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The Data Incubator

Boston, New York City, Online, San Francisco, Washington

The Data Incubator

Avg Rating:4.79 ( 29 reviews )

The Data Incubator offers an intensive full-time, 8-week Data Science Fellowship in NYC, San Francisco, Washington D.C., and online. The Data Incubator is a Cornell Tech-funded training and placement organization leading the charge on data science education. The course accepts applicants who have advanced degrees in STEM and equips them with the final skills to be self-sufficient, productive contributors to data science. 

The Data Incubator's Fellowship application process is online, and applicants should have a strong scientific training where they can work with data programmatically and make valid real-world inferences based on data. Applicants also usually have a strong background in probability, statistics, and experience with programming, scripting, or statistical packages.

The Data Incubator also helps companies find new hires through the fellowship program. With various campuses in different locations, the program is free for admitted Fellows, and employers pay a Fellow's tuition fee if they are successfully hired. Students receive mentorship from hiring companies throughout the program and will build a portfolio project that employers value. The Data Incubator only accepts PhDs and Master's graduates.

The Data Incubator is a Pragmatic Institute company.

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  • Applied Machine Learning Online Course

    Apply
    Data Science, Python, Machine Learning
    OnlinePart Time6 Hours/week8 Weeks
    Start Date None scheduled
    Cost$3,495
    Class sizeN/A
    LocationOnline
    The Data incubator's Machine Learning course is a part-time, online program geared towards giving working professionals immersive, hands-on experience with the most sought-after machine learning skills. Developed with feedback from our hundreds of industry partners and based on the same rigorous methodology as our Fellowship, our course gives students a thorough understanding of the mathematical and statistical underpinnings of machine learning, as well as the practical skills needed to harness the power of raw data to solve pressing business problems. This class is for you if: - You have taken our Data Science Foundations course and want to deepen your understanding of machine learning - You have experience with modeling or have a background in data science, and would like to learn the theoretical principles and practical applications of machine learning - You want to gain hands-on experience applying machine learning techniques to real-world datasets Upon completion of this course, you will: - Understand and be able to deploy the steps required to complete an end-to-end machine learning project - Have learned about various machine learning models, how they work, pros and cons of each, and how they compare - Know how to best use machine learning to solve business problems and analyze problems using a machine learning perspective - Understand how to effectively use Python's machine learning library Find out which TDI online data science course is right for you https://www.thedataincubator.com/online-course-assessment.html
    Financing
    DepositThe course tuition is $3,495.00 with early-bird discounts available.
    Financing
    You can apply for financing through our partner, Climb Credit, and get a decision same-day with no impact on your credit.
    Getting in
    Minimum Skill Level- Intermediate Python - Familiarity with linear algebra - Familiarity with statistical modeling
    Prep WorkRecommended: Data Science Foundations Online Course from The Data Incubator
    Placement TestNo
    InterviewNo
  • Artificial Intelligence with TensorFlow and Keras

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    Data Science, Artificial Intelligence, Machine Learning
    OnlinePart Time6 Hours/week3 Weeks
    Start Date None scheduled
    Cost$1,695
    Class sizeN/A
    LocationOnline
    3-week part-time online program geared towards giving working professionals an immersive hands-on experience with Deep Learning, Neural Networks, Artificial Intelligence, and TensorFlow. Developed based on feedback from our hundreds of industry partners and using the same rigorous methodology as our Fellowship, the curriculum transforms AI amateurs into AI professionals.
    Financing
    DepositThe course tuition is $1,695.00 with early-bird discounts available.
    Financing
    You can apply for financing through our partner, Climb Credit, and get a decision same-day with no impact on your credit.
    Getting in
    Minimum Skill Level- Intermediate Python - Familiarity with linear algebra - Familiarity with machine learning
    Prep WorkRecommended: Data Science Foundations Online Course from The Data Incubator
    Placement TestNo
    InterviewNo
  • Data Science Fellowship

