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When I completed the Springboard Data Science Career Track curriculum earlier this year, multiple people have reached out to me wanting to know more about the program, curriculum and my experience with it. This post is a review of my experiences with the Springboard Data Science Curriculum, based on my personal experience. For a TL; DR version, feel free to jump to the pro’s and con’s listed at the end.

當我在今年早些時候完成Springboard數據科學職業跟蹤課程時，有很多人向我伸出援手，想進一步了解該程序，課程和我的經驗。 這篇文章根據我的個人經歷，回顧了我在Springboard數據科學課程中的經歷。 對于TL； DR版本，可以隨意跳到結尾處的專業人士和反對者名單。

These days, there are several options, both free and paid, available online for working professionals who want to make a career transition toward data science or just want to develop an additional skill set. I am hoping that this article will help those considering enrolling in a formal data science curriculum to get an overview of what they can expect from Springboard program. This should help those considering a formal program to better compare Springboard to other programs and make an informed decision about whether it aligns with their learning goals. Note that this review is based on my personal experience with “ Springboard Data Science Career Track” program which I took during late 2019/ early 2020.

如今，有幾種免費和付費的選擇，可供希望在數據科學領域轉向職業或僅想開發其他技能的職業專業人員在線使用。 我希望本文將對那些考慮參加正規數據科學課程的人有所幫助，以概述他們對Springboard計劃的期望。 這應該幫助那些正在考慮正式計劃的人更好地將Springboard與其他計劃進行比較，并就是否符合他們的學習目標做出明智的決定。 請注意，此評論是基于我在2019年末/ 2020年初參加的``Springboard Data Science Career Track''計劃的個人經驗。

Before diving into the course overview, I will start off with a little background about me and my motivation for enrolling in the program. Hopefully, this puts my observations in perspective. When I enrolled in the program, I have a PhD in Mechanical Engineering and was working as a process engineer in semiconductor manufacturing for around two years. At this point, I completed a few projects which introduced me to machine learning and made me realize the vast potential for AI based improvements in manufacturing. So, I decided develop my data science skill set and get more exposure to the field. I chose Springboard because it is more rigorous and holistic than completing various MOOC’s or learning from tutorials online while not as big of a commitment as a full time degree (and less expensive, too), Furthermore, I was able to complete it at my own pace while working full time. With this context, lets get into the overview of the program

在深入學習課程概述之前，我將首先介紹一些有關我的背景以及我參加該計劃的動機。 希望這能使我的觀察成為現實。 當我注冊該計劃時，我獲得了機械工程博士學位，并在半導體制造領域擔任過程工程師大約兩年了。 至此，我完成了一些項目，將我介紹給機器學習，使我意識到基于AI的制造改進的巨大潛力。 因此，我決定發展自己的數據科學技能并進一步接觸該領域。 我之所以選擇Springboard，是因為它比完成各種MOOC或在線學習教程更嚴格，更全面，而且不像全日制學位那樣投入大量資金(而且費用也不高)，而且，我能夠自己完成它全職工作時保持節奏。 在這種情況下，讓我們進入程序概述

計劃概述 (Program Overview)

The first thing that happens once you enroll in the program is that you will be given access to the program curriculum in your Springboard account. You can access the initial learning modules (except those that belong to the Specialization) right from the beginning. You will also be able to access the course tracker and other useful information like community forums, mentor call notifications and other student resources. The program has multiple resources and tools available to help you make the most out of it.

一旦注冊該計劃，第一件事就是您將獲得在Springboard帳戶中訪問該計劃課程的權限。 您可以從一開始就訪問初始學習模塊(屬于專業的模塊除外)。 您還可以訪問課程跟蹤器和其他有用的信息，例如社區論壇，指導電話通知和其他學生資源。 該程序具有多種資源和工具，可幫助您最大程度地利用它。

Throughout the program, the curriculum is designed to ensure that you are making progress on technical modules, career development modules and capstone projects simultaneously. Finally, one-on-one mentor support is one of the strongest selling points for springboard. Let’s go over each of these separately next.

