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.

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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.

The Process


16-week immersive learning experience

Attend our top-rated data science training for 16-week and learn what is needed to succeed in a data scientist role.

For more information, please visit

Online Data Science Bootcamp

Practical Experience

Work as data science trainee

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

Career Support

Get help in finding a job 

Build your data science profile. (git repo. , tutorials, & blogs)

Mock interview with real data scientist to get you prepared for the real world

Apply for jobs & land your dream job!

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.

Tools & Environments

Cloud Platforms

Storage & Databases


CRM & Automation​





Career Support

While you learn practical skills as a data science trainee, we will also help you with your job search.

Interview preparation and mock interviews

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

We will help you refine your resume and apply for jobs.

Our bootcamp alumni work at these companies

What our customers say

At the end of the bootcamp, five days now, I definitely feel more aware of concepts, potential problems. I think the biggest value is the translation from the concepts to examples combined with an exercise, you have homework, and kind of trying to apply it. This gives some confidence to make a start, however small.
Learn Data Science-Harris-Alumni

Harris Thamby
IT Solution Manager

Learn Data Science-Microsoft Alumni Attendee
The instructors did such a great job of explaining the math in simple, iterations so I did not feel overwhelmed or lost. Their programming knowledge made learning R and the libraries associated with machine learning a breeze. It flowed in a manner which didn’t make me feel as if large details were cloudy.
Cody Morgan web

Cody Morgan
IT Dev. Manager

Highly valuable course condensed into a single week. Enough background is given to allow one to continue their learning and training on their own. Good energy from the instructors. It is clear that they have real industry experience working on problems.
Ben Gawiser web

Ben Gawiser
Software Dev. Manager

Data Science Bootcamp - Amazon Alumni Attendee


16 Week Training
Immersive, hands-on learning experience
In-class exercises
R/python hands-on exercises associated with all training modules
Instructor Support
Assistant instructors and online chat support through the bootcamp duration
50+ additional exercises
R/Python exercises to solidify key concepts covered in-class
Learning Platform Access
Single point access to videos, quizzes, slides and other learning material
One year
Video recording of training
Video recordings of sessions available within 2 - 3 days of every class.
One year
Assess your learnning with all training modules
One year
Alumni Network
Access to 5000+ alumni network at 1500+ companies globally
Software subscriptions during the training
Subscriptions for cloud infrastructure and other software during the bootcamp
Unlimited access to all content
Online data science learning with video tutorials and R/Python Jupyter notebooks
Verified certificate from UNM
Earn a data science certificate with 7 Continuing Education Units through The University of New Mexico
1:1 Mentoring Session with a practitioner
Guidance from an industry practioner
6 Hours
12-week Internship at Data Science Dojo
Practical work experience working as a data science trainee
Job placement services
Support on resume writing and mock interviews to help you land your dream job



Frequently Asked Questions

No. We just help increase the chances of you getting hired. 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.

This is a remote position.

Absolutely. However, we will not offer any travel, boarding, lodging allowance. We will also charge a one-time facilities fee of USD 1200 for the office space, computer and other office amenities.

No. But keep in mind that this is a real learn and work opportunity. We expect you to work for at least eight hours a day with at least 4 hours of overlap with working hours rest of the team based in Redmond, WA (Pacific Time Zone).

Believe us, we have a lot of work in every single track. While you will eventually end up working on multiple areas, you can only choose a single focus area.

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.

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