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Python for Data Science

Overview

Where

Practical Programming
115 W 30th Street
New York, ny 10001
United States

When

Tuesday, February 12, 2019 -
11:00am to 4:00pm

Details

Python for Data Science

February 12th - February 15th,

Four Session Comprehensive Course, Total in-Class Hours 20:
Tue, Feb 12th 11.00am-4.00pm
Wed, Feb 13th 11.00am-4.00pm
Thu, Feb 14th 11.00am-4.00pm
Fri, Feb 15th 11.00am-4.00pm

RSVP here: http://bit.ly/PythonForDataScienceRSVP

Read Syllabus here https://practicalprogramming.nyc3.digitaloceanspaces.com/PythonDataScience.pdf

Our program serves as the foundation for many well-known concepts of data science. We teach practical techniques and algorithms for extracting and studying useful knowledge from data. This course is not a theory class as we believe there are many ways to learn statistics and analytics concepts on your own. We are providing students with a set of practical tools for data science and knowledge on how to apply Python to solve linear algebra, statistics, and probability problems. This course is designed to fill the gap between theoretical academic research and the needs of the industry. We will start with a crash course on the basics of the Python programming language and then learn how to use Python to turn raw data into insight and knowledge.

What to expect from this program:

Fundamental introduction to Data Science using Python programming language, practical application of different statistical, analytical and linear algebra models to a variety of data science projects, and feeling comfortable enough to apply acquired knowledge on your own seeking a junior data scientist position.

Discover best practices for data analysis and start on the path to becoming a data scientist
Get comfortable using Python to build statistical and analytical models
Learn and practice essential tools for data analytics: NumPy, Pandas and Matplotlib
Learn to find solutions to problems by analyzing data using appropriate tools
Master your analytical skills by working on real life projects
Explore graphical techniques to see what your data looks like
Implement the core Data Science techniques of Linear Algebra, Probability, Gradient Descent, and Linear Regression
Build your own analytical tools with Python from scratch
Become familiar with industry standards and learn the best practices for writing code
By the end of this course, you will have a Data Analytics Project to present to potential employers

Who is this program for:

Novice and people with no previous programming experience, seeking a comprehensive course to enter the field of data and analytics and become data scientists.