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June 21st, 28th & July 5th, 12th, 19th (five Saturdays, 20 hours)
Beginner level(no programming background required). A course that introduces you to data analysis & machine learning in the Python programming language. We teach Numpy, Panda & more libraries. We cover topics such as nyc open data cleaning, sentiment analysis, web scrapping, face detection a lot of cool stuff.
(April 19th & May 24th are Easter & Memorial weekend, July 5th is independence weekend)
Time: 1:15pm - 5:15pm
Instructor: John Downs, Software Engineer in Test at Yodle
Course Overview: This five week course is an introduction to data analysis with the Python programming language aimed at beginners.
Project Demo Day & Certificates: From the rudimentary building blocks of programming basics, to data manipulation & use of advanced drawing packages, the course ends with a demonstration of a project of your choice on Project Demo Day. On Demo Day you will access & analyze real data, utilizing the tools & skillsets taught to you throughout the course. After the successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing pass, Honorable Graduation pass, & Active Participation pass.
Certificates are awarded according to your understanding, skill, & participation.
Week 1: Intro to Data Analysis
Using Project Euler problems & NYC Housing Data
Overview of the Python language
IPython - Command shell
Libraries & packages for data analysis - Pandas, Numpy, SciPy, Scikit-Learn
Performing basic data analysis
Week 2: Visualization & Algorithms
Using NYC Housing Data
Graphics with Matplotlib
Web Scraping - Collecting data from the internet
Regression Analysis - Linear & Logistic
Week 3: Machine Learning
Using New York Times articles & AdClick
Scikit-Learn - Library for data analysis & data mining
Supervised & Unsupervised Learning
Cluster Analysis - K Nearest Neighbors & K Means
Bayesian Analysis - Naive Bayes
Week 4: Time Series & Financial Modeling
Using Yahoo Finance
Time series with the Pandas library
Week 5: Building a Data Product