or Reading Sequences
 Iterating over the records in a sequence file
 Parsing sequences
 Sequence Files as Dictionaries
 Compressed file Indexing
 Sequence file format conversion
 Low-level FASTA and FASTQ Parsers
Module 5 – Sequence Alignment
 Multiple Alignment
 Ambiguous Alignments
 Getting Alignment objects as formatted
strings
 Manipulating Alignments
 Alignments as arrays
 Alignment Tools
Bio Python

2. B.Tech. / B.Sc. / M.Tech. / M.Sc.
in Biological Science, Computer
science, Pharmacy, Agriculture,
Biotechnology, Molecular biology,
Medicine, clinical research and
other relevant Qualification in the
life sciences areas are eligible
60 HOURS
Technical Support
Installation and Setup
Maintenance
Application Support
Hardware Support
Guaranteed Warranty
For more information or services
please visit us on the Web at:
Object Automation Software Solutions Pvt. Ltd.
Module 6 – BLAST
 Introduction
 Running BLAST over Internet
 Standalone NCBI BLAST+
 Dealing with RPS-BLAST
 Parsing BLAST Result
 SearchIO object model
 Hit & HSP
 HSP Fragment
Module 7 – Entrez Database
 Introduction
 Connecting the Database
 Searching and Fetching Records
Module 8 – Swiss-Port and ExPASy
 Parsing Swiss-Port files
 Accessing the ExPASy server
Module 9 – PDM Module
 Crystal structure Files
 mmCIF Files
 MMTF format
 PDB File
 PQR File
 Model
 Chain
 Residue
 Atom
 Analysing Structures
Module 10 – Population Genetics
 Working with GenePop
Module 11 – Phylogenetics with BioPhylo
 Working with trees
 Using Tree and Clade objects
 Traversal methods
 Information methods
 Modification methods
Module 12 – Motif using Biomotifs
 Motif creating
 JASPAR
 MEME
 TRANSFAC
 MEME
Module 13 –Genome Analysis
 Genome Diagram
 Chromosomes
Module 14 – Plotting
 Line plot
 Bar chart
 Histogram
 GC Percentage in Sequence
Module 15 – Machine Learning
 Introduction
 Classification Techniques
 Clustering Aanalysis
o Hierarchical clustering
o K-Nearest
 Principal Component Analysis
Module 16 – Phenotype Microarray
 Introduction
 Parsing data
 Manipulating data
Module 17 – Framework
 Running the tests using TOX
 Parser Design
 Substitution Matrices