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    Start Date None scheduled
    Cost$0
    Class size70
    LocationSan Francisco, New York City, Washington, Online
    The Data Incubator Fellowship is an intensive 8-week program to transform PhD and Master's level data and science experts into professional Data Scientists. The program not only provides Fellows with hands-on experience with the tools employers value, it also teaches them the "soft skills" necessary to succeed in this competitive job market. The Data Incubator Fellowship works to get Fellows placed at one of their over 250 hiring partners upon graduation from the program. Over the course of the program, Fellows receive instruction from industry expert Data Scientists while building a portfolio project to showcase their programming and mathematical talents, which employers value. The online Scholar option of our Fellowship program is a paid option. Half of program tuition is refunded to Scholars who find a job with one of our hiring partners after graduation. Applications for both the free, intensive and paid, online programs follow the same process. After an application is accepted, applicants are given the option to participate in either program and details about cost and financing for the online Scholar program are provided.
    Financing
    DepositPlease see https://www.thedataincubator.com/fellowship.html#apply to request information about our paid Scholar option as part of the Fellowship.
    Financing
    Available through Climb Credit
    Getting in
    Minimum Skill LevelMust have or be within 1 year from completing a PhD or Master's degree.
    Prep Workhttps://blog.thedataincubator.com/2014/09/how-to-prepare-for-the-data-incubator/
    Placement TestYes
    InterviewYes
  • Data Science for Business Leaders Online Course

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    Data Science, Business Intelligence
    OnlinePart Time8 Hours/week1 Week
    Start Date None scheduled
    Cost$1,095
    Class sizeN/A
    LocationOnline
    The Data Science for Business Leaders bootcamp course was created for business professionals who want to learn about data, machine learning, and artificial intelligence. No technical background is required. You should take this course if you work with data scientists or analysts regularly, manage teams or projects with a significant data component, or find yourself translating between technical teams and management. The Data Science for Business Leaders course is designed for participants to gain the following: - Strategies to identify, capture, and extract value from both the current and potential data sources accessible to their organizations - A high-level understanding of key machine learning and artificial intelligence methods and how they can be applied to solve business problems and make intelligent business decisions - Guidelines for evaluating potential data science projects to identify return-on-investment "wins" for your business - Strategies for attracting, retaining, and supporting data science talent - Tips for anticipating and avoiding common pitfalls in data science applications. This class will run 4 consecutive days, Monday (5/20) through Thursday (5/23), from from 3:00-5:00 PM ET/ 12:00-2:00 PM PT each day.
    Financing
    DepositThe course tuition is $1,095.00 with early-bird discounts available.
    Financing
    You can apply for financing with our partner, Climb Credit, and get a decision same-day with no impact on your credit.
    Refund / GuaranteeUnfortunately, there are no refunds available.
    Getting in
    Minimum Skill LevelNo technical background is required.
    Placement TestNo
    InterviewNo
  • Data Science Foundations Online Course

    Apply
    Start Date None scheduled
    Cost$3,495
    Class sizeN/A
    LocationOnline
    The Data Incubator's Data Science Foundations online training course is an introductory 8-week, part-time bootcamp geared towards giving ambitious college and graduate-level students, recent college graduates, and working professionals an immersive hands-on experience with foundational data science techniques. Class sessions are LIVE online presentations, twice each week. Our Data Science Foundations online training course curriculum has been developed with feedback from our hundreds of industry partners, using the same rigorous methodology as our Data Science Fellowship program, to transform data amateurs into data professionals.
    Financing
    DepositThe course tuition is $3,495.00 with early-bird discounts available.
    Financing
    You can apply for financing through our partner, Climb Credit, and get a decision same-day with no impact on your credit.
    ScholarshipA $350 scholarship is available via CourseReport.com
    Getting in
    Minimum Skill Level- Elementary programming knowledge - Familiarity with statistics
    Placement TestNo
    InterviewNo
  • Data Visualization with Python Online Course

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    Data Science, Data Visualization, Data Analytics , Python
    OnlinePart Time6 Hours/week3 Weeks
    Start Date None scheduled
    Cost$1,695
    Class sizeN/A
    LocationOnline
    Data science is about helping humans understand the story behind the data, and visualizations provide a powerful tool for helping the analyst understand and communicate that story. Students in The Data Incubator's three-week, part-time Data Visualization course discuss the biases and limitations of both visual and statistical analysis to promote a more holistic approach. This course uses the same rigorous methodology as our Fellowship program.
    Financing
    DepositThe course tuition is $1,695.00 with early-bird discounts available.
    Financing
    You can apply for financing through our partner, Climb Credit, and get a decision same-day with no impact on your credit.
    Getting in
    Minimum Skill LevelElementary programming knowledge
    Placement TestNo
    InterviewNo
  • Introduction to Python for Data Science

