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Markdown Files

Whether you write your book's content in Jupyter Notebooks (.ipynb) or in regular markdown files (.md), you'll write in the same flavor of markdown called MyST Markdown. This is a simple file to help you get started and show off some syntax.

What is MyST?

MyST stands for "Markedly Structured Text". It is a slight variation on a flavor of markdown called "CommonMark" markdown, with small syntax extensions to allow you to write roles and directives in the Sphinx ecosystem.

For more about MyST, see the MyST Markdown Overview.

Sample Roles and Directives

Roles and directives are two of the most powerful tools in Jupyter Book. They are kind of like functions, but written in a markup language. They both serve a similar purpose, but roles are written in one line, whereas directives span many lines. They both accept different kinds of inputs, and what they do with those inputs depends on the specific role or directive that is being called.

Here is a "note" directive:

The only good advice..**Perfect the Visuals**

It will be rendered in a special box when you build your book.

Here is an inline directive to refer to a document: {doc}markdown-notebooks.

Citations

You can also cite references that are stored in a bibtex file. For example, the following syntax: {cite}`holdgraf_evidence_2014` will render like this: {cite}holdgraf_evidence_2014.

Moreover, you can insert a bibliography into your page with this syntax: The {bibliography} directive must be used for all the {cite} roles to render properly. For example, if the references for your book are stored in references.bib, then the bibliography is inserted with:

Learn more

This is just a simple starter to get you started. You can learn a lot more at jupyterbook.org.

About This Role

This role will give you the opportunity to deliver high added value data
& analytics projects and build high quality an innovative solutions for our
clients within a growing service company.

What you will do?

  • Analyze complex client's request with full autonomy
  • Assist businesses in the decision-making process for Data Driven projects
  • Optimize data exploration using Machine Learning techniques
  • Clean and prepare the data with the Data Scientists
  • Analyze and interpret data to extract complex relationships and trends
  • Adapt and integrate analytics models into the client's IS environment
  • Assist the IT teams in all phases of the production, maintenance and update of the models developed
  • Contribute to the design of the technical solution chosen to collect, analyze data, and display the results obtained
  • Offer innovative solutions in the Big Data field
  • Provide internal trainings to new joiners when needed
  • Manage a project from A to Z
  • Support to the Team Lead/Manager regarding client relationship, business development and internal projects

DATA PROJECTS

  • Data: Web scraping/ API/ own data
  1. EDA/data analysis
  2. Data visualization
  3. Collaboration data project
  4. Explainer blogs/articles

PROJECTS WHAT'S INCLUDED

**PROJECT 1**
* EDA ON GROCERIES DATA

1. Objectives:
- Pattern findings in food consumption
- Seasonality in a year
- Before vs. after COVID
- Men vs. women
- What people usually buy together (basket analysis)
- Customer segmentation

2. Dataset:
Example: Kaggle Groceries dataset
(Or your own groceries bills :p)

3. Tools: R/Python, arules package

**PROJECT 2**
* WEB SCRAPING FINANCIAL NEWS - STOCK MARKET SENTIMENT

1. Objectives:
- Obtain financial news as text
- Visualize sentiment scores of your favourite companies

2. Dataset:
Example: Kaggle Groceries dataset
(Or your own groceries bills :p)

3. Tools: Python, beautifulsoup & NLTK package [Example](https://nickmccullum.com/stock-market-sentiment-analysis-python)

**PROJECT 3**
* DATA VIZ - CO2/GREENHOUSE GAS EMISSION

1. Objectives:
- Visualize greenhouse gas emission trends over time & cross countries

2. Dataset:
[Our World in Data](https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions)

3. Tools: Tableau/PowerBI

**PROJECT 4**
* TEAM PROJECT - COLLABORATE WITH LOCAL AGENCIES/FRIENDS

1. Objectives:
- Analyse survey data about your interest topic
- Or contact and request data from a local organisation

DON'T FORGET to write a blog post about it!

**AutoScraper**: A Smart, Automatic, Fast and Lightweight Web Scraper for Python - Check the link here [AutoScraper](https://github.com/alirezamika/autoscraper)

Alternativelly, you can use

**Diffbot**: Check the link here [Diffbot](https://www.diffbot.com/)

The use of the API

**Mixed Analytics**: API Connector for Google Sheets - Check the link here [Mixed Analytics](https://mixedanalytics.com/api-connector/)

Other data resources

**Lists of all of great resources including all kinds of data**: Check the link here [awesomedata/awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets)

Lets say it again..**Visuals Matter A lot**