README.md

About

This git repository contains various scripts and references for the data analytics course held at the UOL.

⚙️ Tools

• Anaconda
  • Python 3 + other package dependencies (e.g., Jupyter Notebook, pandas, etc.)
• Visual Studio Code
  • An integrated development environment (IDE) that is used to write code almost in any programming language
  • Visual Studio Code also known as VS Code
  • 💤 Important: make use of many VS Code extensions (e.g., Python, Pylance, IntelliCode, autoDocstring, Remote Development, etc.)
• github or gitlab at UOL
  • A repository hosting functionality for your source code (and maybe data)
• overleaf
  • LaTeX online editor, preferable way to work on a LaTeX project in a group

💻 Configure VS Code for Data Science (Python)

Use following short video tutorials to prepare your local development workplace using VS Code and extensions

• 🎬 How to Setup Visual Studio Code for Python and Data Science | Better Data Science
• 🎬 3 Must Know VS Code Features for ML & Data Science!

• 🐸 🎬 (optional) Learn Visual Studio Code in 7min (Official Beginner Tutorial)
• 🐸 🎬 (optional) Using Git with Visual Studio Code (Official Beginner Tutorial)
• 🎬 (advanced) STOP writing bad Data Science CODE with these 10 tools in VS Code
• 🎬 (advanced) Powerful VSCode Tips And Tricks For Python Development And Design

🏳️ Tools (Optional)

• Sublime Text
  • An optional simple text editor
• Notepad++
  • Simple, lightweight, full-featured and simply amazing text editor
• PyCharm Community Edition
  • An optional IDE that can be used to write Python code

💬 Helper Projects

• Useful examples: uol-data-analytics-examples

🏁 Datasets

• Useful links to datasets: datasets-links-collection

🎓 Online Materials (Start)

If you decided to perform your data analytics project using Python it is strongly recommended that you will get through the courses listed below.

• 🔰 Exploratory Data Analysis
  • A Course in Exploratory Data Analysis
  • How to Prepare your Data
• 🔰 Working with Jupyter Notebook
  • Jupyter Notebook for Beginners: A Tutorial
  • Minimally Sufficient Pandas
• 🔰 An introduction to data science using Python and Pandas with Jupyter notebooks
  • Overview
  • Printing, Strings, Numbers
  • Logic, Loops, Lists, Tuples, and Dictionaries
  • Pandas Part I
  • Pandas Part II
  • Pandas Time Series
  • My top 25 pandas tricks (video)
  • NOTE: Rest of this tutorials also can be reviewed as well.

🎓 Online Materials (HowTos)

• 🚀 Micro-Courses by Kaggle (Faster Data Science Education)
  • Courses overview
  • Python
  • Pandas
  • Data Visualization
  • Data Visualization: from Non-Coder to Coder
• 🚀 Python + Jupyter Notebook Tutorials by BigDataAnalyticsGroup (last update: 04.2020)
  • Git repository with Python Tutorials
  • 🎬 Videos (in German) are also available on youtube here

🐍 Materials for self-paced study (Python + pandas)

• Reproducible Data Analysis in Jupyter
• Jupyter Notebook on Full Stack Python
• Quick dive into Pandas for Data Science
• Getting started with Python and Jupyter Notebooks for data analysis
• 5 Quick and Easy Data Visualizations in Python with Code
• What Is a Data Frame? (In Python, R, and SQL)
• The Simple Yet Practical Data Cleaning Codes for pandas
• Pandas MultiIndex Tutorial
• 3 Awesome Visualization Techniques for every dataset
• Stylin’ with Pandas
• Every Complex DataFrame Manipulation, Explained & Visualized Intuitively
• 10 Useful Jupyter Notebook Extensions for a Data Scientist
  • Qgrid, itables, DataTables, ipyvolume, bqplot, handcalcs
• Compare SQL and pandas How to Write All of Your SQL Queries in Pandas

📈 Materials for self-paced study (SQL)

• Warm up SQL
  • SQL concepts: from A to Z
  • All about SQL JOINs using (almost real) examples
• Data Wrangling with SQL (in some cases you will need to adapt provided solutions for your database)
  • Using SQL String Functions to Clean Data
  • Writing Subqueries in SQL
  • SQL string functions for Data Munging (Wrangling)
• SQL Basics
  • 5 Ways to Better Understand SQL by Adding Optional Parentheses

📊 Materials for self-paced study (machine learning)

• Python Machine Learning Tutorial: Predicting Airbnb Prices
• CNNs, Part 1: An Introduction to Convolutional Neural Networks

📉 Materials and useful references (SAP HANA)

• Python, SAP HANA and Analytics
  • Python Client API for machine learning in SAP HANA 2.0 (Express Edition SPS 03, Rev. 33
  • Setting up a HANA Express Python Machine Learning API Demo VM
  • This in only one post from the series of posts, for deeper dive, please review the rest:
    • Machine Learning in a Box (part 10) : JupyterLab
• SAP HANA and Machine Learning
  • Hands-on Video Tutorials for SAP Leonardo Machine Learning Foundation
• Visualization Tools
  • Use SAP HANA in Power BI Desktop
  • SAP HANA and SAP Design Studio 1.0 – The 6min Dashboard Data Geek Experiment

💻 Online Courses + Materials for Data Scientist

NOTE: There are always a lot of online courses available out there and this list is just a limited overview. This list is more a mixture of new materials with classical ones (update/review: 19.10.2020)

• Crashkurs für maschinelles Lernen (Microsoft, in German) - https://docs.microsoft.com/de-de/learn/paths/ml-crash-course/
• Intro to TensorFlow for Deep Learning (Udacity) - https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
• TensorFlow + Tutorials - https://www.tensorflow.org/tutorials
• Machine Learning (by Andrew Ng) - https://www.coursera.org/learn/machine-learning
• Statistical Learning - https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning
• Data Science (not all courses are free) - https://www.coursera.org/browse/data-science
• Stat 451: Intro to Machine Learning (Fall 2020) (Sebastian Raschka) - https://www.youtube.com/watch?v=OgK8JFjkSto&list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3&index=1
• MIT 6.S191 Introduction to Deep Learning (Spring 2020) (Youtube) - http://introtodeeplearning.com/