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    Data Science, Python
    OnlinePart Time3 Hours/week1 Week
    Start Date None scheduled
    Cost$199
    Class sizeN/A
    LocationOnline
    Introduction to Python for Data Science is a 3-hour online workshop that teaches students how to use Python to extract, clean, and analyze data. This workshop is taught by a live instructor, so students have the opportunity to ask questions and receive feedback as they learn. Students will gain hands-on practice using Python for data science and leave the course ready to work on their own data science projects. Students who take our Intro to Python workshop qualify for a special tuition credit on our 8-week Data Science Foundations course for the cost of this workshop. Python has developed over decades into a versatile and hugely popular programming language for several reasons. It’s become an essential tool for data science and other scientific computing tasks, largely due to a thriving ecosystem of open-source libraries for analysis and visualization of large datasets. This course introduces students to working with Python packages that are commonly used for data science tasks like data manipulation and machine learning. First, we’ll cover basic Python syntax - showcasing the language’s intuitive code style and broad capabilities. We’ll demonstrate how students can easily start working with Python in Jupyter Notebook, an interactive computing environment that allows one to write and run live code - providing students with hands-on practice using Python for data science. Then, we’ll introduce students to Python’s powerful packages, extensions to the language that make it one of the most capable programming languages today. Students will gain familiarity with the Python packages focused on data-related tasks that form the Python data science "stack". Takeaways Ability to write and run Python code for data analysis in data science notebooks (data cleaning and reformatting, exploration, analysis). An introduction to the fundamental open-source data science libraries in Python and their roles, strengths, and weaknesses for data analysis (including: NumPy, Matplotlib, scikit-learn, & more). This is a hands-on workshop. Students will be immersed in code, and develop scripts & notebooks, which they can take home for future use. Pre-requisites Basic technical ability, comfort with computers, and desire to learn new tools. Minimal experience using computers for analysis or repeated tasks (like formulas or macros in Excel). No installation necessary! All course material is online - students only need internet access and a laptop with an internet browser (like Google Chrome or Mozilla Firefox).
    Financing
    DepositN/A
    Tuition PlansEarly bird discount available
    Refund / GuaranteeUnfortunately, there are no refunds available.
    Getting in
    Minimum Skill LevelBasic technical ability, comfort with computers, and desire to learn new tools. Minimal experience using computers for analysis or repeated tasks (like formulas or macros in Excel).
    Prep WorkNo installation necessary! All course material is online - students on
    Placement TestNo
    InterviewNo

Shared Review

  • Data scientist
    - 4/21/2018
    Liang S. • Graduate
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    Since I had always been in academic before taking TDI, 
    TDI is like a window to the industry, a bridge walking 
    me smoothly from the academic world to the industry one. 
    Through a series of activities like panel discussion and alumni party, 
    TDI offered me a great platform to know what kind of problems 
    companies are trying to solve, what skills they are looking for, 
    how the daily life looks like, etc. Moreover, TDI provides valuable 
    guidance in the whole process of job search, and last but not 
    the least, the chance to work with a bunch of very smart people.

  • Fun!
    - 8/2/2018
    Anonymous  User Photo
    Anonymous • Graduate Verified via GitHub
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    TDI is an intensive fellowship program in data science. Very similar to what you would experience in corporate dynamic culture. However, don't expect to learn to coding or basic machine learning from scratch. You are expected to have basic understanding of ML and data science tool. I would strongly recommend you to do enough homework before you apply to avoid later disappointment. I took around 4-5 DS and ML classes on coursera before I applied. 

  • Andres Gonzalez Casabianca  User Photo
    Andres Gonzalez Casabianca • Graduate Verified via LinkedIn
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    The application process can be daunting and intimidating, however, each step has its reasons. This makes each cohort learn and progress in a homogeneous pace, which is key to a successful completion of the TDI. 

    I was part of the D.C 2017 winter cohort and the 8 weeks were key to position myself as a Data Scientist in the industry. You share and work collaboratively with the rest of the cohort, making it invaluable because you are not only learning from the diverse curriculum but also from your peers. At the end of the day, Data Science is both a Science and an Art, so different perspectives and approaches to problem-solving definitely enhance your skillset. 

