Upcoming CoursesPython Programming with Industry perspective
Prospective Experts:
Principal Coordinator :
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02-06 December 2019 Local Coordinator: |
Course contents:
Introduction & basics of to Python Programming:History of Python, Installing Python, Executing Python Programs, Internal Working of Python, Python Implementations.
Python Character Set, Token, Python Core Data Type, print() function, Assigning Value to Variable, input() function, eval() function, Formatting Number and Strings, Operators
and Expressions.
Decision Statements; Loop Control Statements; Functions, Strings:Boolean Type, Boolean Operators, Using Number and Strings with Boolean Operators, Decision Making Statements and Conditional Expressions While loop, range( ) Function, For Loop, Nested Loops, Break Statement, Continue Statement
Syntax and Basics of a Function, Use of a function, Parameters and Arguments, Local and Global Scope Scope of a Variable, return statement and Recursive Functions.
str class, Inbuilt functions for String, index[] operator, traversal of String, String operators, String Operations
Lists and Dictionaries; Tuples and Sets; File Handling; Pandas Creating Lists, Basic list operators, Slicing, Inbuilt functions for Lists, List operator, List Methods, Splitting, Need of Dictionary, Creating a Dictionary , Adding and Replacing Values, Retrieving Values ; Deleting Items and Traversing Dictionaries.
Tuples and Sets: Creating Tuples; Tuple () Function, Inbuilt Functions for Tuples, Indexing and Slicing; Operations on Tuples; Traverse Tuples from a List, Set operators; Set class.
Object-Oriented Programming: Classes and objects, methods, Operator Overloading, Inheritance, super () and Method Overriding.
File Handling: Need of File Handling, Reading/Writing Text and Numbers to/from a File; Directories on a disk.
Pandas: Using Pandas, the python data analysis library and data frames
Data Handling and Use Cases: RE Pattern Matching, Parsing Data, Introduction to Regression , Types of Regression ,
Use Cases , Exploratory data analysis , Correlation Matrix , Visualization using Matplotlib and Implementing linear regression.
Machine Learning:Machine Learning - Algorithm, Algorithms - Random forest , Super vector Machine , Random Forest , Build your own model in python and Comparison between random
forest and decision tree.
Registration Fee and Accommodation:
No Registration fee is charged for attending this programme planned at any designated academies/Remote centres. However, candidate should submit a Demand Draft/ CBS-Cheque of Rs.1000/- along with application form and the same will be handed over to the participant on the last day of the training. Certificate for participation as well as for Satisfactory performance will be given to the participants subject to fulfillment of attending all sessions, submission of assignments and clearing the test(s).
Boarding and Lodging at Hostels/Guest House will be provided at free of cost only at Identified E&ICT Academies. For details please refer to respective Academy websites. At identified Remote centres only working lunch and snacks will be provided. No Travel Allowance will be paid to the participants.
Core Team Members, E&ICT Academy:
Prof Aparajita Ojha
Email: aojha@iiitdmj.ac.in
Prof. Vijay Kumar Gupta
Email: vkgupta@iiitdmj.ac.in
Prof. P.N. Kondekar
Email: pnkondekar@iiitdmj.ac.in
Dr. Atul Gupta
Email: atul@iiitdmj.ac.in
Dr. Prashant Kumar Jain
Email: pkjain@iiitdmj.ac.in
Website: ict.iiitdmj.ac.in
Contact us :
Maj Neha Rawat (Retd) : +9893443284
Business Manager,
Electronics and ICT Academy
PDPM Indian Institute of Information Technology,
Design and Manufacturing, Jabalpur,
Dumna Airport Road, Jabalpur 482005
Email: neharawat@iiitdmj.ac.in
Website: ict.iiitdmj.ac.in