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    • Home
    • IBM
    • Courses
    • Statistics for Data Science with Python

    Statistics for Data Science with Python

    BY
    IBM via Coursera

    Enrol in Statistics for Data Science with Python training by Coursera to master the basics of statistics, data analysis, and Python.

    Brochure Enquire
    • Overview
    • The highlights
    • Fee
    • Who it is for
    • Eligibility criteria
    • What you will learn
    • Admission Details
    • The syllabus
    • Scholarship
    • How it helps
    • Instructor
    • FAQs
    • Articles
    Mode

    Online

    Duration

    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 -

    HeadAmount 
    1 monthRs. 1,683
    3 monthRs. 3,384
    6 monthRs. 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

    Data analyst Statistician Data scientist Business analyst Scientist Research associate

    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
    • Welcome from your Instructors!
    • Python Packages for Data Science
    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

    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.

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