    Additionally, there is a focus on soft skills, from getting your resume up to speed to effective communication. Each week there are dedicated sessions on how to tackle interview questions, how to sell yourself, and how to navigate opportunely the recruiting process, complementing TDI's rigorous technical curriculum. 

    Going to the TDI was not only enriching but also enjoyable. You come out of the program with a powerful network of top-notch data scientist, a second to none skillset and the right toolkit to navigate the corporate world.

  • Anonymous  User Photo
    Anonymous • Applicant Verified via GitHub
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    After the interview, I was not admitted, but I now know what my weakness was - lack of a prepared project. The exam for which  4days are given consists of 3 parts. Two parts are questions with ~8 subquestions with increasing difficulty. Anyone with a basic data science and programming skills will answer those questions in 2-3 full days. What I'd recommend doing is to complete the 3rd questions - on your project. In their email, prior to the exam, they give a hint that they want the project to be in the draft form. It should be done on a large data set, interesting in analysis and in insight, and be implemented desirably as Heroku app  (and look similar to the ones in the Youtube videos of the projects). IMHO, if you'll prepare such a project you won't have a problem having a good grade. At that point - you might be already a good data scientist - apply for jobs independently. Good luck!

  • Yina  User Photo
    Yina • Data Scientist • Graduate Verified via LinkedIn
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    I highly recommend this 8-weeks intensive training at The Data Incubator (TDI), because it really helped me to go deeper into data science field and get fully prepared for the essential skills to work in a big data industry.

    As a PhD graduate in chemistry background, the transition from academia to industry is not easy. But fortunately, I attended TDI during Winter 2017, and I gained full stack from the program, including the cutting-edge analytics techniques, programming, machine learning, data visualization as well as business mindset. The networking with all other talented fellows is definitely a plus! Needless to say, my 1st data scientist job with a hiring partner in less than a month from graduation is the most valuable thing I got out of TDI!

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    As a recent graduate of the Winter 2018 cohort, going through the 8-week intensive data science training at The Data Incubator has taught me a great deal about various data science tools and has prepared me with the essential skills to thrive at my first data science job. I've gained a full data science stack, such as creating a web application, web-scraping, data cleaning, exploratory analysis and visualization, SQL, machine-learning, big data tools, and cloud computing, as well as a business mindset. More importantly, networking with and learning from other talented and brilliant fellows has taught me a lot about myself and how to become a great data scientist. More importantly, I made a lot of connections that I can see will be long term.

  • Tim Weinzirl  User Photo
    Tim Weinzirl • Data Scientist • Graduate Verified via Linkedin
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    I attended The Data Incubator during Spring 2017. I earned a data science position with a hiring partner in the San Francisco financial district within three weeks of graduating. Below I enumerate the many aspects of The Data Incubator I found valuable.

    Resume building:
    Starting at the semi-finalist level, applicants are provided strategies for resume writing. With some careful thought, I was able to portray seemingly bland parts of my academic background as eye-catching resume bullet points. Time is also dedicated in the first days of the program for polishing resumes yet again before final submission to the employer-facing online resume book.

    Professional headshot:
    Prior to the program, Fellows and Scholars are advised to get a professional headshot.  I had never done this before (or really had been aware of such services), but I realized it was an important part of going all in. While this can be expensive, Fellows who successfully join a partner company are reimbursed for the headshot (I was).

    Structured curriculum and weekly miniprojects:
    The data science curriculum includes lectures, daily coding challenges, and miniprojects. Weekly lectures are accompanied by IPython notebooks mixing text exposition with runnable code. There is a lot of lecture material to master every week, and persevering here helps with interviews and the miniprojects. The notebooks encapsulate the advanced features of scikit-learn, SQL, and big data tools (Hadoop, Spark), and they make for indispensable reference material after the program. The miniprojects are essentially problem sets and provide hands-on experience with these tools.

    The capstone project:
    This is meant to be an application of data science to a publicly available (or scrapable) data set that is ultimately presented as a web application.  It is adisable to have a rough draft, or at least a strong start, on the project before beginning the program, so start thinking about this before applying. There are several upshots to doing well on the capstone: 1) You have a recent data project to talk about in interviews that is more substantive than any of the individual miniprojects, 2) Practice building a web app (e.g., with Flask) for deployment on cloud services (e.g., Heroku), 3) Practice pitching your project in weekly video updates; for these videos, I learned how to edit video/sound with Openshot and to splice in images and screen capture footage of my project. 

