Data Science and Engineering Club

The Open Source resources in Data Engineering, Machine Learning, Data Science topics, inspired by Data Science Masters.

Presentations and Workshops

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Toolset

• Installation

• Introduction to Git and GitHub

• Anaconda Distribution

• Link to the Jupyter Notebook features

The Jupyter notebook is a part of Anaconda Distribution

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

Distributed Systems and Data Storage

• Introduction to Hadoop

• Introduction to Apache Spark

• Introduction to Apache Kafka

Introduction to Python (Pandas library)

• Python is powerful... and fast; plays well with others; runs everywhere; is friendly & easy to learn; is Open.

Pandas package is a part of Anaconda Distribution

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

• Introduction to Panda

• Intro to pandas data structures by Greg Reda

• Python for Data Analysis is a good book to start, written by Wes McKinney the main author of Pandas package. The second book is planning to release in August 2017.

Introduction to Python (Numpy library)

• NumPy is the fundamental package for scientific computing with Python. http://www.numpy.org/

• Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. http://matplotlib.org/

• Seaborn is a Python visualization library based on matplotlib. https://seaborn.pydata.org/

Data Wrangling with Python

Data wrangling is about taking a messy or unrefined source of data and turning it into something useful.

Data Wrangling with Python by Jacqueline Kazil, Katharine Jarmul.

Data Wrangling with Python notebook

Machine Learning with Python

Introduction to Machine Learning with Python by Andreas C. Mueller , Sarah Guido

Supervised Learning: Classification and Regression

Introduction to Scikit-Learn notebook

Unsupervised Learning: Clustering, Dimensionality Reduction

Introduction to Scikit-Learn notebook2

Introduction to Natural Language Processing with Python

Introduction to NLP

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Introduction to Web Development for Data Science with Python

• Introduction to Web Development

Introduction to Deep Learning with Python

• Introduction to Deep Learning

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Data Science Projects Data

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Reference Books and Research Papers:

Data Wrangling with Python by Jacqueline Kazil, Katharine Jarmul

Introduction to Machine Learning with Python by Andreas C. Mueller , Sarah Guido

The Elements of Statistical Learning T.Hastie, R.Tibshirani, J.Friedman

Natural Language Processing with Python � Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper http://www.nltk.org/book/ http://www.nltk.org/book_1ed/

Paper by Tomas Mikolov Google http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf