Program Overview

Our program is intensive and outcome-oriented.


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Participate in 1-on-1 mentorship with an experienced data scientist.


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A curriculum covering: programming fundamentals, mathematics and machine learning algorithms.


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We offer a robust career support program for every student.

Who Should Take Our Course?

Our students come from all walks of life. Generally, our applicants have 1-3 years of work experience in an analytical or technical field as a data analyst, software engineer, or traditional engineer (mechanical / electrical / others). MS/PhD students and recent graduates are also welcome to apply. Unlike programs with an extensive list of requirements, we place value on applicants who have work experience, are self-starters, and are passionate about data science.


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Python Programming

Learn the Python programming language and utilize the amazing ecosystem of libraries that have made Python the leader in academic and scientific programming. Explore the use of web scraping and APIs to connect to vast amounts of data.

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Data Science

With the Age of Data upon us, companies are desperately looking for those who can model, munge and visually communicate data. We start with foundational statistics and probability, dive into machine learning algorithms, and construct end-to-end data science projects.

Program Structure

Remote Learning

Learn Anytime From Anywhere

By joining our Data Science program, you will have the freedom of learning beyond the restrictions of a classroom. Choose your own pace and decide when you enjoy learning best. We are expanding the in-person bootcamp experience to everyone around the world. Benefits of our Data Science program include:

  • Setting your own schedule so you can be productive at your own pace
  • Gaining insight from the advice of industry professionals
  • Boosting your career options by taking part in one-on-one mentorship meetings
  • Participating in enlightening discussions with other students and alumni

Choose Your Pace

12 weeks
Full-Time Pace | 40 hours per week

24 weeks
Part-Time Pace | 20 hours per week

32 weeks
Extended Pace | 15 hours per week

Mentorship

One-on-One Mentorship Support

In other data science bootcamps you can expect a student-teacher ratio of 1-to-10 at best. Here at K2 Data Science you can expect an exclusive 1-on-1 learning experience. K2 searches for practicing industry professionals to serve as mentors for our students. Our mentors are on a mission to help you become a phenomenal data scientist, as well as, help you tackle the job market. Participating in our 1-on-1 Mentorship will provide you with the attention you need to succeed. The program includes:


  • Working with you to build real data science projects
  • Strengthening your skills
  • Checking your assignments, conducting mock interviews, and providing guidance throughout your recruiting process

A Portfolio That Lands You a Job

We have collaborated with Senior Data Scientists from leading analytics companies to develop our curriculum. The projects you build will help you create an amazing portfolio to showcase your newfound skills and experience.

Career Prospects

Career Support

When you complete the Data Science Program, you will be prepared with all the tools you need to advance your job search, and land your dream job as a data scientist. Completing our program will also grant you lifetime access to our alumni network for continued support in all your future endeavors. To ensure you’re hired, our program will help you:


  • Build a LinkedIn profile and establish a strong GitHub presence
  • Develop a professional resume to showcase your new skills
  • Make connections with industry professionals through our extensive network

It's becoming more and more accepted that bootcamp grads are just as capable as Masters degree holders.

- Bo Peng, Partner, Datascope Analytics

Our Mentors Come From Leading Technical Companies



Curemetrix
UBS
Backflip Studios
Firecracker


SEMSCIO
Ignition One
Teradata
Weather Company


Nom Nom
Mendeley
Systematrix
Emory

Our Curriculum

With our advanced curriculum and project-based structure, students get to learn today’s cutting edge data science techniques and technologies. The program is designed for students who prefer not to leave their day jobs, and are ready to take on an extra educational challenge during their evenings and weekends.

The program features a Python-driven curriculum, and immerses you in the world of data science and machine learning algorithms. The course is a perfect match for professionals with substantial technical backgrounds. With a blend of self-paced resources and one-on-one mentorship, students can easily merge their educational goals with their work-life balance.

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Programming & Mathematics

Build the fundamental skills needed for data science as you learn Python, the command line, a terminal and graphical text editor, Git + GitHub, Jupyter Notebooks, web scraping, APIs, relational (SQL) databases, statistics & probability, linear algebra, and multivariable calculus.



Phase 1 Foundations Program

Python Programming

Start from the beginning and progress to the most advanced concepts of Python — including recursion, advanced data structures, object-oriented programming and lambda functions.

Databases

Study how to access data sources and efficiently store them. We will explore web scraping and interface with APIs. With SQL, you will master how data can be made, validated, synced and stored.

Industry Tools

Learn the canonical software used by all data scientists. From Git for version control, to Jupyter for interactive coding, and everything in between.


Statistics & Probability

Go through an entire Statistics & Probability curriculum based in Python, including a section on Bayesian Statistics and Conditional Probability.

Mathematics

Review important concepts from Calculus and Linear Algebra in order to understand machine learning algorithms and optimization.