    Soft skills lectures and interview practice:
    Soft skills lectures provide coaching for resume writing, onsite interviews, and salary negotiations. Weekly interview practice covers computer science and statistics problems of varying difficulty, both on pen/paper and in front of a whiteboard. 

    Summary:
    The Data Incubator is an extremely worthwhile experience. The components of the program outlined above have a snowball-like cumulative effect at turning academics into viable industry job candidate, commensurate with the effort they put into preparation before and during the program.

  • Aurora LePort  User Photo
    Aurora LePort • Data Scientist • Graduate Verified via Linkedin
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    TDI laid the foundation of knowledge for computer science and data science related concepts. The TDI curriculum provided me the breadth and depth of knowledge necessary to understand everything from software engineering and numerical computation to the use of programming tools (including Python and d3 for data visualization), Natural Language Processing, statistics and probability as well as the ability to apply parallel processing to data analysis. Importantly, TDI also prepared me for the technical questions asked during my data science interviews.

  • A game changer
    - 10/16/2017
    Luis  User Photo
    Luis • Research Scientist • Graduate Verified via Linkedin
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    Learning:

    The Data Incubator was an outstanding experience. I got to know and work with some of the brightest minds there are, the cohort included Ph.D's and PosDocs from Stanford, Duke, Johns Hopkins, Yale among others. We were all gathered in the same place to exchange ideas and tackle our very demanding weekly projects (a different relevant subject each week) while working on our final, capstone project.

    I was exposed so many different technologies, from big data to deep learning, from statistical analysis tools to machine learning, some of them I already knew and some were new to me.

    Interviewing:

    They have a great list from hiring partners all over the US (and many abroad), including very big names from Silicon Valley and Seatle.

    We had weekly interviewing practice, from hard to soft skills, from coding to communicating.

    Your success is their success, it is a win-win scenario.

  • Elham  User Photo
    Elham • Graduate Research Assistant • Applicant Verified via LinkedIn
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    Hi,

    I am a PhD student in Mechanical Engineering. For my career, I’ve gave it a lot of thoughts and I chose to be a data scientist, so I got my minor in statistics. I’m going to graduate by the end of 2017. While ago, I received an email about Data incubator. I though it’s a good opportunity for me to learn more about data scientist and it’s going to be great for my resume. The first time that I applied for Data Incubator, it asked for recommendations and resume and the next step was link to a test, when it was the time for the test, the link wasn’t working. They emailed applicants after couple of days and apologized for the technical problem and gave us new timing for the test which didn’t work for me. After 6 months, I received another email from Data Incubator which said they kept my recommendation and encouraged me to apply again. I did and this time I got to the test. The test was super time consuming. In one question the data was related to 600,000 people who applied for a loan from a company and they were looking for real answers like the fraction of 36 month loans to 48 month or Pearson correlation between return rate and interest rate and more complicated stuff. Only this question had 12 parts and each part took me lots of time to code in R and I had to copy my code to the exam page! Another question was proposing a project that could be a good reason for a company to hire me, Also, I had to provide the code for the project and presenting it and post it on Youtube and create the github version of it. As you can guess see it was lots of work. I went through all and it took me 3 full days to get it done and submit all they asked! Then, I received an email that I pass the exam successfully and I’m the finalist and should get ready for the video conference interview. In the interview, there were 5 other poor PhD students that worked hard to prove themselves. I thought I did good job in the interview. Any ways, few days ago, I got the result from Data Incubator. They said congratulations on my selection but as a scholar! What does it mean?! When I opened the acceptance letter they asked for $1000 deposit, and it's not all, I have to pay $15000 more before the program gets started in September! I have to work for them for free for 8 weeks and pay them $16000, plus I’m responsible for my place and meals. Just imagine how much renting a place for 8 weeks in large cities like San Francisco or New York is going to cost?!  

    $16000 is approximately my 12 months income since I’m still a student and I believe it’s pretty much the same for all other PhD students around the country! So, I think Data Incubator is just a modern way of scientific fraud.

    The fact is getting job in industry for a PhD student is really hard specially because PhD students are overqualified for most of the positions. Data incubator abuses this need of poor students and make them work hard for free and asks for $16000 for 8 weeks teaching material that you can access on Youtube for free!

