This is a 5 Weeks Python and Data Science course designed for individuals that would like ramp up with data science rapidly. Previous Python knowledge is not required.
All attendees will receive a certificate of completion of this course. The certificate will be issued to you after the course and a hash of it will be stored on the blockchain as proof of completion.
This includes 5 consecutive Saturdays of in-person classroom instruction and homework followed by team-based project work.
WEEK 0 – PREP WORK
One week before the course you will be provided with prep work. The prep work will get you going on prerequisites and warmups, including how to use Github, install an editor, Python and required tools. We’ll provide support during this period so that when you start class on Day 1 immediately.
WEEK 1 – CORE PYTHON & DATA STRUCTURE TECHNIQUES
In the first week, we will review core Python data structures including lists, tuples, dictionaries and sets. We’ll also look at some advanced data structures like deque, named tuples, counters and more. The goal of this week is to work deeply with the base Python libraries.
WEEK 2 – CORE PYTHON – MANAGING DATA
In the second we’ll start to look at importing, manipulating and exporting raw data. We’ll cover how we can deal with data sitting in file formats like CSV and Excel, import them into Python and manipulate the data, invoke REST APIs that provide data and manage JSON content.
WEEK 3 – DATA, SQL, NoSQL, Spark & PySpark
Week 3 we will explore connecting to large datasets using databases and query semantics. We will cover MySQL, MariaDB, Google Firebase and Apache Spark frameworks and see how we can work with data over a GB in size, derive analytics, filter, map/reduce and execute queries in parallel.
WEEK 4 – PANDAS, NUMPY & CHARTING
Although Python alone is fairly robust, it finds a lot of its power in the very popular data sciences libraries called pandas and NumPy. These libraries are the workhorses of the data science world and we will take a deep dive into these libraries exploring data structures like the ndarray, DataFrames and Series. We will then explore charting with Matplotlip and Plot.ly and create visualizations of summary data of large datasets.
WEEK 5 – MACHINE LEARNING
We’ll learn how a machine learning algorithm like kNN can be used with the Iris or Boston datasets to predict values. By the end of the day you will have a conceptual understanding of machine learning and how to use the most popular Python machine learning library scikit-learn to perform predictions and evaluate the accuracy of those predictions.
Homework will be supplied each week and is due the following class.
WHERE ARE STUDENTS COME FROM
Glad you asked. Just check out these charts and search the job boards. Business schools are teaching it, employers like JPMorgan are expecting it to see it on resumes.
Chainhaus is dedicated to and passionate about your education, coding and blockchain. We deliver classes globally and specialize in blockchain, AI and data science. We are geeks with MBAs and mystics of the dark coding arts 🙂 We live, eat and breath Python everyday building solutions for large customers and running events. We not only teach Python and blockchain but code & develop dapps and are business owners with a strong understanding of the industry and how it can be applied to businesses.
ABOUT THE INSTRUCTOR
Professor Jamiel is CEO of Chainhaus, an advisory, software development, application studio and education company focused on blockchain, artificial intelligence and machine learning. Jamiel has over 15 years of experience in technology, capital markets, real estate and management and is an adjunct professor at Columbia Business School, NYU and CUNY teaching graduate-level blockchain, AI and data science subjects. He is currently authoring a book on Corda with O’Reilly and runs one of the largest blockchain, AI and data science Meetups in NYC. Jamiel is a licensed real estate agent in New York and New Jersey with Douglas Elliman. Jamiel enjoys coding in over 8 languages, travel and the elegant nuances of MMA.
More information on his bio is available on LinkedIn: http://linkedin.com/in/jamiel
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