RCS_Data_Analysis_Python_11_2019

Python Data Analysis Course 11.2019

Binder(cloud hosted Jupyter notebooks) Beta

Course Plan

Goal

Build a complete data analysis pipeline using Python ecosystem

• Define the problem
• Gather the raw data
• Process (clean) the data
• Explore
• Analysis (apply models, make predictions)
• Reports and Visual Results in a form understandable to stakeholders

Setup (2h)

• Git and Github
• short intro to command line
• Text Editors
• Anaconda
• cloud based tools (Google Colab, myBinder, etc)

General Python Introduction (10h)

• basic data types
• working with compound data(slicing)
• structure (functions, classes, )
• program flow (conditionals)
• input/output
• importing external libraries
• introduction to NumPy, Pandas

Gathering Data with Python (2-4h)

• web scraping with Selenium, Beautiful Soup
• using APIs

Databases

SQL (2-4h)

• reintroduction to SQL databases
• ACID compliance

NoSQL (4-6h)

• NodeJS
• MongoDB
• other NoSQL databases

Big Data(2-4h)

• The 4 Vs - (volume, variety, velocity, veracity)
• Apache Hadoop Ecosystem
• Apache Lucene -> Elasticsearch

Cleaning Data (2-6h)

• advancing your NumPy, Pandas skills

Analysis and Data Exploration(4-10h)

• Pandas, matplotlib etc

Social Network Analytics

• Graph Analysis (Network Analysis)

Machine Learning with Python (6-10h)

Note: ML section may be expanded if good progress is made in other sections :)

Principles of ML -

• test/train data
• supervised/unsupervised learning
• classifiers
• regressors

ML Tools

• scikit-learn
• TensorFlow with Keras
• PyTorch
• Tesseract for OCR

Visualization (4-6h)

• PowerBI OR Tableau
• Python visualization libraries (mathplotlib, Seaborn)
• Graphviz
• Dash/Plotly

Useful Python Libraries (2-6h)

• PDF processing
• email
• PyQT
• nltk

Building a complete data analysis pipeline (4-6h)

• Course Project

Tools of the trade:

Anaconda Distribution(Python, R and more) https://www.anaconda.com/download/