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Data Science with R - Capstone Project

Image of instructor, Jeff Grossman

Financial aid available

IBM

About this Course

In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate.

For this project, you will assume the role of a Data Scientist who has recently joined an organization and be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modeling to be performed on real-world datasets. You will collect and understand data from multiple sources, conduct data wrangling and preparation with Tidyverse, perform exploratory data analysis with SQL, Tidyverse and ggplot2, model data with linear regression, create charts and plots to visualize the data, and build an interactive dashboard. The project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization.

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What you will learn

Write a web scraping program to extract data from an HTML file using HTTP requests and convert the data to a data frame.

Prepare data for modelling by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.

Interpret datawithexploratory data analysis techniques by calculating descriptive statistics, graphing data, and generating correlation statistics.

Build a Shiny app containing a Leaflet map and an interactive dashboard then create a presentation on the project to share with your peers.

Skills you will gain

Instructors

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Jeff Grossman

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IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

For more information about IBM visit: www.ibm.com

See how employees at top companies are mastering in-demand skills

Syllabus - What you will learn from this course

Module 1 - capstone overview and data collection, module 2 - data wrangling, module 3: performing exploratory data analysis with sql, tidyverse & ggplot2.

At this stage of the Capstone Project, you have gained some valuable working knowledge of data collection and data wrangling. You have also learned a lot about SQL querying and visualization. Congratulations! Now it's time to apply some of your new knowledge and learn about Exploratory Data Analysis (EDA) techniques, again through practice. You can use the datasets you wrangled in the previous Module. However, if you had any issues completing the wrangling, no worries - we have prepared some clean datasets for you to use. You will be asked to complete three labs:

Module 4: Predictive Analysis

Module 5 - building a r shiny dashboard app, module 6 - present your data-driven insights, frequently asked questions.

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center .

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IBM

IBM Data Science Professional Certificate

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.

Rav Ahuja

Instructors: Rav Ahuja +11 more

Instructors

Svetlana Levitan

Financial aid available

191,519 already enrolled

Professional Certificate - 10 course series

(63,219 reviews)

What you'll learn

Describe what is data science, the various activities of a data scientist’s job, and methodology to think and work like a data scientist  

Develop hands-on skills using the tools, languages, and libraries used by professional data scientists  

Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python 

Apply various data science skills, techniques, and tools to complete a project using a real-world data set and publish a report for stakeholders

Skills you'll gain

Details to know

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Add to your LinkedIn profile

Available in English

Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, Spanish, Persian, Korean

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Prepare for a career in Data Science

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Get exclusive access to career resources upon completion

Get free access to IBM’s People and Soft Skills Specialization

Improve your resume and LinkedIn with personalized feedback

Practice your skills with interactive tools and mock interviews

Plan your career move with Coursera’s job search guide

¹Lightcast™ Job Postings Report, United States, 1/1/22-12/31/22. ²Based on program graduate survey responses, United States 2021.

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills.

It’s a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – no prior knowledge of computer science or programming languages required – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist.

The program consists of 9 online courses that will provide you with the latest job-ready tools and skills , including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.

Upon completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.

In addition to earning a Professional Certificate from Coursera, you'll also receive a digital badge from IBM .

This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

Applied Learning Project

This Professional Certificate has a strong emphasis on applied learning. The courses include a series of hands-on labs in the IBM Cloud that give you  practical skills with applicability to real jobs , including: Tools:  Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio Libraries : Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects

Extract and graph financial data with the Pandas Python library.

Use SQL to query census, crime, and school demographic data sets.

Wrangle data, graph plots, and create regression models to predict housing prices with data science Python libraries.

Create a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability.

Apply and compare machine learning classification algorithms to predict whether a loan case will be paid off or not.

Train and compare machine learning models to predict if a space launch can reuse the first stage of a rocket.

What is Data Science?

Define data science and its importance in today’s data-driven world.

Describe the various paths that can lead to a career in data science.

Summarize advice given by seasoned data science professionals to data scientists who are just starting out.

Explain why data science is considered the most in-demand job in the 21st century.

Tools for Data Science

Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools 

Utilize languages commonly used by data scientists like Python, R, and SQL 

Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features  

Create and manage source code for data science using Git repositories and GitHub. 

Data Science Methodology

Describe what a methodology is and why data scientists need a methodology.

Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study. 

Determine an appropriate analytic model including predictive, descriptive, and classification models to analyze a case study.  

Decide on  appropriate sources of data for your data science project. 

Python for Data Science, AI & Development

Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.

Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.

Access web data using APIs and web scraping from Python in Jupyter Notebooks.

Python Project for Data Science

Play the role of a Data Scientist / Data Analyst working on a real project.

Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.

Apply Python fundamentals, Python data structures, and working with data in Python.

Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

Databases and SQL for Data Science with Python

Analyze data within a database using SQL and Python.

Create a relational database on Cloud and work with tables.

Write SQL statements including SELECT, INSERT, UPDATE, and DELETE.

Build more powerful queries with advanced SQL techniques like views, transactions, stored procedures and joins.