Data Science Curriculum

Learn how to explore new data sets, implement a comprehensive set of machine learning algorithms from scratch, and master all the components of a predictive model, such as data pre-processing, feature engineering, model selection, performance metrics and hyperparameter optimization.

Phase 2 Data Science Curriculum

Exploratory Data Analysis

Use Python to explore, clean, extract and visualize information from data sets. Use introductory data mining algorithms to find patterns, perform feature extraction, and reduce dimensionality.

Machine Learning

Apply everything you have learned so far to create expansive data pipelines and predictive models. You will be able to estimate a value, classify new data, cluster unknown data into discrete groups, use unstructured data like text or images, detect anomalies, make recommendations, and predict future transactions.

Electives

Extend your data science knowledge into new frontiers. Setup Hadoop clusters, process streaming data with Spark, create interactive visualizations with Bokeh, explore the R statistical programming language, tackle Kaggle challenges, and develop deep neural networks with TensorFlow and Keras.


Complete short projects at the end of each unit. These projects will expose you to every step of the standard workflow and prepare you for the final phase.

Phase 2 Data Science Projects

Supervised Learning

You will refine your understanding of regression models with regularization, optimization and overfitting. Next up is the large family of classification algorithms.

Unsupervised Learning

You will focus on unsupervised learning approaches-—including clustering and dimensionality reduction-—to find structure in unlabeled data. We will also cover mathematical topics such as vector spaces and distance metrics.

Time Series, NLP and Big Data

The last phase will cover sequential data, natural language processing, big data tools and techniques (Hadoop, MapReduce & Spark).

Data Science Projects

Build two final projects that highlight your interests and provide operational value.

Phase 3 Final Projects

Independent Project

Choose an industry that interests you, and try to answer a business question, or solve a common problem.

Capstone Project

Continue working on your personal project, work on a project with your current company, work on a project with your mentor's company, or work on a project with one of our startup partners.


Complete Career Support

Career Support when you are ready. The point is to get a job.


Our Career Support Program is a comprehensive collection of resources and services that are designed to prepare you for the technical recruiting process.

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Study Guides and Workshops

You will have access to a proprietary database of behavioral and technical interview questions. We hold workshops on various topics such as LinkedIn profiles, technical blogging, meetup networking, and much more.

Mock Interviews and Resume Reviews

Your Mentor will carefully review your resume and cover letter, lead mock phone screens, and conduct practice interviews so you can handle real technical interviews with confidence.

Industry Partners and Alumni Network

You will have the option to work with a tech startup on a real data-driven project at the end of our curriculum. Once you reach the project phase, feel free to start reaching out to alumni for advice on your job search.

Pick a Track

Structure the program around the unique constraints of your schedule. The total coursework and tuition are identical regardless of the track you choose. The tracks are merely guidelines, you are free to accelerate and decelerate within your comfort level.


Full-Time


Full-Time

40 hours

per week

12 weeks

48 mentor sessions

Part-Time


part-time

20 hours

per week

24 weeks

48 mentor sessions

Extended


Part-Part-Time

15 hours

per week

32 weeks

48 mentor sessions

Price: $6,000

Can I pay over time?

Yes--you can pay for the Data Science Bootcamp in 3 installments. If you opt for the payment plan, your total course price will be $7,200.

What if I don't like it?

If you’re unsatisfied with the program within the first 2 weeks, you can cancel for a full refund. If you decide to cancel later on, you'll recieve a prorated refund.

  • Work with an expert data scientist mentor
  • Collaborate with a community of peers
  • Design machine learning projects that impress
  • Network with industry contacts

Cohorts start each month.

Apply Now

Interested?

Got Questions?

Curriculum


How deep does your program go? How much of K2 is in-house curriculum vs. outside resources?

We have developed our own project-based, hand-crafted curriculum to make sure you receive the results you need to be successful. Our program is divided into two sections: curriculum and projects.

The curriculum is designed to teach you the concepts of building a robust machine learning system. Within each project, you will apply the machine learning system concepts in order to build your own work. We offer a combination of comprehensive videos, text-based curriculum, supplemental resources, and structured assignments.

Just as it’s critical to learn how to ride a bike without training wheels, it’s important to learn how to work independently as a data scientist. As you move from the curriculum to the projects, you will begin independently solving open-ended problems as an experienced data scientist would. You will still have opportunities to solve problems through collaborations with an experienced data scientist along the way.

On occasion we reference external books, video tutorials or courses for further learning.

How is K2's curriculum created?

K2's curriculum is designed and developed in-house by experienced data scientists. Our curriculum development team constantly acquires feedback from students, mentors, and employers to iterate our curriculum. This process ensures that you’re always learning the most up-to-date and relevant skills.

What are the data science projects?

You will work on two projects that simulate real-world projects data scientists deal with. The first project involves independently solving a business question of your choice. During the second project, you will work with your current company or a startup company within our network to tackle an outstanding business problem.