    Another thing that you have to know is that Data Incubator sponsor companies are not willing to hire people who participated in the program because they have to pay to Data Incubator! Funny, isn’t it!  

    I got really disappointing after all I went through to pass many hard steps of their competition. I hope my sad experience helps you save your time energy and effort. Do not get trapped. My last advice: all we need is a little self-confidence, we are PhDs for God sake, we deserve to have good jobs in great companies!

     

    Response From: Alyssa Thomas of The Data Incubator
    Title: Director, Program Experience
    Wednesday, Aug 23 2017
    Elham, 
    Congratulations on being accepted as a Scholar this session! We know it can be hard to find out that you haven't been selected as a Fellow, but with a large number of applicants every session we have to make some very difficult decisions.  In the end less than 1% of our applicants are accepted as Fellows, and only about 2% as Scholars. Many of our most successful students applied to the program more than once before being accepted as Fellows, and we encourage anyone who is serious about a career in Data Science to keep trying! We also provide a blog post with application tips here: http://blog.thedataincubator.com/2014/09/how-to-prepare-for-the-data-incubator/

    While we understand your concern about the amount of work involved in the application - the work that our students do throughout the program is entirely their own. The Data Incubator does not own any rights to the work completed throughout the program, just the curriculum and training material itself, which is not available to anyone except our students.

    With regards to our hiring partners - over 90% of Data Incubator graduates are working in Data Science roles within 6 months of graduation. Our partners do pay a fee for hiring our graduates - this is why we can offer the Fellowship for free and it is a key component of keeping the program cost as low as possible for our Scholars. We are sensitive to the cost of the program though, which is why we offer a 50% tuition refund for scholars who are placed with hiring partners.

    We take our commitment to finding industry jobs for our Fellows very seriously, but that is also why we can only admit a small percentage of applicants. We strongly encourage all applicants to keep trying and improving their coding skills in the meantime!
  • Awesome program!
    - 6/8/2018
    Xia Hong • Graduate
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    Completing miniprojects on diverse and up-to-date topics really helped me to be confident about how to apply my technical skills on solving problems in practical situations. The hands-on experience from end to end, especially the relevance of the techniques to that in industry, is going to be a long term benefit for me and certainly for any previous and current fellow. 

    The opportunity to have conversation and build relationship with different companies. This is not only for landing a job but more for a healthy business relationship in a long term. Getting the benefit from the bridge built up by The Data Incubator between fellows and partners is one thing. Another important goal of networking is for future communication and collaborations. Here comes our Fellows. I kept in touch with some of the fellows after we finish the program and we keep each other posted. It is invaluable having fellows experience the transition from academia to industry together including sharing thoughts and helping each other.

  • Ryan Ferons • Software Engineer • Student
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    I attended the Data Science Foundation Class in winter 2018. The class was held online using Go to Meeting and Slack. Average class attendance was around 20. The teacher was always on time and there were very few technical inconveniences during lectures. The Data Science Foundation provided each student with a virtual server with class projects already loaded which greatly improved on-boarding times at the beginning. We were able to jump right into the material and not spend a lot of time trying to make everyone’s machines work.

    The class content was organized well. We started by reviewing python and learned pandas and other libraries required by the class. We then moved to statistics and probability. Then back to additional python libraries and finished up with a very brief introduction to machine learning. It being a foundation class it moves very quickly across the top of a wide variety of topics. Having come from a background in programming I felt very comfortable throughout the entire class. I did struggle with some of the math concepts. I felt that more time could have been spent in that area.

    Overall the class gave me an excellent foundation on which to continue learning data science. I use the tools and concepts learned in the class everyday at work and it has helped to advance me in my field. You won’t regret taking this course.

  • Anthony • Graduate
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    TDI gave me a lot of experience handling data in a way that I didn't get in an academic environment. The data sets were big, messy, and realistic. In addition, I thought that the capstone was an excellent way to get into a more industrial environment. The Data Incubator required a lot of database management, web scraping, and the like, which I didn't get in the academic setting I came from.

    I also felt that TDI gave me a number of excellent opportunities. It may seem frustrating at times, but the partners really do want to hire Fellows, and TDI's salary and compensation ranges are very accurate (in my experience). I'm not sure I would have gotten the same response rate and offers if I hadn't been applying through TDI.

Thanks!