Brochure
Enquire- Welcome from your Instructors!
- Python Packages for Data Science
Online
3 Hours
Quick facts
| particular | details | ||
|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Learning efforts
4 Hours Per Week
|
Course overview
The Statistics for Data Science with Python online course introduces you to the basics of statistical procedures and methods used for data analysis. After completing this programme, you will have practical knowledge of essential topics in statistics, including – data collection, summarizing data using descriptive statistics, displaying and visualizing data, and examining relationships between variables.
The Statistics for Data Science with Python syllabus also includes hypothesis testing, probability distributions, expected values, ANOVA (analysis of variance), correlation, and regression analysis. You will also get hands-on training in statistical analysis using Jupyter notebooks and Python – the tools of choice for Data Scientists and Data Analysts.
At the end of the Statistics for Data Science with Python course, you will undertake a project to apply various concepts learned during the course and solve a Data Science problem that involves a real-life inspired scenario. Coursera also provides a certificate upon course completion, which offers tangible career benefits. However, you must pay a fee to get the certification.
The highlights
- An IBM offering
- Purchasable and shareable certificate
- Flexible deadlines
- Takes 14 hours to complete
- Delivered in English
- Course Videos & Readings
- Graded assignments
- Hands-on training
Program offerings
- Hands-on training
- Delivered in english
- Course videos
- Readings
- Graded assignments
- Flexible deadlines
- Purchasable and shareable certificate
Course and certificate fees
The fees for the course Statistics for Data Science with Python is -
| Head | Amount |
| 1 month | Rs. 1,683 |
| 3 month | Rs. 3,384 |
| 6 month | Rs. 5,051 |
certificate availability
Yes
certificate providing authority
Coursera
Eligibility criteria
Data science with python
The Statistics for Data Science with Python programme does not mandate any prerequisites for joining. However, you should complete the Python for Data Science course before applying for this programme.
Moreover, if you'd like to receive a certificate of completion, you need to complete the entire course curriculum successfully.
What you will learn
Statistical skills
Knowledge of data visualization
After completing the Statistics for Data Science with Python course, you will be able to:
- Conduct hypothesis tests, regression analysis, and correlation tests
- Apply and calculate measures of dispersion and measures of central tendency to grouped and ungrouped data
- Summarise, present, and visualize data in a clear and concise way which provides practical insights to non-statisticians
- Demonstrate mastery in statistical analysis using Jupyter Notebooks and Python
- Identify appropriate hypothesis tests to apply on standard data sets
Who it is for
This Statistics for Data Science with Python training course is ideal for various students and professionals who intend to apply to data and statistics-driven jobs such as Data Analysts, Business Analysts, Statisticians, Data Scientists, and Researchers.
Admission details
To participate in the Statistics for Data Science with Python course by Coursera, follow these steps:
Step -1. Gain access to Coursera's website.
Step -2. Sign in with your existing Coursera credentials or create a new account. Next, read the course details.
Step -3. On the top of the course page, you’ll find the “Enroll for free” option. Click on the option.
Step -4. A pop-up page will appear, with two options i.e., audit only and purchase the certificate. Choose according to your preference.
Step -5. You’ll be redirected to the payment gateway if you want to purchase the course. Otherwise, you can start auditing the course immediately.
Filling the form
The Statistics for Data Science with Python certification online course doesn’t have an application form. You just have to sign in using your Coursera account to access the material. Alternatively, your Google and Facebook credentials can also be used to sign in.
The syllabus
Module 1: Course Introduction and Python Basics
Videos
Readings
- Course Overview
- (Optional) Basics of Jupyter Notebooks
App Item
- (Optional) Python Review
Module 2: Introduction & Descriptive Statistics
Videos
- Welcome to Statistics!
