Data Science with Python: Data Analysis and Visualization

Data Science with Python: Data Analysis and Visualization

Data Science with Python: Data Analysis and Visualization

Beginner

This class is a comprehensive introduction to data science with Python programming language. 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 data analytics 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.

Course Overview
Beginner

This class is a comprehensive introduction to data science with Python programming language. 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 data analytics 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.

October Session
$1590.00
October Session
Oct 28 - Dec 2, 2018, 1:00-5:00pm

Date and Time

October Session

Oct 28 - Dec 2, 2018, 1:00-5:00pm
Day 1: October 28, 2018
Day 2: November 4, 2018
Day 3: November 11, 2018
Day 4: November 18, 2018
Day 5: December 2, 2018
$1590.00
Add to Cart

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.

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.

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course.

Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

 

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

[IMPORTANT] In the class we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video.

Reviews

There are no reviews yet.

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.

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.

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course.

Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

 

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

[IMPORTANT] In the class we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video.

Reviews

There are no reviews yet.

Testimonials View All Student Testimonials

John Chen, Software Engineer
John Chen, Software Engineer
at
American Express

It's important when learning anything to get the fundamentals right. If you build bad habits, it can become difficult to fix them later on, especially if you have also built many dependencies on those bad habits. This is why when I wanted to start learning about data science, I chose to take this course to help me make the right choices from the very beginning.

I would say that I got exactly what I came for. Tony is a very good instructor. He is able to express complicated concepts in an understandable way, and I would definitely say that now I understand enough about the Python ecosystem that I could start learning on my own if I wanted.

Aaron Ouyang
Aaron Ouyang
Analyst at
Annalect

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
Michael Caruana
Senior Product Manager, Data Science at
Fusion

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
Matt Gray
Analyst, Insights & Strategy at
NBCUniversal, Inc

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
Diana Enriquez
Content Researcher, TED Content at
TED Conferences

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
Kannan Sankaran
Software Engineer, Business Systems at
AppNexus

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
Christopher Crosbie
Healthcare and Life Science Solution Architect at
Amazon Web Services

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
Sasha Bartashnik
Analytics at
Zulily

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
Pia Ramchandani
Manager at
PwC Advisory Analytics

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
Paul Schaffer
Director at
Analytics Media Group

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.

Date and Time

October Session

Oct 28 - Dec 2, 2018, 1:00-5:00pm
Day 1: October 28, 2018
Day 2: November 4, 2018
Day 3: November 11, 2018
Day 4: November 18, 2018
Day 5: December 2, 2018
$1590.00
Add to Cart