Value Added Course : Geospatial Data Analysis using Python (PU/GEOG/VACC-1/24-25)   Date: 23/04/2025

NOTICE FOR SHORTLISTED PARTICIPANTS

Course Name:Geospatial Data Analysis using Python (PU/GEOG/VACC-1/24-25)

Following are the shortlisted participants for the Value-Added Certificate Course titled Geospatial Data Analysis using Python (PU/GEOG/VACC-1/24-25) being offered by the Department of Geography, Presidency University.

Serial No.

Application No.

Name

Course Name

Amount to be Paid (Rs.)

1

PU/GEOG/VACC-1/24-25/01

Sk Asraful Alam

Geospatial Data Analysis using Python

10,303/-

2

PU/GEOG/VACC-1/24-25/02

Suraj Das

Geospatial Data Analysis using Python

10,303/

3

PU/GEOG/VACC-1/24-25/03

Rameswar Mukherjee

Geospatial Data Analysis using Python

10,303/

4

PU/GEOG/VACC-1/24-25/04

Tiyasha Das

Geospatial Data Analysis using Python

10,303/

5

PU/GEOG/VACC-1/24-25/05

Mou Garai

Geospatial Data Analysis using Python

10,303/

6

PU/GEOG/VACC-1/24-25/06

Braj Kishor Dwivedi

Geospatial Data Analysis using Python

10,303/

7

PU/GEOG/VACC-1/24-25/07

Alivia Roy

Geospatial Data Analysis using Python

10,303/

8

PU/GEOG/VACC-1/24-25/08

Pallab Mahata

Geospatial Data Analysis using Python

10,303/

9

PU/GEOG/VACC-1/24-25/09

Chaitali Roy

Geospatial Data Analysis using Python

10,303/

10

PU/GEOG/VACC-1/24-25/10

Tahsin Jabeen

Geospatial Data Analysis using Python

10,303/

11

PU/GEOG/VACC-1/24-25/11

Priyanka Dasgupta

Geospatial Data Analysis using Python

10,303/

12

PU/GEOG/VACC-1/24-25/12

Saheli Bhattacherjee

Geospatial Data Analysis using Python

10,303/

13

PU/GEOG/VACC-1/24-25/13

Aftab Alam Ansari

Geospatial Data Analysis using Python

10,303/

14

PU/GEOG/VACC-1/24-25/14

Kaniz Fatma

Geospatial Data Analysis using Python

10,303/

15

PU/GEOG/VACC-1/24-25/15

Anushka Ghoshal

Geospatial Data Analysis using Python

10,303/

16

PU/GEOG/VACC-1/24-25/16

Nilanjana Ghosh

Geospatial Data Analysis using Python

10,303/

17

PU/GEOG/VACC-1/24-25/17

Gaurav Paul

Geospatial Data Analysis using Python

10,303/

18

PU/GEOG/VACC-1/24-25/18

Subham Pramanick

Geospatial Data Analysis using Python

10,303/

19

PU/GEOG/VACC-1/24-25/19

Shrinava Chakraborty

Geospatial Data Analysis using Python

10,303/

20

PU/GEOG/VACC-1/24-25/20

Basanta Bentkari

Geospatial Data Analysis using Python

10,303/

21

PU/GEOG/VACC-1/24-25/21

Chandrayee Sen Majumder

Geospatial Data Analysis using Python

10,303/

22

PU/GEOG/VACC-1/24-25/22

Ishika Gantait

Geospatial Data Analysis using Python

10,303/

23

PU/GEOG/VACC-1/24-25/23

Suvashree Das

Geospatial Data Analysis using Python

10,303/

24

PU/GEOG/VACC-1/24-25/24

Anindita Das

Geospatial Data Analysis using Python

10,303/

25

PU/GEOG/VACC-1/24-25/25

Rajesh Maji

Geospatial Data Analysis using Python

10,303/

26

PU/GEOG/VACC-1/24-25/26

Maitrayee Das

Geospatial Data Analysis using Python

10,303/

27

PU/GEOG/VACC-1/24-25/27

Ishani Chakraborty

Geospatial Data Analysis using Python

10,303/

28

PU/GEOG/VACC-1/24-25/28

Santu Manna

Geospatial Data Analysis using Python

10,303/

29

PU/GEOG/VACC-1/24-25/29

Tirtharaj Sarkar

Geospatial Data Analysis using Python

10,303/

30

PU/GEOG/VACC-1/24-25/30

Arma Alam

Geospatial Data Analysis using Python

10,303/

31

PU/GEOG/VACC-1/24-25/31

Somasis Sengupta

Geospatial Data Analysis using Python

10,303/

Kindly use the application number at the time of payment for your ValueAdded Certificate Course.

The following link for payment will be available from 24/04/2025 to 29/04/2025, midnight, IST.

Payment Link

Post successful payment, participants shall obtain their enrolment number and details of commencement of the course.

VAC Course Commencement Date: 5 th May, 2025

VAC Course End Date (tentative): 23 rd June, 2025

Steps to follow during the Payment Process through SBI Collect Link:

1. Choose Payment Category: VAC-AL 25

2. Enter Application Number: (Get your application number from the notice for payment)

And press Submit.

3. Enter the details and click submit and pay your fees. Note down the Payment reference number and keep the print out of the receipt for future reference if required.