Can I customize the curriculum in any way?

Absolutely! You will work with your designated mentor to choose modules and projects that are tailored to your abilities and areas of focus.

Approach


What do you look for in students?

A number of our K2 students will be current MS/PhD students or recent graduates. We place value on students that have some level of work experience within a technical or analytical field. We also look for students who have taken the initiative to learn on their own, are enthusiastic about data science, and are committed to excellence. If this sounds like you, then you’ve found the right program!

We look for students who are empowered learners with the following qualities:

  • Passion for Data. You have shown your passion by beginning to learn on your own. You are truly determined to become a data scientist whether you get into K2 or not.
  • Technical Foundation. You have taken quantitative courses and explored computer programming through in-person classes or MOOCs.
  • Growth Mindset. No matter what you have worked on so far, you aimed for excellence. You are targeting growth for yourself and others.
How does mentoring work?

At K2, we teach with a flipped classroom model: you learn, complete assignments, and resolve issues on your own. If you have any questions, you can also message a Teaching Assistant through our online platform.

During your weekly video-call sessions, you'll learn about more complex concepts. Your mentor will work with you to develop the best practices of data science, and help you through any complicated problems. Feel free to seek professional advice or discuss your progress with your mentor. Your mentor will be there to encourage and help you every step of the way.

Above all, your mentor is committed to your success. Trust your mentor, and you’ll go far!

How do I choose my mentor?

Right now, your program manager will match you up with a mentor based on your location, background and future goals. Eventually, students will be able to choose their mentors.

If you have a specific goal for K2 that you would like to discuss, please contact a Program Manager to find a mentor best suited to your needs.

What does the online community consist of?

We believe there are incredible benefits to learning within a community of your peers. To support this, K2 Data Science offers a vibrant online community.

  1. Join our exclusive Slack channel, where we have developed a thriving community. Exchange questions, comments, feedback, and advice with fellow students.
  2. In each unit, you will have the opportunity to examine the work of your peers as well as receive feedback from your peers on your own projects.
  3. Weekly Office Hours where you can meet other students, talk about that week's assignments, practice your presentation skills, and ask questions about the curriculum over video chat.

Hiring & Job Prep


How does the job prep work?

The job prep curriculum will prepare you for the technical recruiting process. We walk you through how to navigate the world of predictive analytics, how to prepare a great data science portfolio, how to excel at technical interviews, and how to communicate the results of a machine learning project. The rest of our curriculum prepares you to excel at building end-to-end models.

The program curriculum includes dedicated material to review with an experienced mentor in preparation for the recruiting process to become a data scientist. Students create polished portfolios of projects that demonstrate job-ready analytical skills to prospective employers. Mentors carefully review resumes and cover letters for their students, lead mock phone screens, and conduct practice interviews so students can handle real technical interviews with confidence.

Students receive dedicated support from their mentor, including resume and portfolio critique, and a review of LinkedIn and GitHub profiles to ensure the best possible presentation to prospective employers. We will help you define criteria to guide your job search, and implement a process and cadence for managing the search.

What kind of jobs will I be ready for when I'm done?

Our program trains you to be industry ready for your first position as a data scientist, capable of taking on machine learning work.


How does job prep work for international students?

The job prep works the same for international students, but our industry contacts primarily consists of companies in major U.S. metropolitan areas. Many of the jobs listed on our internal job board, as well as exclusive industry contacts, will be based in the U.S.

Timing & Policies


How do the tracks work?

You'll opt in to one of our timed "tracks".

Each track signifies how many hours you will need to dedicate each week if you want to finish in a 3, 6 or 8 months.

If you ever fall behind, we'll reach out to make sure you complete the course by the expected dates! If you can't keep up with your chosen track, you can either switch to a less-intensive track, move to a future cohort, or discontinue your participation and receive a pro-rated refund. We want to ensure that you succeed based on your course goals.

How do I know which track is right for me?

Choose a track based on your schedule — are you looking to complete this program as soon as possible, and can you commit to a full-time pace?

Or, can you commit a solid chunk of time outside of your full-time job? In that case, the 20-hour/week track is likely the best choice.

An intensive course like ours is the ideal format to help you quickly learn the skills you need to move forward.

I still have questions...

We’re happy to answer any other questions you might have about K2 Data Science. You can email us here.

What kind of hardware is required? Do I need to purchase any software or tools?

We have 2 strong hardware requirements.

First, all students must have an external monitor serving as a second screen. This is necessary when you are learning data science in a distance learning environment.

Second, we strongly recommend a Mac or Linux-based computer. All our instruction will be for these types of operating systems and we do not have the resources to support issues that may arise with Windows PCs. If you have installed open source software, setup the Python development environment and used the PowerShell console, you should be fine on Windows.

You will not have to purchase any software, however, we may recommend paid apps or services that can enhance your productivity.