Course Objective
Create a Jupyter Notebook
Insert code and markdown cells
Share your notebook through GitHub
Final Project:
Github Repository
Here is the link of the final assignment uploaded in my GitHub repository.
https://github.com/Akina-Aoki/IBM-Data-Science-Files/blob/main/DataScienceEcosystem.ipynb
VC Studio
A preview of the finished assignment imported VC Studio.
Jupyter Notebook
A preview of the same assignment, my first attempt to learn in Jupyter Notebooks.
Markdowns
Headings
Recall: to convert a code cell to markdown, first click inside the cell, then on the dropdown labelled "Code" in the toolbar and select "Markdown".
You can create headings by adding a #
sign before a word or a phrase. There are six levels of headings. The number sign #
indicate the level of heading for example # Hello corresponds to level 1,the highest level heading.
- Display the following text using six different heading levels.
Bold Text
You can create bold text by adding two asterisks or underscores before and after a word, phrase, or a sentence.
Italic Text
You can display text in italics by adding a single asterisk or underscore before and after a word, phrase, or a sentence.
Hyperlinks
You can create a hyperlink in the following format.'
Name of the link is the clickable link text
Link url is the web address of the url which will be directed on clicking the link.
To display a clickable link without a name, enclose the link in angle brackets. <>
Images
Images can be rendered in the following format.
Create Tables in Markdown
To create tables, use:
hyphens
(----)
for column headerspipes
|
to separate each columnText on a new line to separate each row
| Country Name | Capital | | -------------| ------ | | United States | Washington DC | | Australia | Canberra | | India | New Delhi |
Unordered List
An unordered list is also referred to as a bulleted list.
You can create an unordered list by adding dashes (-), asterisks (*), or plus signs (+) in front of line items.
Ordered List
We can create an ordered list by adding line items with numbers followed by periods.
Source Disclosure:
In this blog post, I discussed some basic key concepts from the IBM Data Science Professional Certificate by IBM and Coursera. The course provided valuable insights into the programming languages used by the data scientists, such as python, popular data science tools such as GitHub, jupyter notebook, and R studio. Throughout this post, I referenced and built upon the ideas presented in the course. You can find more information about the course Tools for Data Science | Coursera.