Data Science Training + Industry Experience

Not having any industry experience is the most common challenge cited by potential job seekers. We will give you the industry experience that you need. Apply now for our data science training and internship program.
Practicum is a unique approach to data science learning that blends in-class learning with real-world experience. Once you finish your 16-week training, you will work as a data scientist trainee at Data Science Dojo. We will help you build your data scientist profile, conduct mock interviews and help apply for open data scientist positions.

How does it work?

Get Trained

Attend our 16-week immersive data science learning program. Learn what is needed to succeed in a data scientist role.

Gain Practical Experience

Work as data science trainee. 

Work alongside with other members of our data science team on real-world projects. Learn not just data science but also the other skills that differentiate practitioners from everyone else.

Get a Job

Start looking for a job with our career support program. We will help you build your data science profile (git repositories, tutorials, blog posts). Go through mock interviews with real data scientist. Apply for jobs through our strongly connected alumni network.

10 career tracks to choose from

During the first week of your 12-week internship, you will choose a career track and finalize your learning roadmap. Just like our other data scientists, you will attend meetings, use project management tools, send status reports, checking your code, build pipelines/dashboards and meet deadlines.

You will get the practical experience you need to find a job.


Track 1: Analytics Translator

Analytics translators are professionals who serve as a bridge between various actors in a data science organization. Besides an understanding of core data science/engineering concepts, Analytics Translators possess skills like domain knowledge, project management, requirement gathering, prioritization, customer communication and communication of insights and results.


Who should consider this track?

If you would like to build a career as a product manager for data products, this track would be a good fit for you.


What will a trainee learn in this role?

We use some of the most sophisticated tools for analytics. You will be expected to understand customers based on usage heatmaps, email/web analytics, customer communication and other sources of data and come up with product ideas. You will be asked to prioritize the features in these products and get at least one product implemented by the end of your 12-week trainee period.

Track 2: Consumer Understanding

Our customers interact with via different touch points. Besides our website they visit other properties like code.dsdblogtutorialslearning platform, and demos. In addition to this, our customers also interact with us through our Facebook, LinkedIn, Twitter, YouTube and Instagram channels. We also send out email campaigns and surveys to our customers. Customer understanding track is about being able to get actionable insights about customers using all of these data points.


Who should consider this track?

Anyone interested in building a career in web analytics, customer understanding, market research and building data products.


What will a trainee learn in this role?

Cutting edge tools and techniques for understanding customer behavior. We use HubSpot, HotJar, Google Analytics, Segment.IO for data acquisition. You will be cleaning, joining, aggregating a lot of messy data. You will be expected to perform exploratory analysis on this data and possibly build propensity/churn models on real-world data and test your models in real business setting.

Track 3: Marketing Analytics

Modern marketing is all about being data-driven. Our state-of-the-art marketing analytics infrastructure collects a lot of data. How do we know which of the marketing campaigns, referrers, touch points or channels effective? How should we allocate our advertising dollar?


Who should consider this track?

Anyone interesting in becoming a Marketing Data Scientist.


What will a trainee learn in this role?

You will work at the intersection of classical marketing strategies and modern data science tools and techniques. You will learn tools for CRM, data acquisition, event tracking, campaign management, and visualization. techniques for segmenting, analyzing

Track 4: Sales Analytics

Data-driven sales strategies have become so powerful that organizations have dedicated groups of data scientists and analysts working on sales data, they see immense value in gathering insights from sales data. Sales analytics is identifying, modeling, and predicting trends to determine the best course of action for increased results. It involves all forms of business transactions, calls, emails, and meeting notes. You will work on creating target segments with the cutting edge tools and techniques in industry.


Who should consider this track?

Anyone interested in becoming a data-driven Sales strategist by analyzing and recommending best course of action


What will a trainee learn in this role?

You will learn to analyze textual data by processing and modeling a large number of emails. We use Outlook 365, and HubSpot to track email interactions. You are expected to perform exploratory analysis by converting unstructured data into structured data and create topic models.

Track 5: Web Analytics

Almost every business has an online presence these days and web analytics is a popular resource to collect and measure web data to understand and optimize web usage. We use the most sophisticated tools in the industry to optimize user experiences. It involves analyzing traffic data to map user behavior and increase conversion rates.


Who should consider this track?

Anyone interested in becoming a data-driven Web analytics engineer with an understanding in user behavior flows.


What will a trainee learn in this role?

You will learn to analyze traffic data and understand user behavior on the web. We use Google Analytics, HubSpot, and HotJar to monitor our website’s performance. You are expected to create actionable reports and recommend changes in web pages by developing a hypothesis and conducting online experiments.

Track 6: Social Media Analytics

Social media is commonly used to build an online presence and these networks collect large amounts of data every second. We have a huge following on YouTube, LinkedIn, Facebook, Twitter and Instagram along with several posts and mentions daily.This track involves gathering data from social networks to analyze and recommend changes to enhance likability.


Who should consider this track?

