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
Introduction to Data Science in Python is an online certification programme provided by the University of Michigan and available on Coursera which is one of the five specializations of Data Science. Thus, this course must be pursued prior to the other course in the specialisation i.e Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python. Learners will be able to study data science in Python in greater depth with the tutelage of experienced instructors.
Introduction to Data Science in Python Certification, accessible on the platform of Coursera, will expose the candidates to basic techniques of Python programming such as lambdas, reading the CSV files, and the NumPy library and python programming environment. By enrolling in the Introduction to Data Science in Python Online Course, the learner will develop the capacity to deal with tabular data and manipulate it along with running the fundamental inferential statistical analyses.
Introduction to Data Science in Python Certification by Coursera will shed light on data manipulation, and the process of using the group by and pivot tables in an effective fashion. Introduction to Data Science in Python Certification Syllabus will help the students explore the cleaning techniques using the popular python pandas data science library and usage of the Series and DataFrame for data analysis and many more.
The Highlights
- Provided by Coursera
- Approximately 34 hours of programme
- Offered by the University of Michigan
- Flexible Deadlines
- Intermediate Level Course
- Shareable Certificate
- Financial Aid Available
- 100% Online Course
Programme Offerings
- English videos with multiple subtitles
- intermediate level course
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback
- Graded Programming Assignments
- 14 day refund period
- EMI payment options.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
Introduction to Data Science in Python Certification Fee will differ based on the candidate’s learning duration in terms of the month. The free structure is provided in the table given below:
Description | Total Fee in INR |
Course Fee, 1 month | Rs. 4,117 |
Course Fee, 3 months | Rs. 8,234 |
Course Fee, 6 months | Rs. 12,352 |
Eligibility Criteria
Certification Qualifying Details
The students are required to attend the programme in the full sense by completing all of the course materials, readings, videos, graded and peer-reviewed assignments, quizzes, etc to be eligible to be conferred with Introduction to Data Science in Python Certification.
What you will learn
Through the Introduction to Data Science in Python Training, the learners will be able to build a solid understanding of the following concepts:
- Python Programming
- Numpy
- Pandas
- Data Cleansing
Who it is for
Introduction to Data Science in Python Classes is the course designed as part of five specializations in data science and thus, the course is suitable for the professionals such as Python Programmers, Data scientists, etc.
Admission Details
Step 1- First, the candidates are required to register and sign up on https://www.coursera.org/ to get the courses offered on Coursera.
Step 2- After activating the Coursera account, the candidate can sign in.
Step 3- Then, the candidate can search the ‘ University of Michigan’ in the search box to be able to find the courses offered by the University of Michigan.
Step 4- Find the course ‘Introduction to Data Science in Python’ in the list and click on it.
Step 5- Then, the page of the course will appear, click on the option ‘enrol’ and pay the mentioned amount of fee or can just audit the course without paying the fee.
The Syllabus
Videos
- Introduction to Specialization
- Introduction to the Course
- The Coursera Jupyter Notebook System
- Python Functions
- Python Types and Sequences
- Python More on Strings
- Python Demonstration: Reading and Writing CSV files
- Python Dates and Times
- Advanced Python Objects, map
- Advanced Python Lambda and List Comprehensions
- Numerical Python Library (NumPy)
- Manipulating Text with Regular Expression
Readings
- Syllabus
- Notice for Auditing Learners: Assignment Submission
- Help Us Learn More About You!
- Week 1 Textbook Reading Assignment (Optional)
- 50 years of Data Science, David Donoho (Optional)
- Syllabus
- Regular Expression Operations documentation
Quiz
Quiz 1
Programming Assignment
- Assignment 1
Ungraded Labs
- Your Personal Jupyter Notebook Workspace
- Module 1 Jupyter Notebooks
Plugin
- Regex Practice Session
Videos
- Introduction to Pandas
- The Series Data Structure
- Querying a Series
- DataFrame Data Structure
- DataFrame Indexing and Loading
- Querying a DataFrame
- Indexing Dataframes
- Missing Values
- Example: Manipulating DataFrame
Readings
Reading Assignments (Optional)
Quiz
Quiz 2
Programming Assignment
- Assignment 2
Ungraded Labs
- Module 2 Jupyter Notebooks
Videos
- Merging Dataframes
- Pandas Idioms
- Group by
- Scales
- Pivot Table
- Date/Time Functionality
Reading
Week 3 Reading Assignments (Optional)
Quiz
Quiz 3
Programming Assignment
Assignment 3
Ungraded Labs
- Module 3 Jupyter Notebooks
Videos
- Basic Statistical Testing
- Other Forms of Structured Data
Readings
- Science Isn't Broken: p-hacking
- Goodhart's Law (Optional)
- The 5 Graph Algorithms that you should know
- Post-course Survey
- Keep Learning with Michigan Online!
Quiz
Final Quiz
Programming Assignment
- Assignment 4
Ungraded Labs
- Module 4 Jupyter Notebooks
Instructors
UM–Ann Arbor Frequently Asked Questions (FAQ's)
The programme is instructed by Christopher Brooks who is the Assistant Professor in the School of Information.
The learners can complete the programme approximately within 34 hours.
Yes, the full process of the course is in the online mode and thus, the students can learn on their own schedule.
Coursera provides multiple subtitles for the course videos in numerous languages of Arabic, French, Portuguese (European), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, and Spanish.
No, the students will not be rendered placement support after the course.