在整個計劃中，該課程旨在確保您在技術模塊，職業發展模塊和頂峰項目上同時取得進展。 最后，一對一的導師支持是跳板的最大賣點之一。 接下來讓我們分別討論每個。

Technical Curriculum Modules

技術課程模塊

For technical curriculum, springboard does not develop its own material but organizes and provides access to other resources on the web, both free and paid. I felt the units to be logically organized and material to be well curated. Material start off with basic data science skills including data parsing, data manipulations, visualization and basic statistics before moving on to different machine learning topics.

對于技術課程，Springboard不會自行開發資料，而是組織并提供對Web上其他免費和付費資源的訪問。 我覺得各個單元要合理地組織起來，素材要精心策劃。 在開始學習不同的機器學習主題之前，材料首先要具備基本的數據科學技能，包括數據解析，數據處理，可視化和基本統計??信息。

I found that the material on machine learning gives a good overview of different types of models and helps build an intuitive understanding of how the model works. In general, the material seems to be aimed at introducing the various models, developing an intuition about how they work and making you use the model libraries in python. However, if you are looking for in-depth mathematical theory or implementation details about specific algorithms, you may not find that in the curriculum.

我發現有關機器學習的材料很好地概述了不同類型的模型，并有助于建立對模型工作方式的直觀了解。 一般而言，該材料似乎旨在介紹各種模型，對它們的工作方式產生直覺，并讓您使用python中的模型庫。 但是，如果您正在尋找有關特定算法的深入數學理論或實現細節，則可能在課程中找不到。

Being a career track program, the curriculum does a good job of covering additional skills required for data scientists by including modules on good coding practices, testing and debugging your code as well as productionizing data science models.

作為職業跟蹤計劃，該課程很好地涵蓋了數據科學家所需的其他技能，方法是包括有關良好編碼實踐的模塊，測試和調試代碼以及??生產數據科學模型的模塊。

Finally, the program gives an option to choose between three specializations, each of which will have different modules for about the last 30% of the course. The specializations available are for Data Science Generalist, Deep Learning and Natural Language Processing. I chose NLP as my specialization and did not look at the quality of the material in other specializations first hand. However, based on talking to other students, I understand generalist track covers models like recommendation systems in more detail. My observations are based on NLP specialization I chose.

最后，該程序提供了一個選項，供您選擇三個專業，每個專業在課程的最后30％左右都有不同的模塊。 可用的專業是數據科學通才，深度學習和自然語言處理。 我選擇NLP作為我的專業，但沒有直接了解其他專業的材料質量。 但是，基于與其他學生的交談，我更全面地了解了通識教育課程涵蓋的模型，例如推薦系統。 我的觀察基于我選擇的NLP專業化。

Capstone Projects

頂峰項目

I found the capstone projects to be very helpful to apply the skills I learn as a part of the technical curriculum. These are a good opportunity to develop a data science portfolio, which can be very helpful during job search, especially if you are planning to transition to data science jobs from a different field.

我發現頂峰項目對于將我學到的技能應用到技術課程中非常有用。 這是開發數據科學產品組合的好機會，這在求職過程中可能會非常有幫助，特別是如果您打算從不同的領域過渡到數據科學工作。

You are expected to select the first Capstone project early on and make progress on the projects alongside the technical curriculum. In addition to the code, you will also be required to document your findings as a report and a presentation as a part of final submission.

您應盡早選擇第一個Capstone項目，并與技術課程一起在這些項目上取得進展。 除代碼外，還要求您將調查結果記錄為報告，并作為最終提交的一部分進行演示。

If you are interested in applying data science to a particular domain, projects will let you pick a dataset from the same domain so you can get familiar with the data and common strategies for data wrangling and feature selection in that domain.

