Frequently Asked Questions

Our application process is divided into two phases. The written application, and then a video-chat or in-person interview. We will accept people into the program on a rolling basis. We recommend spending time on the written application. It is a competitive application process.

It is a very promising landscape for Data Science jobs. A McKinsey Study conducted in 2011 predicted “there will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning…By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills.” This continues to be a growing field and one that requires a unique skillset and background.

A data analyst is usually someone who works within a company and helps to make and communicate insights from their data to measure outcomes, make predictions, and guide business decisions. Often there is a lighter coding burden for someone with the title “data analyst,” though they may be expected to know certain languages or packages in R or python.

A data miner’s role might be very similar to a data analyst’s, though use of the term “mining” usually carries the connotation of working with large datasets to find patterns and extract insights.

The term “data scientist” is used the most broadly. A job posting for a data scientist might describe a role identical to others called “data miner” or “data analyst,” though there is usually more and more diverse coding skills needed for a data scientist job. For the most part, though, data scientists are asked to participate in the whole cycle of problems and solutions – they help identify opportunities for companies to use data, find, collect and integrate relevant data sources, they perform analyses of varying degrees of complexity, they write code and create tools that teams and businesses will use continually over time, and they tell the story of what they have done to stakeholders.

A computer science course tends to be much more broad and theoretical. The Data Science boot camp will be focused on applications and the computer science skills we cover will be more narrowly focused to topics in data structures, algorithms, I/O, python language as are pertinent to the data science workflow.

In addition, we cover topics in statistics and machine learning that are not really computer science per se. Finally, we provide a lot of experience in the soft skills that make data scientists so valuable – client interviews, communicating results, working with deadlines, perfectionism, and expectations.

Some experience in both programming and statistics is necessary. Further background in either one is helpful but not critical for this bootcamp. You will jump into solving problems directly, which requires use of Python skills and statistics knowledge together. This means you will learn more in the area you have less of a background in. No matter which part you started strong at, you will have the complete skill set and confidence at the end.

Students will build a strong foundation in python-language, the UNIX/LINUX command line, machine learning packages such as scikit.learn, other statistics modules including Scipy and pandas, web scraping packages like BeautifulSoup, PostgreSQL and MongoDB databases, collaborative coding under GIT version control system, working on remote cloud servers such as AWS and Azure, custom visualization tools, especially Matplotlib and d3.js with support in HTML, CSS, and Flask/Bottle Frameworks.

They will also learn about (and become familiar with) distributed algorithm frameworks such as Hadoop/MapReduce, system architectures with multiple servers of different roles.

Each student will spend 20 to 30 hours doing online Pre-work before the start of the class. We provide a Command Line Crash Course, tutorials to become familiar with Python, and a number of package installation tutorials (i.e., numpy, scipy, pandas, scikit.learn), as well as some preliminary statistics work. Test-out/check-out modules will let students know when they are “prepared enough” for class.

You will need your computer, your brain and a great personality. You laptop can run any OS and have at least 16GB RAM, with latest multi-core processors, and at least a 100 GB HD.

You will complete multiple data science projects throughout the bootcamp. These projects will produce results like blog posts, graphs, or reports. For each of these projects, you will have a story of what the problem was, how you approached it, how you solved it, and what the results look like. The final project will be your passion project, where you will have full control, from start to finish. Besides giving you hands-on experience and confidence, these projects will provide you the stories and outcomes as a great way to demonstrate your abilities to potential employers.

Divergence Academy conducts immersive learning programs for data scientists and data engineers in Dallas-Fort worth (DFW) Area. We recruit and train high-potential candidates in machine learning, statistical analysis, software engineering, and big data. After working with us, our graduates will be placed in mostly Enterprise businesses in DFW area but some may choose to go Austin, San Antonio or Houston – the other three cities with lots of activity. Our programs consist of hands-on practice solving problems and extracting actionable information from real-world data sets.

Enterprise businesses in Dallas-Fort worth area are big consumers of Data Mining and Predictive Analytics. While startup environment is kicking off in high gear, the majority of data science activities is centered on the usage of modeling tools and integration with existing Enterprise Data Warehousing systems. Divergence Academy’s program is focused on bridging the practical application of Data Science with Math, Statistics and Software programming rigor to mining larger datasets and building better prediction and scoring models through application of machine learning and data at scale. The curriculum is based on feedback from local companies.

