Data Science with Python: Data Analysis and Visualization

Data Science with Python: Data Analysis and Visualization
Course Overview

This class is a comprehensive introduction to Python for Data Analysis and Visualization. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular Python data analysis and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

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Dates & Time Venue Tuition  
July 30, 2017 - August 27, 2017 1:00-5:00pm Weekends
Day 1: July 30, 2017
Day 2: August 6, 2017
Day 3: August 13, 2017
Day 4: August 20, 2017
Day 5: August 27, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1590.00 Add to Cart
September 17, 2017 - October 22, 2017 1:00-5:00pm Weekends
Day 1: September 17, 2017
Day 2: September 24, 2017
Day 3: October 1, 2017
Day 4: October 15, 2017
Day 5: October 22, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1590.00 Add to Cart
November 5, 2017 - December 17, 2017 1:00-5:00pm Weekends
Early-Bird Pricing!
Day 1: November 5, 2017
Day 2: November 19, 2017
Day 3: December 3, 2017
Day 4: December 10, 2017
Day 5: December 17, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1590.00
$1510.50
Early-Bird Pricing!
Add to Cart
Questions? Read our FAQs & Refund Policy
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Instructors
Tony Schultz
Tony Schultz
Tony received his Ph.D. in Physics from the City University of New York and has taught at Sarah Lawrence College over the past decade. Tony specializes in developing machine learning and pattern recognition algorithms for processing motion capture data. He is passionate about teaching scientific computing and studying deep structures in human motion.
Gian Klobusicky
Gian Klobusicky
Gian Klobusicky is a data scientist, currently focused on marketing analytics at Etsy. Before starting at Etsy, Gian worked as a data scientist at NBCUniversal and as a cognitive science researcher at the University of Rochester and Temple University. Most of his professional work has involved marketing/user behavior and predictive modeling. Gian primarily codes in Python; his favorite libraries include numpy, seaborn, and pymc3.

Product Description


Overview

 

This class is a comprehensive introduction to Python for Data Analysis and Visualization. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular Python data analysis and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

Details

 


Prerequisites

 

If you have good knowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) and are familiar with concepts of list comprehension and for/while loop, you are good to go with the Python for Data Analysis and Visualization course. We will cover these basic Python programming topics in the course as well, but move at a relatively fast speed.

Syllabus

Unit 1: Introduction to Python

Python is a high-level programming language. You will learn the basic syntax and data structures in Python. We demonstrate and run codes within Ipython notebook, which is a great tool providing a robust and productive environment for interactive and exploratory computing.
  • Introduction to Ipython notebook
  • Basic objects in Python
  • Variables and self-defining functions
  • Control flow
  • Data structures

Unit 2: Explore Deeper with Python

Python is an object-oriented programming (OOP) language. Having some basic knowledge of OOP will help you understand how Python codes work. More often than not, you will have to deal with data that is dirty and unstructured. You will learn many ways to clean your data such as applying regular expressions.
  • Introduction to object-oriented programming
  • How to deal with files
  • Run Python scripts
  • Handling and processing strings

Unit 3: Scientific Computation Tools

There are two modules for scientific computation that make Python powerful for data analysis: Numpy and Scipy. Numpy is the fundamental package for scientific computing in Python. SciPy is an expanding collection of packages addressing scientific computing.
  • Numpy
  • Scipy

Unit 4: Data Visualization

Python can also generate graphics easily using “Matplotlib” and “Seaborn”. Matplotlib is the most popular Python library for producing plots and other 2D data visualizations. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing statistical graphics.
  • Seaborn
  • Matplotlib

Unit 5: Data manipulation with Pandas

Pandas provides rich data structures and functions for working with structured data. The “DataFrame” object in Pandas is just like the “data.frame” object in R. Pandas makes data manipulation (filter, select, group, aggregate, etc.) as easy as in R.
  • Pandas

Final Project

After 20 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.