Data Analysis with Python

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Data Visualization with Python

Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story

Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble

Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps

Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

Machine Learning with Python

Describe the various types of Machine Learning algorithms and when to use them 

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

Applied Data Science Capstone

Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders 

Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation

Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors

Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model 

coursera ibm data analyst capstone project github -

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world. For more information about IBM visit: www.ibm.com

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Get a head start on your degree

When you complete this Professional Certificate, you can earn college credit if you apply and are accepted into one of the following online degree programs.

University of North Texas

University of North Texas

Bachelor of Applied Arts and Sciences

15+ hours of study/wk per course

University of London

University of London

Bachelor of Science in Computer Science

3 – 6 years

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Frequently asked questions

What is the refund policy.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

How can I earn my IBM Badge?

Upon completion of this program, you will receive an email from Coursera with directions on how to claim your  IBM Badge Opens in a new tab  through Acclaim. Learn more about IBM Badges Opens in a new tab  

What is data science?

Data science is the process of collecting, storing, and analyzing data. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the first course of this Professional Certificate, "What is Data Science?"

What are some examples of careers in data science?

An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background. Some examples of careers in data science include: 

- Business Intelligence Analyst

- Data Analyst

- Data Architect

- Data Engineer

- Data Scientist

- Machine Learning Engineer

- Marketing Analyst

- Operations Analyst

- Quantitative Analyst

How long does it take to complete the Professional Certificate?

The Professional Certificate requires completion of 9 courses. Each course typically contains 3-6 modules with an average effort of 2 to 4 hours per module. If learning part-time (e.g. 1 module per week), it would take 6 to 12 months to complete the entire certificate. If learning full-time (e.g. 1 module per day) the certificate can be completed in 2 to 3 months.

What background knowledge do I need for this program?

This Professional Certificate is open for anyone with any job and academic background. No prior computer programming experience is necessary, but is an asset, as are familiarity working with computers, high school math, and communication and presentation skills. For the last few courses, knowledge of calculus and linear algebra is an asset but not an absolute requirement.

Do I need to take the courses in a specific order?

Yes, it is highly recommended to take the courses in the order they are listed, as they progressively build on concepts taught in previous courses. For example the Data Analysis, Data Visualization, and Machine Learning courses require knowledge of Python covered earlier in the program.

What will I be able to do upon completing the Professional Certificate?

Become job ready for a career in Data Science. Develop practical skills using hands-on labs in Cloud environments, projects and captsones.

I already completed some of the other courses in this Professional Certificate. Will I get "credit" for them?

If you have already completed some of the courses in this Professional Certificate, either individually or as part of another Specialization, they will be marked as "Complete". You do not have to take those courses again, and will be able to finish the Professional Certificate more quickly. You will only need to complete the courses that you have not yet completed.

I have already completed the Introduction to Data Science Specialization. Can I still enroll in this Professional Certificate?

Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish the Professional Certificate more quickly.

Which program should I enroll in - the Introduction to Data Science Specialization, or this Professional Certificate?

This Professional Certificate consists of 9 courses. The "Introduction to Data Science" Specialization has 4 courses, all of which are also included in this Professional Certificate.

If you are unsure about your ability to commit to the level of effort and time required to complete this Professional Certificate, we recommend starting with the Introduction to Data Science Specialization, which has fewer courses. If, after completing the Specialization, you are still determined to continue building your data science skills, you can then enroll for this Professional Certificate and then just complete the courses that are not in the Specialization.

I have already completed the Applied Data Science Specialization. Can I still enroll in this Professional Certificate?

Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish this Professional Certificate more quickly.

How can I access job opportunities with IBM and other organizations after completing this Professional Certificate?

As a Coursera learner who completes this Professional Certificate, you will have special access to join IBM’s Talent Network. Our Talent Network members receive all of the tools you need to land a dream job with IBM - sent directly to your inbox! You will get job opportunities as soon as they are posted, recommendations to apply matched directly to your skills and interests, and tips and tricks to help you stand apart from the crowd.

Can I get college credit for taking the IBM Data Science Professional Certificate ?

Yes. The IBM Data Science Professional Certificate recently secured a credit recommendation from the American Council on Education's (ACE) Credit Recommendation, which is the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 12 college credits for completing the program. This aims to help open up additional pathways to learners who are interested in higher education and prepare them for entry-level jobs.

How do you share your proof of completion with the educational institutions for transferring credit?

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credlybadge, which contains the ACE®️credit recommendation.  Once claimed, you will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed. 

Where can I find more information on ACE credit recommendations?

Please see Coursera’s ACE Recommendations FAQ.

More questions

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COMMENTS

  1. ibm-data-analyst-professional · GitHub Topics · GitHub

    Updated on Apr 10 Jupyter Notebook firatolcum / IBM_Data_Analyst_Professional_Certificate_Course Star 2 Code Issues Pull requests This repository is for completed projects and code snippets from IBM Data Analyst Professional Certificate Course: https://www.coursera.org/professional-certificates/ibm-data-analyst

  2. GitHub

    GitHub - shavilya/IBM-data-analyst-capstone-project: This repository contains materials for the IBM Data Analyst Capstone course from Coursera, including code, Jupyter notebooks and project files, organized by module. It's intended for educational purposes only. Contact me if you have any questions or need help with the materials.

