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Dartmouth Engineering Data Science Bootcamp

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Dartmouth Engineering Data Science Bootcamp

Avg Rating:5.0 ( 1 reviews )

Dartmouth Engineering offers a 24-week, online Professional Certificate in Applied Data Science bootcamp. Students should expect to devote approximately 10-15 hours weekly to the bootcamp. Time will be divided between recorded video lectures, coding video demos, assignments, attending office hours, group webinars, and meetings with a dedicated career coach and mentor.

Data Science bootcamp students will learn data visualization, machine learning, risk management, predictive capabilities, Python, regression models, and algorithms, such as K-means clustering and random forest. Students will develop a portfolio to share with potential employers. 

Data Science students will receive one-on-one career coaching, mentorship from industry experts, and access to regular webinars covering topics such as LinkedIn profiles, elevator pitches, interview goals, and developing rapport with potential employers. Access to the online learning platform, vidoes, and bootcamp materials is available for six months after bootcamp completion. 

Applicants to the Dartmouth Engineering Data Science bootcamp are required to pass an assessment to demonstrate their knowledge of calculus, linear algebra, statistics, and probabilities. Bootcamp students are required to have their own computer with Windows 7 or later, MacOS, or Linux. 

The Dartmouth Engineering Data Science bootcamp is fully graded,including assignments, quizzes, and a final project. Upon graduation with a cumulative score of 75%, bootcamp students will receive a Professional Certificate in Applied Data Science from Thayer School of Engineering at Dartmouth. Deferred payment and financing options are available to assist with tuition fees. 

Dartmouth Engineering Data Science bootcamp is powered by Emeritus.

Recent Dartmouth Engineering Data Science Bootcamp Reviews: Rating 5.0

all (1) reviews for Dartmouth Engineering Data Science Bootcamp →

Recent Dartmouth Engineering Data Science Bootcamp News

  • Professional Certificate in Data Science

    Apply
    Data Science, Data Visualization, Algorithms, Python, Machine Learning
    OnlinePart Time15 Hours/week24 Weeks
    Start Date June 29, 2021
    Cost$6,500
    Class sizeN/A
    LocationOnline
    Data Science bootcamp students will learn data visualization, machine learning, risk management, predictive capabilities, Python, regression models, and algorithms, such as K-means clustering and random forest. Students will develop a portfolio to share with potential employers.
    Financing
    DepositN/A
    Financing
    Lending Partner: Skillsfund

    Monthly Installment Plans available
    Getting in
    Minimum Skill LevelComfort with Calculus, Linear Algebra, Statistics, and Probability
    Placement TestYes
    InterviewNo
    More Start Dates
    June 29, 2021 - Online
  • Erik Miller  User Photo
    Erik Miller • Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    It took a while for me to pull the trigger on enrolling. What sealed the deal was the 50% tuition rate offered, which cut the price from what felt like an outrageous $12.5k to a much more reasonable $6k. I was out of work (at the start of the course) due to the pandemic so it felt like a good time to jump in.

    The course begins with an entry math quiz, which was reasonably difficult. It included things like linear algebra, high-level calculus, and advanced statistics. I had never taken linear algebra before but was able to learn quite a bit with Khan Academy. You don't need a perfect score on this entry quiz, but you do need to score around 80% to my recollection. 

    The course begins with a month-long Python coding bootcamp. This portion is technically ungraded and you can totally skip it if you have a good background in Python. All the learning occurs on the DataCamp platform while homeworks are completed in Jupyter notebooks. There is a final Jupyter project for this Python coding section which IS graded. 

    The rest of the course is split into 10 modules. Each module consists of two weeks. The first week consists of recorded lectures, some required textbook reading, a one-hour live office hour, forum discussions, and a coding assignment. The second week typically consists of math-based video lectures, a math quiz or two, and a live coding assignment done together as a class and then finished/submitted on your own. The modules are as follows: Practical Applications of Analytics, Data Structures and Plotting, Introduction to Statistics and Probabilities, Linear OLS Models, Linear Regressions Interactions and Transformations, Logistic Regression and Applying GLM, Data Visualization Strategies, Experimental Design/Causal Research/Targeting Analysis, Machine Learning and the final module is the final project. I found all the modules to be very valuable and a good mix of new challenges/repetition. 

    The final project basically consists of identifying a potential business problem, curating an applicable dataset, and applying machine learning techniques to attempt to solve the problem. You will use Python and relevant ML libraries (mainly sklearn, but others can be used). The trick to the final project is finding something relatively novel that is also doable. There was a broad range of success/failures among my cohorts' final projects. Some people picked things that were too simple and were basically considered mainstay ML problems (ex. identifying mushrooms/flowers based on measurements) while others picked ML problems that were way too big in scope or ill-defined. 

    The course is also jam-packed with career-oriented learning opportunities. There are multiple sessions with career coaches and assigned mentors, both group and one-on-ones. Be sure to take full-advantage of these, especially if you are planning on breaking into a new data science role. 

    Expectations:
    Expect to spend at least 13 hours a week on this course. I'm definitely not a slow-learner and I felt 13 hours was my average. Expect to spend even more time if you have any foundational knowledge deficits such as calculus or statistics. 
    Do not expect to become a pro after just this course. It will give you all the necessary skills and knowledge to further your data science journey, but there is much more to do after. I cannot speak to whether it provides everything needed to become competitive for entry-level data science jobs, but I imagine it does, especially at the larger firms like Amazon, AirBnB, etc. 

    The things I didn't like: 
    I wish we had more access to actual Dartmouth faculty members. The only actual Dartmouth faculty member you will see will be in the pre-recorded lectures. All live interactions occur with an Emeritus employee. He does a fantastic job, but you can tell he is teaching multiple courses at once. Don't expect office hours to last more than the allotted hour. 

    Secondly, I was a little disappointed in the range of knowledge/skills among course participants. It felt like far too much time was dedicated to subjects/knowledge that was included in the entry quiz. What's the point of the entry quiz if we're going to end up going over most of that stuff anyway? It just kind of felt like wasted time and I wasn't getting my full moneys worth reviewing stuff that I felt was foundational. 

    Third, I wish we had spent a lot more time on machine learning as opposed to the more basic stuff. Again, basic statistics were tested for in the entry quiz. Sure it was nice to learn how to code linear regressions in Python, but the meat and potatoes should have been ML. After all, you really only spend two weeks on the ML stuff and it felt rushed since there is so much to learn in the ML world. 

    Similarly, I wish we had done the ML stuff earlier in the course or at least touched on it more frequently throughout the course. Ultimately, your entire final project is based on ML. But it's a little annoying to be expected to come up with a final project when you don't even really understand the capabilities of ML, to begin with. I know I felt a little lost and in the dark.  

    Overall, I highly recommend this course to anyone interested in data science.