Description
Course Name: Post Graduate Diploma in Data Science
Course Id: PGDDS/Q1001.
Education Qualification: Graduate.
Duration: 370 Hrs (Equivalent to One Year).
Total Credits: 18.
How You will Get Diploma Certificate:
Step 1- Select your Course for Certification.
Step 2- Click on Enroll Now.
Step 3- Proceed to Enroll Now.
Step 4- Fill Your Billing Details and Proceed to Pay.
Step 5- You Will be Redirected to Payment Gateway, Pay Course and Exam Fee by Following Options.
Card(Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.
Step 6- After Payment You will get Study Material Login id and Password on your email id.
Step 7- After Completion of Course Study give Online Examination.
Step 8- After Online Examination you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign).
Step 9- After Certification you will receive Prospect Job Opportunities as per your Interest Area.
Online Examination Detail:
- Duration- 120 minutes.
- No. of Questions- 60. (Multiple Choice Questions).
- 10 Questions from each module, each carry 10 marks.
- Maximum Marks- 600, Passing Marks- 40%.
- There is no negative marking in this module.
| How Students will be Graded: | ||
| S.No. | Marks | Grade |
| 1 | 91-100 | O (Outstanding) |
| 2 | 81-90 | A+ (Excellent) |
| 3 | 71-80 | A (Very Good) |
| 4 | 61-70 | B (Good) |
| 5 | 51-60 | C (Average) |
| 6 | 41-50 | P (Pass) |
| 7 | 0-40 | F (Fail) |
Benefits of Certification:
- Government Authorized Assessment Agency Certification.
- Certificate Valid for Lifetime.
- Lifetime Verification of Certificate.
- Free Job Assistance as per your Interest Area.
Syllabus
Introduction to Data Science: Introduction, integrated development environments (IDE), web integrated development environment, hypothesis testing, basic definitions in graphs, drawing centrality in graphs, page rank.
Data Structures and Algorithms: Introduction, singly linked list, doubly linked list, finding the smallest and largest values in the binary search tree, tree rebalancing, bubble sort, factorial of a number, probability search, counting the number of words in a string, determining the first matching character between two strings.
Data Visualization: Introduction, visualizing data: mapping data onto aesthetics, directory of visualizations, visualizing proportions visualizing nested proportions, handling overlapping points, balance the data and the context, visualizing geospatial data.
Data Wrangling: Data wrangling definition, data formats, basic data wrangling, useful commands, other commands and tools.
Classification and Tabulation of Data: Objectives of classification, advantages of tabulation, types of tables, types of graphs, characteristics for a good statistical average, classification of data, types of tables, general precautions for tabulation.
Data Science and Ethical Issues: Introduction, data cleaning, applications of decisions trees, non-linear regression, multiple linear regression, cross-validation, creating a decision tree, data privacy.
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