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Course Overview
This is a modern and comprehensive introduction to data science and machine learning in Python.
By the end of the course, you’ll walk away with a work applicable understanding of the Data Science process and how to use the methodologies and tools to solve real-world problems in business and academia.
See Course Packet- 4 Week, Part-Time Commitment
- Online, Remote Learning
- Intermediate/Advanced Level Program
- Immersive Online Environment
Your Instructor
Isaac Faber Ph.D.
Chief Data Scientist at U.S Army AI Task Force
Isaac comes to Coding Dojo with over 15 years of experience as a Data Scientist and several major academic accomplishments in Statistical Programming, Systems Engineering, and Operational Analytics. Furthermore, he also comes with 4 years of experience as an Assistant Professor at West Point Military Academy's College of Engineering.
Learn More:
Instructor Spotlight: Q&A with Isaac Faber
Ph.D Decision & Risk Analysis
Stanford University
2016 - 2019
Assistant Professor
West Point
2011 - 2014
Chief Data Scientist
Matrix DS
2002 - present
The Fundamentals of Modern Data Science
In 4 weeks, you’ll learn the principle concepts and technologies behind modern Data Science, and work on real data-sets and problems to put your learning into practice.
See Course Packet
Technologies you'll learn:
- Python
- Numpy
- Pandas
- Matplotib
- Seaborne
- Bokeh
- SciKit-Learn
Your Schedule
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Week 1
Data Prep in Python
Data Prep with Python & DS Libraries
Learn the many ways of preparing and cleaning data with Python and scientific libraries, such as Pandas and Numpy.
Technologies Covered- Python
- Numpy
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Week 2
Intro to Data Analysis
Data Analysis & Scientific Visualization
Analyze, explore, and visualize large data sets in the same way that real Data Scientists do on the job.
Technologies Covered- Matplotib
- Seaborne
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Week 3
Data Modeling & Machine Learning
Foundations of Data Modeling & Intro to Machine Learning
Build statistical models — regression and classification — that generate usable information from raw data.
Learn to apply decision trees and random forests to your data modeling via SciKit-Learn, a machine learning library utilizing Python.
Technologies Covered- Scikit-Learn
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Week 4
Data Visualization & Project Week
Build, Deliver, Present
Put your learning from the last 3 weeks to the test and present/visualize a large-scale Data Science project.
Technologies Covered- Bokeh
Data Prep with Python & DS Libraries
Learn the many ways of preparing and cleaning data with Python and scientific libraries, such as Pandas and Numpy.
- Python
- Numpy
Weekly Lectures & Office Hours
Sit down virtually with your instructor and classmates once a week for demos or lecture topics. Also check-in with your instructor for regular office hours to go over questions and assignments.
See Course Packet
24/7 Full Content Access
From day one, gain full access to our industry-leading learning platform and your Data Science curriculum. Get a head-start on new topics or review previous ones -- it’s up to you!
24/7 Full Content Access
From day one, gain full access to our industry-leading learning platform and your Data Science curriculum. Get a head-start on new topics or review previous ones -- it’s up to you!
Data Science Course Packet
Dig Deeper into our course and curriculum
Upcoming Start Dates
- Course Dates
- Deadline to Enroll
- Tuition
- Available Seats
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OCT 21st, 2019 to Nov 15th, 2019 |
Oct 18, 2019 |
$2,795 |
12 seats left |
Payment Plans
Payment is required to reserve a seat in the program.
All payments are refundable until day 1 of class.
Pay in Full
1 Payment
Due to reserve seat in programming
Full Payment
$500 off for full payments
Split Pay
2 Payments
Due to reserve seat in programming
Payment 1
$1,000
Due by 1st Friday of class
Payment 2
$1,795
FAQ
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Is this course good for beginners?
This course requires beginner level experience in Python programming. This can be from professional or recreational experience.
The ideal student will have entry-level technical experience in software/web development or data analysis/statistics.
Upon enrolling, you'll be required to complete your prep work on Python programming and linear algebra.
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Are there any prerequisites to enroll?
All enrolled students are required to possess a High School Diploma, GED or equivalent.
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Is there financing available for this course?
Full tuition payments are eligible to receive $500 off the total tuition due. Monthly financing and scholarships are not available at this time.
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Will we work on projects during the course?
You’ll work on 1 final big project during week 4 of the program. Leading up to week 4, you’ll work on small and large assignments to put your learning into practice.
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How often will I meet with my classmates and instructor?
You’ll virtually meet with your cohort every week to sync on the topic of the week, as well as have access to office hours with your instructor.
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Do you offer a full-time Data Science bootcamp?
Not yet but it’s in the works! Speak to one of our team members if you’d like to join the waiting list.
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Any prep work required before starting?
Yes! You’ll need to brush up on your math skills and become familiar with the Python programming language.
Your required prep work will be provided upon enrolling.
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Will I be able to get a job?
For beginners, this course is one of the industry’s most comprehensive first steps towards starting a career in Data Science. But we didn’t design this course to directly land you a job in the field. Our primary goal is to provide you a modern and comprehensive introduction.
If you’re already technical, you’ll be able to apply the concepts and techniques in this course in your current career.
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How will I be able to access the course content?
If you’re already technical, you’ll be able to apply the concepts and techniques in this course in your current career.
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When are the lectures?
Lectures are held twice per week at 6PM PST on Monday and Wednesday.
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What is covered in each week?
Every week you’ll be introduced to a new fundamental topic in the Data Science process, and you’ll work on assignments to put your learning into practice. You’ll practice using real Data Science tools to solve real-world case problems.