vdmitriyev/uol-data-analytics

The repository contains a variety of references and materials for the data analytics course held at UOL

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/