You are here

Python for Data Science

Python for Data Science

Overview

Practical Programming
115 W 30th St
5th Fl Ste 501
New York, NY 10001
Register for Course
Saturday, January 26, 2019 - 5:00am
$999

Details

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. How this program is organized: Lecture on new topics takes about 90 minutes. After lecture, students start working on new exercises with instructor guidance. Around 1.00pm students present and discuss their work with instructors, learn alternative solutions, and best practices from instructors and invited data scientist professionals. You will learn: Discover best practices for data analysis and start on the path to becoming a data scientist 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 Implement the core Data Science techniques of Linear Algebra, Probability, Gradient Descent, and Linear Regression By the end of this course, you will have a Data Analytics Project to present to potential employers Preparation: Laptop Python for Data Science Syllabus: Session 1 Variables Data types: strings, integers, floats, lists Mutability Control Flow statements If statements For loopsPractical Exercises Session 2 Functions Data types: tuples, dictionaries, sets While loops Indexing and slicing Reading from CSV and TXT Files Writing to CSV and TXT Files Analyzing a Files content Practical Exercises Session 3 Scientific computing with Python NumPy Arrays Creating and manipulating NumPy Arrays Computation on NumPy Arrays Broadcasting and UFuncs Sorting and Indexing NumPy Arrays Practical Exercises Session 4 Python Data Analysis Library - Pandas Pandas Data structures Aggregating data in Pandas Data Indexing and Selection Logic, Control Flow and Filtering in Pandas Aggregation and Grouping High-Performance Pandas Visualization with Matplotlib Line Plots, Scatter Plots and Histograms Customizing Plots Final Project

Register for Course

Additional Sessions