- Types of Data
- Measure of Central Tendency
- Measure of Dispersion
Quizzes
- Introduction and Descriptive Statistics
- Practice Quiz - Introduction to Descriptive Statistics
App Item
- Lab: Descriptive Statistics
Module 3: Data Visualisation
Videos
- Visualization Fundamentals
- Statistics by Groups
- Statistical Charts
- Introducing the teacher's rating data
Quizzes
- Data Visualization
- Practice Quiz - Data Visualization
App Item
- Lab: Visualizing Data
Module 4: Introduction to Probability Distributions
Videos
- Random Numbers and Probability Distributions
- State your hypothesis
- Normal Distribution
- T distribution
- Probability of Getting a High or Low Teaching Evaluation
Readings
- Alpha (α) and P-value
- Standard Normal Table
Quizzes
- Introduction to Probability Distribution
- Practice Quiz - Introduction to Probability Distribution
App Item
- Lab: Introduction to Probability Distributions
Module 5: Hypothesis testing
Videos
- ANOVA
- Dealing with tails and rejections
- z-test or t-test
- Correlation tests
- Equal vs unequal variances
Videos
- z-test or t-test
- Dealing with tails and rejections
- Equal vs unequal variances
- ANOVA•4 minutes
- Correlation tests
Assignments
- Hypothesis Testing
- Practice Quiz - Hypothesis Testing
App Item
- Lab: Hypothesis Testing
Module 6: Regression Analysis
Videos
- Regression - the workhorse of statistical analysis
- Regression in place of t - test
- Regression in place of ANOVA
- Regression in place of Correlation
Assignments
- Regression Analysis
- Practice Quiz - Regression analysis
App Item
- Lab: Regression Analysis
Module 7: Project Case: Boston Housing Data
Readings
- Project Case Scenario
- Overview of Project Tasks
- Task 1: Become familiar with the dataset
- Task 2: Generate Descriptive Statistics and Visualizations
- Task 3: Use the appropriate tests to answer the questions provided
- (Optional):Task 2: Create or Login into IBM cloud to use Watson Studio
- (Optional): Load in the Dataset in your Jupyter Notebook
- (Optional): Share your Jupyter Notebook.
Peer Review
- Create and Share your Jupyter Notebook
App Items
- Peer Graded Assignment
- (Optional):Obtain an IBM Cloud Feature Code
Module 8: Final Exam
Assignment
- Final Exam
Module 9: Other Resources
Reading
- IBM Digital Badge
Practice Exercise
- Opt-in to receive your badge!
Assignment
- Opt-in to receive your badge!
Plugin
- Cheat sheet for Statistical Analysis in Python
Scholarship Details
If you can’t afford to pay for the Statistics for Data Science with Python certification, you can apply for the financial aid program. To apply for financial assistance, you must submit an application form.
The form requires you to provide details pertaining to your educational qualifications, career motivation, and financial constraints. You will receive an email upon application approval, which typically takes 15 days.
How it helps
The main benefit of enrolling in the Statistics for Data Science with Python program is that you can audit the course for free. Also, the program is designed by IBM and delivered by Coursera, ensuring a quality learning experience. Moreover, the course is a self-paced one, meaning you can study at your own convenience.
Lastly, if you pay the certification fee, you will get an industry-recognized certificate from Coursera, which will enable you to apply for lucrative positions with confidence.
Instructors
Dr Murtaza Haider
Professor
IBM
Other Masters, Ph.D
Ms Aije Egwaikhide
Senior Data Scientist
IBM
Other Bachelors, Other Masters
FAQs
Will I earn any university credits after completing this course?
The Statistics for Data Science with Python online course doesn’t offer any university credit upon course completion. However, some universities may accept Coursera certificates for credit. It’s best to check with the institution to learn more.
How long is this programme?
The course duration is nine weeks.
Is financial assistance available?
Yes, Coursera provides financial assistance to those who cannot afford the course fee.
Do I have to pay any fee to participate in this programme?
No, you don’t need to pay anything to participate. A fee is only applicable if you wish to earn a certificate at course completion.
Is this course part of a specialisation?
Yes, this programme is a part of Data Science Fundamentals with Python and SQL Specialisation.
