About this Course

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Learner Career Outcomes

46%

started a new career after completing these courses

45%

got a tangible career benefit from this course

19%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 16 hours to complete
English

Learner Career Outcomes

46%

started a new career after completing these courses

45%

got a tangible career benefit from this course

19%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 16 hours to complete
English

Offered by

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IBM

Syllabus - What you will learn from this course

Content RatingThumbs Up83%(21,470 ratings)Info
Week
1

Week 1

4 hours to complete

Data Scientist's Toolkit

4 hours to complete
17 videos (Total 84 min), 1 reading, 4 quizzes
17 videos
Languages of Data Science2m
Introduction to Python3m
Introduction to R Language3m
Introduction to SQL3m
Other Languages6m
Categories of Data Science Tools2m
Open Source Tools for Data Science - Part 17m
Open Source Tools for Data Science - Part 25m
Commercial Tools for Data Science5m
Cloud Based Tools for Data Science8m
Libraries for Data Science4m
Application Programming Interfaces (API)4m
Data Sets - Powering Data Science6m
Sharing Enterprise Data - Data Asset eXchange3m
Machine Learning Models7m
The Model Asset Exchange5m
1 reading
Lab - Explore Data Sets and Models 10m
4 practice exercises
Practice Quiz - Languages 30m
Practice Quiz - Tools30m
Practice Quiz - Packages, APIs, Data Sets, Models30m
Graded Quiz30m
Week
2

Week 2

6 hours to complete

Open Source Tools

6 hours to complete
10 videos (Total 57 min), 9 readings, 7 quizzes
10 videos
Git and GitHub via command line (Optional)9m
Branching and merging via command line (Optional)5m
Contributing to repositories via pull request (Optional)8m
Getting Started with Jupyter Notebook5m
Getting Started with JupyterLab6m
Jupyter Architecture6m
What is RStudio IDE?4m
Installing Packages and Loading Libraries in RStudio IDE2m
Plotting Within RStudio IDE3m
9 readings
GitHub Lab - Getting started10m
Branching, Merging and Pull Requests on GitHub (Optional)10m
Pre-requisites for command line interface (Optional)10m
Configuring SSH access to repository (Optional)10m
Lab 2: Branching and merging via command line (optional)10m
Lab 3: Contributing to repositories via pull request (Optional)10m
Jupyter Notebooks on the Internet10m
Lab: RStudio – The Basics10m
Lab: Creating an Interactive Map in R30m
4 practice exercises
Practice Quiz - GitHub30m
Practice Quiz - Jupyter Notebook30m
Practice Quiz - RStudio IDE30m
Graded Quiz30m
Week
3

Week 3

3 hours to complete

IBM Tools for Data Science 

3 hours to complete
15 videos (Total 72 min), 2 readings, 3 quizzes
15 videos
Watson Studio Introduction4m
Creating an Account on IBM Watson Studio2m
Jupyter Notebook in Watson Studio - Part 1 2m
Jupyter Notebook in Watson Studio - Part 23m
Linking GitHub to Watson Studio2m
Other IBM Tools for Data Science1m
IBM Watson Knowledge Catalog6m
Data Refinery7m
SPSS Modeler Flows in Watson Studio6m
IBM SPSS Modeler7m
SPSS Statistics7m
Model Deployment with Watson Machine Learning4m
Auto AI in Watson Studio4m
IBM Watson OpenScale7m
2 readings
Lab: Creating a Watson Studio Project with Jupyter Notebook10m
Lab: Modeler Flows in Watson Studio10m
3 practice exercises
Practice Quiz - Watson Studio30m
Practice Quiz - Other IBM Tools30m
Graded Quiz30m
Week
4

Week 4

3 hours to complete

Final Assignment: Create and Share Your Jupyter Notebook

3 hours to complete
1 reading
1 reading
IBM Digital Badge2m

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