Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Data Analysis and Visualization course is a free learning programme of sixteen weeks, where recorded lectures will be provided to the students via an online classroom platform. This course will help students gain exposure in the area through the use of cutting-edge modelling, analysis, and visualization approaches. The Data Analysis and Visualization syllabus includes three core modules. It will focus on practical issues with complicated real-world data and involve numerous case studies as well as hands-on work using the R programming language. The Data Analysis and Visualization training are of intermediate level. The course emphasizes the learning-by-doing exercises assessments.
The Highlights
- Self-paced learning course
- Approx sixteen-week of the duration required
- Taught by the industrial professionals
- Free course
- Offered by the Udacity
- Three modules curriculum
- Intermediate level
Programme Offerings
- Instructor video
- Training
- assessments
- Learning by doing exercises
- quizzes
Courses and Certificate Fees
| Certificate Availability |
|---|
| no |
- There is no fee for Data Analysis and Visualization course
Data Analysis and Visualization fees structure
Particulars | Amount |
Course fee | Nil |
Eligibility Criteria
Work experience
The candidate needs to have programming experience to apply in the Data Analysis and Visualization course by Udacity.
Education
Candidates must have elementary knowledge of the mathematics aspects like calculus, introductory probability, and linear algebra for applying into the Data Analysis and Visualization course. No Machine Learning knowledge is required as a prerequisite.
What you will learn
By the end of the Data Analysis and Visualization programme, the candidate will learn the following concepts -
- Candidates will learn about the dynamics of the R programming language and its usage
- R programming syntax must be taught in the middle of the course
- An enrolled student could understand the data structure and how programming enhances it
- The procedure of data preprocessing will be taught to the candidates
- The models of linear regression would be taught to the students through examples
- Students will learn about the regularization principles in depth
- Data Analysis procedure could be taught at the end as the conclusion.
- Data Visualization is an important element of the curriculum that will be taught in the training
Admission Details
Follow the procedure to enrol in the Data Analysis and Visualization online course -
Step 1: Follow the weblink to know about the course dynamics in detail https://www.udacity.com/course/data-analysis-and-visualization--ud404.
Step 2: The candidate needs to select the option “start the free course”, thereafter sign in using a Google or Facebook account.
Step 3: If candidates don't have an account on Udacity, then they have to register first.
Step 4: The enrollment is done.
Application Details
No application filing option is there for this particular course. The candidate can directly add the course to the classroom with sign-in using the social accounts.
The Syllabus
- The R programming language
- R programming and data structures
- R programming syntax
- Data preprocessing
- Data visualization
- Data processing
- Logistic regression
- Regularization
- Linear regression
Instructors
Georgia Tech Frequently Asked Questions (FAQ's)
Yes, Udacity provides several scholarship options for the candidates. They can opt for the scholarship that goes with their eligibility.
The online course is a completely online curriculum, so no offline interaction will occur.
Candidates have to sign-up first, and then the course will be added to their dashboard sort classroom. This is how the enrollment is done for this course.
The candidate can complete the curriculum within sixteen weeks after enrolment.
The candidate needs to have elementary Mathematics knowledge for applying to the course.