Data Application Lab

This program was very challenging to me. There were times that I thought I would not make it through and wanted to quit. I had to remind myself that this program is only introducing me to the various plethora of tools in data analytics. