Working materials for the Data Science Bootcamp developed by the Institute of Data Science at Maastricht University
The following examples and implementations are not general Data Science rather financial use cases.
- Introduce the basics of the python programming environment such as functions, reading and manipulating CSV files, and the NumPy library.
- Introduce data manipulation techniques using pandas data science library.
- Introduce the abstraction of the Series and DataFrame as the central data structures for data analysis.
- Develop a general understanding of data formats and representations.
- Get an overview of some python visualization packages.
- Learn how to perform a data science pipeline and their best use cases.
1. Data Science with Python
2. Intro to Internet Parsing
3. Data Wrangling with Pandas
4. Exploratory Analysis and Visualization in Python
5. Plotly Express: a Walkthrough
- TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
- firmai/industry-machine-learning
- rhiever/Data-Analysis-and-Machine-Learning-Projects
- drivendata/data-science-is-software
- free-programming-books/
- awesomedata/awesome-public-datasets
- StephenElston/ExploringDataWithPython
- StephanieStallworth/Exploratory_Data_Analysis_Visualization_Python
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Python is an open-source high-level, interpreted, programming language.
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A data science/text analytics project may include everything from scraping data from the web, analyzing a mixture of text and numerical data, computing features, training a model, creating high-quality graphs, and then hosting a web app with results.
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70,000 libraries in the Python Package Index
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It has a massive user community, who contribute to a large number of high-quality, well maintained open-source tools.
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Widely used in industry and academia.
"Python isn't the best at anything, but it's second best at everything"
MaastrichtU-IDS/cheatsheets
Jupyter Notebook Guide
Disclaimer: The data sources and libraries are either open available or made up.