Sd/-
Prof. Soumendu Chatterjee
Dr. Priyank Pravin Patel
(Course co-ordinators)

Value Added Course : Geospatial Data Analysis using Python (PU/GEOG/VACC-1/24-25)   Date: 17/03/2025

Title:Geospatial Data Analysis using Python

Course Code: PU/GEOG/VACC-1/24-25

Course Type: Certificate

Commencement Date and Time: 21st April, 2025 (from 5 pm to 7 pm)

(Date and Time are subject to change and shall be notified)

Date of Completion: Tentatively by the last week of June 2025

Duration of the Course: 30 (Thirty) Hours

Schedule of the Classes: 1 hour/class; 2 classes/day (From 5 pm to 7 pm); 2 days/week

Mode of Course: Offline

Medium of Instruction: English

Course Fees: 10,303/- (Rupees Ten Thousand Three Hundred and Three only)

(Mode of Payment will be intimated subsequently to the shortlisted candidates)

Eligibility (Who can apply): Graduate in any discipline of Earth / Physical / Social Sciences

Course Coordinator Name: Dr. Priyank Pravin Patel and Prof. Soumendu Chatterjee

Email IDs of Course Coordinators:

priyank.geog@presiuniv.ac.in / soumendu.geog@presiuniv.ac.in

Course Objective:

Machine learning (ML), an important sub-field of Artificial Intelligence (AI), finds multifarious applications in Earth Systems Science. The high performance of ML techniques in handling classification and regression problems and for pattern recognition has drawn tremendous academic interests among the researchers and professionals. This course aims at imparting live training on basics of machine learning (ML) techniques and their implementation in Python using data related to the Earth.

Learning Outcome:

· Basics of Python programming and using libraries- numpy, pandas, skleran etc. for handing data.

· Build, train and evaluate ML models from data for prediction and classification purposes.

· Expertise in supervised ML algorithms including linear and logistic regression, decision tree, random forest, support vector, KNN and PCA.

Module content

UNIT 1: PYTHON BASICS I

1.1 Programming Methodology: Algorithm and Flowchart

1.2 Introduction to Python: Installation of Python environment (Jupyter Notebook in ANACONDA); Variables and types, Operators and Operands, Statements, Input and Output, Modules and built-in functions; Conditional and Looping constructs (if-else, while and for loops with range method for simple and nested cases); Variable Scope; User defined functions.

1.3 Strings: Traversing, slicing, operations, methods and built-in functions, regular expression and pattern matching

1.4 Lists: Creating list, Accessing, traversing, adding, updating arid deleting elements, list functions and methods, list as an argument, matrix implementation.

UNIT 2: PYTHON BASICS II

2.1 Dictionaries: Key-value pair, Creating, initializing, accessing, traversing elements, dynamic allocation, appending value, merging dictionaries, removing items, dictionary functions and methods

Tuple: Creating tuple, appending element, assignment, slicing, tuple functions and methods.

2.2 Object Oriented Programming: Concept, Class and Object creation, Object attributes and class attributes, Methods

2.3 Numpy: Importing csv file, handling arrays, implementing mathematical operations on arrays using numpy.

2.4 Pandas: Accessing data, manipulating data in dataframe

2.5 Plotting of data using matplotlib

UNIT 3: BASIC SUPERVISED MACHINE LEARNING ALGORITHMS AND THEIR CODING I

3.1 Types of Machine Learning; Steps in supervised machine learning

3.2 Bivariate and Multiple Linear Regression: Mathematical background; Score, Cost function, gradient descent and learning rate; Coding Linear regression using sklearn.

3.3 Binary and Multi-class Logistic Regression: Cost function, gradient descent, classification measures and their coding in Python.

3.4 Decision Tree: Concept; Building Decision Tree for discrete and continuous data, accuracy assessment; Implementation in sklearn and pruning.

UNIT 4: BASIC SUPERVISED MACHIHE LEARNING ALGORITHMS AND THEIR CODING II

4.1 Random Forest: Concept and implementation in python.

4.2 K-Nearest Neighbour: KNN algorithm and its implementation in python.

4.3 Support Vector Machine: SVM algorithm; SVM cost function and accuracy; Non- linear decision boundary and kernels; Multi-class classification; Implementation in python.

4.4 Principal Component Analysis: Mathematical background; Application of PCA on 2D and 3D data; Coding for PCA; Application in Image classification.

Resource Persons:

Eminent professionals from relevant fields will take the classes

General terms and conditions:

· The aspiring candidates must fill in all the mandatory fields given in the Google form within the time frame from 17th March 2025 to 31st March 2025 mid-night (24.00 hrs).

· Once submitted, the candidates cannot change their responses in the form.

· The selection of candidates will be done on a first-come, first-serve basis.

· The maximum number of candidates will be decided based on the availability of resources; however, the minimum number will be 15 (fifteen).

· Selected participants will be informed about the payment method subsequently via email along with their Application Number.

· On successful submission of Course fees, participants will be notified about their enrolment number and commencement details of the course.

  • Once a candidate gets enrolled, the fees will be non-refundable.

Sd/-
Dr. Priyank Pravin Patel
Prof. Soumendu Chatterjee
Course Coordinators

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