Anyone interested in becoming a Marketing Data Scientist


What will a trainee learn in this role?

You will work on gathering data from multiple social media networks using APIs to measure performance of our social media channels. You will also work on consolidating large amounts of semi-structured data to analyze social sentiment by putting them into context and categorize emotions by intensity.

Track 7: Customer Feedback Analytics

Customer feedback is one of the most important aspects of business. It is essential to collect feedback and understand customer’s experience. We analyze our customer feedback closely so we can find the hidden value for improving our services. This track involves working with surveys, reviews, and user behavior data gathered from our learning platform to learn about our customers needs.


Who should consider this track?

Anyone interested in building a career in customer success analytics and building data products.


What will a trainee learn in this role?

You will learn to analyze customer feedback by consolidating various sources of feedback into a structured form and build data visualizations for exploration. Using natural language processing techniques, you will work on understanding customer satisfaction.

Track 8: Email Campaigns Analytics

Every organization sends/receives thousands of emails and measuring success of email campaigns can directly attribute to revenue or conversions. Being able to harness insights from email campaigns will put organizations ahead of their competitors. Our email campaigns generate a lot of data and we use cutting-edge tools and techniques to track and analyze our email campaigns.


Who should consider this track?

Anyone interested in becoming a Marketing Data Scientist


What will a trainee learn in this role?

You will work on analyzing email campaigns to explore possible improvements in campaign design and outreach. We use HubSpot, Google Analytics, and Mandrill to track email campaigns. You are expected to run exploratory data analysis and conduct A/B tests on email campaigns.

Track 9: Advertising Campaign Analytics

Online advertising has proven to be an effective way to acquire customers. With online advertising we get data to measure the success of an ad. A combination of marketing goals and ad metrics allows us to build predictive models, knowing all the moving parts in an ad campaign can directly drive an organizations revenue is the power of analyzing ad data. This track involves working on ad campaign data to enhance effectiveness of ads.


Who should consider this track?

Anyone interested in becoming a Marketing Data Scientist.


What will a trainee learn in this role?

You will work on advertising campaign data from Google Adwords, Facebook, LinkedIn, and Instagram. You are expected to optimize acquisition cost, time to ROI prediction, segmenting target audience and build propensity to buy models with real advertising campaigns.

Track 10: ETL and Log Mining

Event logs represent a prime source of big data. Given the rich and varied data in event logs, it is a critical skill to have for a Data Scientist. Our learning platform and other web properties utilize cutting edge technologies in software and data engineering. This track might involve any of the following:

  • Setting up ETL pipeline for various online properties.
  • Mining of events (clicks, page loads, logins etc.).
  • Writing scripts


Who should consider this track?

Anyone interested in becoming a Data Analytics Engineer or starting a career as a a Developer with a focus on data analytics.


What will a trainee learn in this role?

You will work on building a data analytics pipeline for multiple platforms with fine-grain event tracking. You are expected to extract event logs, analyze user behavior and build dashboards for real-time tracking.

What technology stack will I learn?

Our data scientists and data engineers are not loyal to specific platform or tool. We use a combination of open source and paid tools for getting the job done. Take a look at some of the tools we use in our day-to-day work.
    • Git
    • Python
    • R Language
    • JupyterHub
    • AWS
    • DigitalOcean
    • GCP
    • Mongo DB
    • AWS
    • MySQL
    • Jira
    • Confluence
    • Bitbucket
    • MailChimp
    • HubSpot
    • Docker
    • Spark
    • Hadoop
    • Google Analytics
    • HotJar
    • PowerBI
    • MongoDB
    • MySQL
    • Python(Django)
    • WordPress

Career Support

While you learn practical skills as a data science trainee, we will also help you with your job search.
Interview Preparation
Interview preparation and mock interviews
Data Science Portfolio
By the end of the 12-week period, you will have a data science portfolio including some git repositories, public presentations, YouTube tutorials, and blogs
1:1 Mentoring sessions
We will help you refine your resume and apply for jobs.

Our bootcamp alumni work at these companies

What our students say

Ryan Eaton - University of California, Davis

I came to the bootcamp with a background in scientific data analysis hoping to broaden my understanding of predictive models and machine learning. Data Science Dojo surpassed my expectations through combination of in-person, discussion-oriented classes and practical, hands-on exercises. Modules on feature engineering and cross-validation techniques I found especially useful. From day one it was obvious that emphasis would be on algorithms and rationale underlying the predictive models covered over intricacies of the particular functions/libraries used to generate them. Though I had no trouble figuring out how to code to train and test models after working through the well-documented detailed exercises. All-in-all, Data Science Dojo is a strong choice for the student wanting to understand machine learning fundamentals.

Ryan Eaton
Staff Research Associate

Javier Beverinotti - Inter-American Development Bank

This bootcamp is excellent. I recommend it 100%. The professor in charge is very patient and he really knows the topics. A great experience.