Recommended Readings

 

  • Learn Python the Hard Way: http://learnpythonthehardway.org/
  • Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Preparation – How to set up Python environment

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Testimonials

Aaron Ouyang

Great class. For only a 5 week class it is very comprehensive. Covers the basics and commonly used libraries used in python for data analysis as well has how to use them. Notebooks used in the class are a great go-resource after the class ends. Also a great community of data professionals and networking if you are thinking about a new gig.

Michael Caruana

Great comprehensive course that give you a thorough overview of Python and how it can be used in the field of Data Science.

Matt Gray

As a novice coder, this class was a great way to learn how I can manipulate and analyze data in Python. Would recommend for anyone interested in learning how to use python and apply to daily work.

Diana Enriquez

I enjoyed this class — I would give it a 4, only because it went a little too fast for me at some points. I am a beginner of the most clearly beginner level. I had played with some front end programming, but never attempted backend work. The 5 hour classes on Saturdays were tough because it required a lot of homework and studying during the week, but the instructor was good about answering questions and pushing us to keep working on new and interesting things. The program was extremely supportive of me while I was trying to learn new material, I have and will continue to recommend this class/NYC Data school.

Kannan Sankaran

I took the first offering of Data Science using Python a few weeks ago, and definitely recommend it to anyone who loves hands-on learning with some guidance. Let me explain: Last year, I took Coursera’s Machine Learning/Intro to Data Science courses and did well, but did not do a hands-on project that would enable me to retain a lot of knowledge. But this course required me to pick a detailed project and present it to a live audience, who then determined whether I did well or not. So I learned how to do web scraping, extract social media API data, write object-oriented Python, utilize a NoSQL database (MongoDB) to store results, and finally create visualizations in D3 and HighCharts. And then the pressure to present well, just to pass the class. Our instructor John was competent, knowledgeable and helpful, and covered a variety of useful tools like Pandas and Scikit Learn, including machine learning algorithms. And Vivian is always pushing us harder to do better. Sounds familiar?

Christopher Crosbie

The instructor, John Downs, was very knowledgeable and did an excellent job of providing an overview in the key areas of Python. After the five week class I went from knowing essentially nothing about Python to using it as one of my “go to” tools in which I am able to accomplish tasks at work on a daily basis.

Sasha Bartashnik

I took the beginner level Python class with John Downs and really had a great experience. John is very knowledgeable about Python and programming in general, and was able to be helpful to students of all levels in the class. The exercises in class and the homework got our hands dirty with the language and the final project was a great way to create a real result by the end of the course. Overall it was challenging, but a valuable intro to a useful tool that was easier to approach with real-life sessions than self-study demos on my own. I’ll definitely take classes with NYC Data Science Academy in the future and would recommend it to my friends.

Pia Ramchandani

John Down’s Python for Data Analysis class was a helpful introduction to using python toolkits such as Pandas and Scikit Learn to work with large and complex data structures. John started the class off slowly to get the group adjusted to Python syntax, but made sure to include all of the essential data management/analysis techniques to get started (e.g. dataset merging, manipulation, basic stats/regression, etc). In a short course, John did a great job of including numerous examples in ipython notebooks that he gives to the class– this approach was very helpful for exposing beginners to more complex techniques that they can go back to when they are ready. I definitely recommend this course to any beginner interested in learning how python can help make data analysis faster and easier.

Paul Schaffer

I strongly recommend this class to all potential students who have some programming background. The pace at the beginning is necessarily rapid to cover the basics of syntax and structure, so that more time can be devoted to numpy/scipy/pandas/etc. John was a fantastic instructor, and impressively it was his first time teaching the course! Super nice/patient/knowledgeable, and he has a real knack for explaining stuff. Taking introduction to Python for Data Analysis was a great decision for me. In a relatively short period of time, I was introduced to the top analytical code libraries in Python and gained experience using them. Well worth the time and money: I’d do it again in a heartbeat.