如果您有興趣將數據科學應用于特定領域，則可以使用項目從同一領域中選擇數據集，以便熟悉該領域的數據以及數據整理和特征選擇的通用策略。

Mentor Support

導師支持

One of the main selling points of Springboard is their one-on-one mentoring. When you enroll in the program, Springboard will pair you with a mentor based on your preferences and what you are looking for in the course. You will have regular 30-minute check-ins with the mentor for the duration of the course. Mentor is your main resource for keeping track of your progress, getting help understanding topics if you get stuck and for grading and providing feedback on your assignments and capstone projects.

Springboard的主要賣點之一是一對一的輔導。 當您注冊該計劃時，Springboard將根據您的偏好和您在課程中尋找的內容與導師配對。 在整個課程期間，您將定期與導師進行30分鐘的簽到。 Mentor是您的主要資源，可用于跟蹤進度，在遇到困難時幫助您理解主題以及為作業評分和提供頂峰項目反饋。

Based on mentor profiles on the Springboard website, I found that the mentors can have varying backgrounds, including those with advanced degrees in data science related fields as well as those with non-STEM backgrounds who successfully transitioned to data science. I suggest that you think about your career goals and what you are expecting to get out of your mentor before giving your mentor preferences. Technically, you do not get to select your mentor directly. Instead, Springboard assigns you a mentor based on your preferences and availability. However, I found the Springboard team to be helpful in trying to understand my preferences and changing the assigned mentor early on in the program.

根據Springboard網站上的導師簡介，我發現導師的背景各不相同，包括具有數據科學相關領域的高級學位的人員以及成功過渡到數據科學的具有非STEM背景的人員。 我建議您在給出導師偏好之前，先考慮自己的職業目標以及期望從導師那里得到的收獲。 從技術上講，您不會直接選擇導師。 相反，Springboard會根據您的偏好和可用性為您分配一名導師。 但是，我發現Springboard團隊在嘗試了解我的偏好并在程序的早期更改分配的指導者方面會有所幫助。

In addition to the assigned mentor, you will also get unlimited one off mentor calls. I found these to be helpful if you need someone else to give you a different perspective on a topic or if you find yourself stuck midway during the week and don’t want to wait till the next scheduled mentor call to get unblocked.

除了分配的導師，您還將獲得無限制的一次導師電話。 如果您需要其他人就某個主題提供不同的觀點，或者您發現自己在一周中途陷入困境，并且不想等到下一個預定的導師電話來暢通無阻，我會發現這些方法很有幫助。

Career Development Module and Career Coaching Calls

職業發展模塊和職業指導電話

The program has career development curriculum modules interspersed between technical modules and you are expected to work on them simultaneously. Material included is related to common career development topics like updating Linkedin profile, resume writing and informational interviews. As a significant part of job search is networking, you will be encouraged to develop your network through informational interviews, participating in meet-up’s and reaching out to other contacts in your chosen company or job profile.

該計劃的職業發展課程模塊插在技術模塊之間，您應該同時進行研究。 包含的材料與常見的職業發展主題相關，例如更新Linkedin資料，簡歷撰寫和信息采訪。 聯網是求職的重要組成部分，因此，我們鼓勵您通過信息面試，參加聚會以及與所選公司或職位資料中的其他聯系人聯系來發展您的網絡。

You will also be scheduling calls with one of the career coaches who can review your job search materials and strategy to give personalized feedback. As a part of the program, you will have access to career coaching and can schedule mock interviews up to six months after the end of your program, as long as you are actively searching for jobs.

您還將安排一位職業教練安排電話，他們可以查看您的求職材料和策略以提供個性化反饋。 作為該計劃的一部分，只要您正在積極尋找工作，就可以在職業生涯結束后的六個月內安排模擬面試，并且可以安排模擬面試。

Other Tools and Resources

其他工具和資源

In addition to those mentioned before, you can also attend weekly office hours to watch Capstone project presentations by other students or ask general questions. You will also have access to discussion forums or participate in peer study groups, which are a good resource for networking with other students and brainstorming project ideas. They also conduct Springboard Rise, an in-person conference/ networking event in SFO bay area.