Javier Beverinotti
Lead Economist

Ottmar Figueroa R. - IOM - UN Migration

Very comprehensive, excellent balance of theoretical understanding (the why behind the how) and practical application of concepts. Congratulations to all the team making the online cohort happen, it was a GREAT experience!

Ottmar Figueroa R.
Senior ICT Officer a.i. - Data Analytics and Business Intelligence

Andrew Choi - Care1st Health Plan

It was a great experience for increasing the expertise on data science. The abstract concepts were explained well and always focused on real applications and business cases. The pace was adjusted as needed to let everyone follow the topics. Week was intense as there are many topics to cover but schedule was well managed to optimize people attention

Andrew Choi
Program Analyst

Zainab Al Lawati - Petroleum Development Oman

It was the best training I've ever attended. The 5 days were packed with knowledge, great discussion, and hands-on experience. The learnings were also solidified by the additional exercises provided for practice. Despite the fast pace and the intensity, it was easy to follow and never boring :D

Zainab Al Lawati
Software Engineer

Chee Tong Leow - Apple

Very insightful course and actionable immediately after taking the course to start exploring my ML model.

Chee Tong Leow
Senior IT Manager

Rockel Marquis - MedStar Health

This training was an all around great experience. Data Science, Data Engineering and Machine Learning are complex but this training was presented in a way that anyone with basic business or technical skills can easily understand and apply the concepts.

Rockel Marquis
Ambulatory Data & Reporting Analyst

Emmanuel Tachie-Menson - ARB Apex Bank Ltd

I would absolutely recommend this bootcamp for pretty much anyone who works, even in a bank, because I just think that the content is fabulous. You just get to understand the whole landscape in data science. I would absolutely recommend Data Science Dojo!

Emmanuel Tachie-Menson
System Analyst

Harris Thamby - Microsoft

It was a great experience for increasing the expertise on data science. The abstract concepts were explained well and always focused on real applications and business cases. The pace was adjusted as needed to let everyone follow the topics. Week was intense as there are many topics to cover but schedule was well managed to optimize people attention.

Harris Thamby

Akshita Garg - Microsoft

I liked that the home works were not too hectic and the topics were covered in such a simple language which was very well understandable. I really loved the overall training and the way concepts were covered in such a short time.

Akshita Garg
Program Manager

Emily Paynter - Hamilton Medical Center

As a complete beginner, I was a little nervous that I would be behind or lost. The information was presented in such a way that is accessible to everyone. I feel like I completed an entire semester course in only 5 days. This was a great bootcamp!

Emily Paynter

Andrew Donate - United States Air Force

Excellent training! If you truly want to learn about data science and machine learning, this is the program to attend!

Andrew Donate

Edwin Agbenyega - University of New Mexico

I appreciate how much effort was placed in making the resources available. All the teachers and assistants were very helpful and took time to address all the problems we had. It was also a very practical bootcamp since we got to participate in a kaggle datascience contest during the week. Overall, this was an awesome bootcamp!!

Edwin Agbenyega
Institutional Researcher

Robert Taylor - University of New Mexico

I attended the Data Science Dojo data science bootcamp in Albuquerque, NM in October 2019. This was an outstanding learning experience.

Robert Taylor
Research Scientist 3 - Clinical and Translational Research

Sarun Prabhat Luitel - University of New Mexico

I had taken ML and AI in Undergrad and looking at the curriculum made me wonder.. how can 2 semesters worth of class fit in 5 days? I'll tell you .. you focus on theory and directly use off the shelf library and have learnt enough to command the parameters.

Sarun Prabhat Luitel
Programmer Analyst

Daniel De Francisco Cabral - University of New Mexico

I really liked it. It exposes you to many different tools and the online material is great.

Daniel De Francisco Cabral
Get in touch
Feel free to ask any questions or comments you may have and we’ll get back to you within one business day.
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Frequently Asked Questions

No. We consider job guarantees in a certain role border-line unethical. Thousands of past attendees have transformed their careers as a result of our training. However, your motivation and hard work will determine the job outcome.

Yes. Besides giving you practical work experience, we will help you with interview preparation, conduct mock interviews and 1:1 mentoring sessions and help you build your data science portfolio such as having a git repository, youtube tutorials, blog and other social media posts.

We are located in Redmond, WA. However, due to the COVID-19 pandemic situation, this position is going to be remote. You will be expected to work like any other Data Science Dojo team member using online collaboration and productivity tools.

Absolutely. Once the COVID-19 pandemic situation changes and we start working from our office, you are welcome to work from our Redmond, WA office.

No. But keep in mind that this is a real learn and work opportunity. We expect your work hours to have at least 4 hours of overlap with rest of the team in Redmond, WA.

We do not believe in one-size-fits-all, however, each of the career tracks involves a lot of work. While you may eventually end up working on multiple areas, we will encourage trainees to stay focused on just one career track.

Yes, practicum is meant for those who are committed to changing careers and provides a path to learn practical data science and analytics roles. The internship requires your 40 hours every week.