Reach out to us at hello (at) divergence (dot) com, or see register yourself as a partner on http://pconnect.divergence.academy.

Divergence Academy will be your full-time primary focus during the program. Each day consists of 1 or 2 short lectures and a series of exercises to practice the material covered in lecture. Participants typically spend about 10 hours in the classroom daily, with assigned readings and tutorials completed outside of class. This includes about 2 networking events or guest lectures opportunities each week. Classroom hours are from 9:00 – 6:00, Monday-Friday, and there are 15-20 participants in each program.

Yes. You can receive updates about application deadlines, new programs and workshops, and data science events by subscribing here.

International applicants are eligible to apply for the Data Science a Immersive program at Divergence Academy. Unfortunately, we are not able to offer visa assistance, and we cannot guarantee that our local companies will sponsor a visa.

No, Divergence Academy is not an accredited academic institution. We provide a Certificate of Professional achievement in Data Sciences for the students of our immersive program. We are also working with edX to provide a digital verifiable certificate that you can include on your LinkedIn profiles. Our success is measured by job placement and student satisfaction.

No. However, if you are accepted into one of our programs, we will point you to several resources that can help you find a place to stay, and offer guidance on nearby neighborhoods.

Our immersive programs use hybrid learning – integrated online on Open edX platform at http://online.divergence.academy and offline at our academy location in Addison, TX. We do not offer a completely online program today.

The Data Science Immersive is a hands-on, project-based program that trains participants to solve data science problems. Through working with messy, real-world data sets, students gain experience across the data science stack — data munging, exploration, modeling, validation, visualization, and communication. The program is based in Python, and utilizes tools and libraries including pandas, matplotlib, Numpy, Pymc, and scikit-learn. As data sets get larger, we introduce tools like SQL and NoSQL.

One of the best ways to learn more about the program is to attend out Open House events scheduled via meetup; follow our blog and twitter handle (@divergencedfw) and our Facebook page. The best way to learn more details about the day-to-day is to see for yourself. Come visit us at the academy – send an email to hello@divergence.academy. Here are links to the three blogs that most closely chronicle the program: We also host occasional in-person and online information sessions with Q&A about our programs. Subscribe to our email list to receive updates about these information sessions.

In the admissions process, we look for background in a quantitative discipline including foundational statistics, probability, linear algebra, and mathematics. We expect a strong working knowledge of at least one programming language (we use Python). Common backgrounds include engineers, scientists, and analysts with 0-15+ years of industry experience. The skills we screen for in the admissions are highly learnable for anyone with a background in engineering of any kind, data analysis, or the quantitative sciences. Our Data Science Primer will help guide any studying you need to do before the program.

Step 1: In the same week, complete our standard technical screen to measure your quantitative and programming skills. This consists of four questions covering the basics of programming, SQL, and statistical data analysis.

Step 2: Interview with one of the instructors in our program so we can get to know each other. We will ask you about your goals for the program, as well as evaluate your ability to code through a programming problem.

Step 3: The final interview is with one of the co-founders and is focused on data science topics like linear algebra, statistical analysis, and your approach to typical data science problems.

Final step: Receive your acceptance to Divergence Academy.

No. We partner with lending companies. We offer a $2,000 discount on the immersive program tuition fee for women, veterans or members of the U.S. military.

Our applicants are diverse — they include former researchers, mechanical and electrical engineers, consultants, professional stock brokers. Most of our successful applicants have backgrounds in analysis, software engineering, and/or quantitative PhDs. You should be proficient in Python (or very strong in another language), familiar with data analysis tools and practices, and know college-level math, statistics, and probability. Not sure what “proficient” or “familiar” mean? Try these exercises; if you can complete them comfortably, you are ready to apply.

Still not sure? The first step in our admissions process is a take-home assessment, so apply now and try your hand at it. If you aren’t accepted to the program the first time you apply, we do encourage you to continue studying and apply again. Admissions at Divergence Academy are rolling, so it’s always in your best interest to apply early.

Yes. As part of Divergence, we have partnered TAD Tuition Management System to offer a plan to complete payment of tuition in 9 installments. We are also actively working with other loan providers for lending options specifically tailored for Divergence Academy students. Once you apply, we will send you specific application links for our programs.

Yes. If you receive a discount, the discount amount is deducted from the tuition you owe before any lending options are considered.