Certificate Course in Data Science Using Python & R Language
About the
Program
• Data Science using Python and R is a certification program for engineering graduates, diploma holders, and any other degree graduates with Computer programming backgrounds.
• The course provides comprehensive training to students in the field of Data Science by way of interaction, projects, presentations, industrial visits, practical training, job orientation, and placements.
• It is a perfectly designed certification course for aspiring graduates.
• The certified graduates are known to excel at developing the Data science application, data visualization, and analysis.
• They are expected to select the proper tools and techniques, take the appropriate decisions at the right time, and ensure that the same are implemented properly.
• The course allows you to obtain the knowledge and skills needed to assume Data analytics positions in a wide range of organizations.
Salient features:
• Focused learning in Data Science, Computational Intelligence, and High- Performance Computing.
• Practical application-oriented teaching.
• Hands-on knowledge of Python and R.
• Electives choices to take a deeper knowledge in a specific domain of Data Science.
Program
Objectives
a. This course will give you a practical grounding in the field of Data Science and will take you deeper into a specific domain based on the elective chosen.
b. This course focuses on practical, real-life applications from various segments namely industries, institutes, governments, etc., underpinned by academic theory and research, preparing you for success anywhere in the world.
c. The objective of introducing this course was to enable self-employment and provide skilled professionals in Python and R in the field of Data Science and Big Data Analytics.
d. This course will aid you in the process of acquiring knowledge in the areas of Machine Learning and deep learning, High-Performance Computing platforms, Data Science, Big Data Analytics, Python, and R.
e. A blend of Interaction, Presentations, Projects, Industrial Visits, and Practical Training is used to build the skill set of Data Science students to enable them to produce innovative solutions to problems, apply research skills to business challenges, and communicate effectively.
Program
Outcomes
a. Acquire knowledge about general aspects of Computational Intelligence and deep learning, High-Performance Computing platforms, Data Science, Big Data Analytics, Python, and R.
b. Describe the role of the various technologies in solving the data science applications and also will be able to overcome the limitations.
c. Analyse the various tools and techniques and select the appropriate ones based on the application specifications.
d. Apply the knowledge to design a solution for a problem, select the appropriate tools and techniques based on the application specifications, and implement using Python and R platforms.
e. Evaluate the various tools and techniques and the design models for their performance w.r.t. to the problem under consideration. Compare the results and deduce the suitable results to finalize the solution.
f. Create new methods, tools, and techniques to solve the issues that are not solvable with the available methods.
Career
Prospects
After the completion of Data Science using Python and R course, candidates have various career options to choose from. Candidates who are keen to go for further higher studies can get an edge over others in their selection to masters programs.
Listed below are some of the popular job roles for certified professionals:
• Data Scientist
• Big Data Analyst
• Data Analyst
• Data Engineers
• Database Administrator
• Machine Learning Engineer
• Data Architect
• Statistician
• Business Analyst
• Data and Analytics Manager
• Artificial Intelligence Engineer
Eligibility
Criteria
• B.E./B.Tech. in any branch of engineering.
• Candidates with BSc, MSc, BCA, MCA.
• AMIE in any branch of engineering.
• Diploma in any branch of engineering.
• A background of statistics, Data Structure, Computer Organization and Architecture, Operating Systems, and Database Management Systems will be an added advantage.
Evaluation
Examination will be conducted as multiple choice questions (MCQs)/Assignments/Case study problems
Program Structure
| Sr.
No. |
Module | Total (Hrs/Week) | Total Credits |
Total Credits |
||
| TH | PR | TH | TW | |||
|
1 |
Introduction to Python, Excel & SQL
Python: Introduction to Python and IDEs, Python Basics, Object-Oriented Programming Excel: Excel Fundamentals, Excel For Data Analytics, Data Visualization with Excel, Excel Power Tools, Classification Problems using Excel, Information Measure in Excel, Regression Problems Using Excel… SQL: SQL Basics, Advanced SQL, Deep Dive into User Defined Functions, SQL Optimization, and Performance |
02 |
02 |
02 |
01 |
03 |
|
2 |
Python for Data Sciences: Extract Transform Load, Data Handling with NumPy, Data Manipulation Using Pandas, Data Pre-processing, Data
Visualization etc… |
02 |
02 |
02 |
01 |
03 |
|
3 |
Machine Learning: Introduction to Machine Learning,
Regression, Supervised Learning, Unsupervised Learning, Reinforcement learning, Performance Metrics |
02 |
02 |
02 |
01 |
03 |
|
4 |
Data Science with R Programming: Introduction to R, R Packages, Sorting Data Frame, Matrices, and Vectors, Reading Data from External Files, Generating Plots,
Association Rule Mining, Regression in R, Database Connectivity with R, R Case Studies |
02 |
02 |
02 |
01 |
03 |
|
5 |
Project-based Case studies
Prediction, Analytics, customer churn detection, income classification, bank fraud detection, Understand Bank customer data, sentiment analysis, Understanding Financial data and prediction, Understand Agricultural Data etc |
16 |
08 |
08 |
||
| Total Number of Hours | Grand Total Credits | 20 | ||||
Program Evaluation
| Sr.
No. |
Module |
Examination Scheme | |||
| Marks | |||||
| CA | ESE | TW | Total | ||
| 1 | Introduction to Python, Excel & SQL | 20 | 30 | 25 | 75 |
| 2 | Python for Data Sciences | 20 | 30 | 25 | 75 |
| 3 | Machine Learning | 20 | 30 | 25 | 75 |
| 4 | Data Science with R Programming | 20 | 30 | 25 | 75 |
| 5 | Project-based Case studies | – | – | 50 | 50 |
| Total | 80 | 120 | 150 | 350 | |
* CA: Continuous assessment, ESE: End-semester examination
Fee
Structure
Rs. 20,000/-
Infrastrcture
Enquiry
Form