  3. GitHub

    IBM-Data-Analyst-Capstone-Project Documents and data used to create my Capstone Project for IBM Data Analyst Professional Certificate (Completed January 2022) Capstone Project Objective

  4. GitHub

    cap project Add files via upload last year README.md Update README.md last year README.md IBM-Data-Analyst About this Course In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate.

  5. sonpn82/IBM-Data-analyst-capstone-project

    Capstone project for IBM Data Analyst Coursera Course - GitHub - sonpn82/IBM-Data-analyst-capstone-project: Capstone project for IBM Data Analyst Coursera Course

  6. b06601024/Coursera-IBM-Data-Analyst

    Capstone Course 1: Introduction to Data Analytics Course 2: Excel Basics for Data Analysis Course 3: Data Visualization and Dashboards with Excel and Cognos Course 4: Python for Data Science, AI & Development Course 5: Python Project for Data Science Course 6: Databases and SQL for Data Science with Python Data Analysis with Python

  7. IBM Data Analyst Capstone Project

    You will perform the various tasks that professional data analysts do as part of their jobs, including: - Data collection from multiple sources - Data wrangling and data preparation - Exploratory data analysis - Statistical analysis and data mining - Data visualization with different charts and plots, and - Interactive dashboard creation.

  8. dikoharyadhanto/Data-Analyst-Capstone-Project-Coursera

    A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior.

  9. Projects · IBM-Data-Analyst-Capstone-Project · GitHub

    GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects.

  10. Applied-Data-Science-Capstone-coursera_BY_IBM

    Applied Data Science Capstone Coursera By IBM. Contribute to sherwinsab/Applied-Data-Science-Capstone-coursera_BY_IBM development by creating an account on GitHub.

  11. IBM Data Analyst Capstone Project > Week 1 > Lab 4 ...

    IBM Data Analyst Capstone Project > Week 1 > Lab 4: Collecting Data Using Web Scraping · GitHub Instantly share code, notes, and snippets. darryllamoureux / IBM Data Analyst Capstone Project Lab 4: Collecting Data Using Web Scraping.ipynb Last active 2 years ago Star 1 Fork 0 Code Revisions 2 Stars 1 Embed Download ZIP

  12. Project Scenario and Overview

    This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets.

  13. Coursera Capstone Project W3

    edownin1. /. Coursera Capstone Project W3 - Exploratory Data Analysis.ipynb. Last active 2 months ago. 1. 1. Code Revisions 3 Stars 1 Forks 1. Embed.

  14. Coursera Capstone Project W3

    Coursera Capstone Project W3 - Exploratory Data Analysis.ipynb · GitHub Instantly share code, notes, and snippets. edownin1 / Coursera Capstone Project W3 - Exploratory Data Analysis.ipynb Last active 3 weeks ago Star 1 Fork 1 Code Revisions 3 Stars 1 Forks 1 Embed Download ZIP Raw Coursera Capstone Project W3 - Exploratory Data Analysis.ipynb Raw

  15. Coursera Capstone Project W1L2

    GitHub Gist: instantly share code, notes, and snippets. ... Instantly share code, notes, and snippets. edownin1 / Coursera Capstone Project W1L2 - Collecting Data Using APIs.ipynb. Last active November 27, 2022 23:24. Star 1 Fork 1 Star Code ... Coursera Capstone Project W1L2 - Collecting Data Using APIs.ipynb ...

  16. IBM Data Analyst Professional Certificate

    IBM Data Analyst Professional Certificate | Coursera IBM Data Analyst Professional Certificate Unlock your potential in data analytics. Build job-ready skills for an in-demand career as a data analyst. No degree or prior experience required. Instructors: Rav Ahuja Enroll for Free Starts Jun 1 Financial aid available 132,757 already enrolled About

  17. Data Engineering Capstone Project

    Throughout the self-paced online courses, you will immerse yourself in the role of a data engineer and acquire the essential skills you need to work with a range of tools and databases to design, deploy, and manage structured and unstructured data. By the end of this Professional Certificate, you will be able to explain and perform the key ...

  18. IBM Data Analyst Capstone Project

    Start your review of IBM Data Analyst Capstone Project

  19. Data Science with R

    In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate. For this project, you will assume the role of a Data Scientist who has recently joined an ...

  20. Coursera Capstone Project W4

    Created 2 years ago. Star 0. Fork 1. Code Revisions 1 Forks 1. Embed. Download ZIP. Raw. Coursera Capstone Project W4 - Data Visualization.ipynb. Sign up for free to join this conversation on GitHub .

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  22. IBM Data Science Professional Certificate

    Starts Jun 1 Financial aid available 190,848 already enrolled About Outcomes Courses Testimonials What you'll learn Describe what is data science, the various activities of a data scientist's job, and methodology to think and work like a data scientist