除了前面提到的內容外，您還可以參加每周的辦公時間，觀看其他學生的Capstone項目演示或提出一般性問題。 您還將可以訪問討論論壇或參加同行學習小組，這是與其他學生建立聯系并集思廣益項目想法的良好資源。 他們還在SFO海灣地區進行現場會議/社交活動Springboard Rise。

Finally, to summarize, Springboard offers a holistic and well-curated program for anyone wanting to develop their data science skill set and pivot to a data science career. You can take advantage of multiple resources for professional development and networking while developing your technical skills and creating a project portfolio. However, the expected time commitment is obviously higher than a typical MOOC and you need to pass an initial skills assessment to be admitted to the program.

最后，總而言之，Springboard為想要發展其數據科學技能并轉向數據科學職業的任何人提供了一個完整且精心策劃的程序。 您可以利用多種資源進行專業開發和聯網，同時發展自己的技術技能并創建項目組合。 但是，預期的時間投入明顯高于典型的MOOC，您需要通過初步技能評估才能被錄取。

利弊 (Pros and Cons)

Pros

優點

Curriculum is broader and holistic compared to a MOOC class. So, likely more credible, especially for those for whom going back to school is not an option

與MOOC課程相比，課程更廣泛，更全面。 因此，可能更可信，尤其是對于那些無法重返校園的人而言

The course has opportunities to participate in discussion forums, peer study groups and attend in-person networking events, which are useful to learn more about the data science community and network with other peers who may be trying to get into the field as well

本課程有機會參加討論論壇，同行研究小組并參加面對面的社交活動，這對于了解有關數據科學社區的更多信息以及與可能也嘗試進入該領域的其他同行的交流非常有用

The curriculum is curated and organized logically. Very good for those starting out in the field and trying to quickly develop a foundation an all aspects of data science

該課程是按邏輯安排和組織的。 對于剛開始該領域并試圖快速建立數據科學各個方面的基礎的人員來說非常好

You will have accountability. There is a tracker as well as weekly check-ins with mentor. This will make it easier to work stay consistent through periods of low motivation

您將承擔責任。 有一個跟蹤器，以及每周有導師陪同的入住手續。 這將使在低動力時期保持一致的工作變得更加容易

Ability to pick a specialization. For the final few modules, you will be able to specialize in one of three tracks based on interests and career goals : Natural Language Processing, Deep Learning and Generalist Machine Learning

能夠選擇專業。 對于最后的幾個模塊，您將能夠基于興趣和職業目標來專注于以下三個領域之一：自然語言處理，深度學習和通用機器學習

Cons

缺點

Pricey. While its true you are getting many resources for the price, if you are unlikely to use everything, there may be better options available

貴 確實，您可以獲得很多資源，但如果您不太可能使用所有內容，那么可能會有更好的選擇

Course is developed primarily using Python and its libraries. If you prefer or looking to learn R or other programming language, this may not be for you

課程主要是使用Python及其庫開發的。 如果您喜歡或希望學習R或其他編程語言，可能不適合您

Experience with mentor can vary. The mentors listed on website have a broad range of backgrounds, from PhD’s in machine learning based disciplines to non-STEM majors who transitioned to Data Science. Make sure to let the Springboard team aware of your preference and what you are looking for so you can be matched with someone who can help you best

導師的經驗會有所不同。 網站上列出的導師背景廣泛，從基于機器學習的博士學位到過渡到數據科學的非STEM專業人士。 確保讓Springboard團隊了解您的喜好和所要尋找的東西，以便與可以為您提供最佳幫助的人相匹配

翻譯自: https://towardsdatascience.com/review-of-springboard-data-science-career-track-54bce8fcaf13

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