Data Science

This page will eventually consist of a number of resources related to Data Science as practiced in Python. Right now I’m working on PANDAS.

An article I have found quite helpful as an introductory, opinionated guide is Minimally Sufficient Pandas by Ted Petrou (1/2019).

Articles

• Tirtha Sarkar. Statistical Modeling with Python: How-to & Top Libraries. kite, 2019.
• Mirko Stojiljkovic. Python Statistics Fundamentals: How to Describe Your Data. real python, 12/16/19.
• Kirit Thadaka. Data Science, the Good, the Bad, and the…Future. kite, 2019.
• Pranathi V.N. Vemuri. Image Segmentation with Python. kite, 7/18/19.
• T.J. Simmons. 10 Essential Data Science Packages for Python. kite, 5/27/19.
• Padmaja Bhagwat. Introduction to Artificial Neural Networks in Python. kite, 7/18/19.
• Hennadii Madan. Matplotlib Explained. kite, 3/5/19.
• Daniel Pyrathon. Practical Machine Learning with Python and Keras. kite, 1/30/19.
• The Kite Team. Tensorflow or PyTorch? A Guide to Python Machine Learning Libraries (with examples!). kite, 10/25/18.
• Eleanor Stribling. Python, NLTK, and the Digital Humanities: Finding Patterns in Gothic Literature. kite, 10/4/18.
• Luke Lee. First Steps with PySpark and Big Data Processing. real python, 7/31/19.
• Bryan Weber. Scientific Python: Using SciPy for Optimization. real python, 1/20/20.
• Joseph Lee Wei En. How to Get Started with Python for Deep Learning and Data Science. freecodecamp, 3/6/19.
• Beau Carnes. Learn NumPy and Start Doing Scientific Computing in Python. freecodecamp, 8/9/19.
• Brad Solomon. Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn. realpython.
• Rahul Agarwal. Data Scientists, the 5 Graph Algorithms that You Should Know. towardsdatascience, 2019.
• Mirko Stojiljkovic. NumPy, SciPy, and Pandas: Correlation with Python. realsimple, 12/23/19.
• Luke Lee. First Steps with PySpark and Big Data Processing. realpython, 7/31/2019.
• Shaumik Daityari. How to Plot Charts in Python with Matplotlib. sitepoint, 7/10/2019.
• Mirko Stojiljkovic. NumPy arange(): How to use np.arange(). realpython, 7/22/2019.
• Chris Moffit. Python Tools for Record Linking and Fuzzy Matching.* pbpython, 2/18/2020.

Natural Language Processing (NLP)

• Shaumik Daityari. Getting Started with Natural Language Processing in Python. 3/2019.

Pandas

• Zachary Wilson. The Pandas Library for Python. kite, 3/25/19.
• Bhavani Ravi. Python Pandas–Basics to Beyond. hackernoon, 2019.
• Bhavani Ravi. Learn Python Pandas in 5 Mins.
  • Part 2. 2/7/19.
• Parul Pandey. Time Series Analysis with Pandas. kite, 2019.
• Alex DeBrie. How to Use Pandas GroupBy, Counts and Value Counts. kite, 7/18/2019.
• Mokhtar Ebrahim. Python Pandas Tutorial: Getting Started with DataFrames. like geeks, 2/2019.
• Brad Solomon. Pandas GroupBy: Your Guide to Grouping Data in Python. real python, 11/18/19.
• Alex DeBrie. Pandas Pivot: A Guide with Examples. kite, 6/29/19.
• T.J. Simmons. The Quickest Ways to Sort Pandas DataFrame Values. kite, 6/25/19.
• Alex DeBrie. Pandas Merge, Join, and Concat: How To and Examples. kite, 5/3/2019.
• Alex DeBrie. Guide: Pandas DataFrames for Data Analysis. kite, 4/3/19.
• Dataframe Visualization with Pandas Plot. kanoki, 2019.
• Brad Solomon. Python Pandas: Tricks & Features You May Not Know. Real Python.
• Chris Moffitt. Effectively Using Matplotlib. practical business python, 4/25/17.
• Peter Nistrup. Exploring Your Data with Just 1 Line of Python. 9/25/19.
• Reka Horvath. Using Pandas and Python to Explore Your Dataset. realpython, 1/6/20.
• Mirko Stojiljkovic. Pandas: How to Read and Write Files. realpython, 12/2/19.

Books

• Jake VanderPlas. Python Data Science Handbook. O’Reilly, 2018.
  • Full text is available in github repo.

Tools

• Scikit-Learn – “Simple and effecient tools for predictive data analysis…built on NumPy, SciPy, and matplotlib.”
• Jupyter
  • Mike Driscoll. Jupyter Notebook: An Introduction. realpython.
• Tensorflow
  • Tensorflow Cookbook. 2.8k Stars – 2019.
• Renato Candido. Setting Up Python for Machine Learning on Windows. realpython.
• Data Science Python Notebooks – 18.5k Stars – 2019. On Deep Learning (Tensorflow, Theano, Caffe, Keras), scikit-learn, kaggle, big data (Spark, Hadoop, MapReduce, HDFS), matplotlib, pandas, numpy, scipy, etc.
• Keras
  • Victor Zhou. Keras for Beginners: Implementing a Convolutional Neural Network. 2019.
• Homemade Machine Learning* – 14k Stars – 2020 – “Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.”
• Streamlit – “open-source app framework is the easiest way for data scientists and machine learning engineers to create beautiful, performant apps in only a few hours!”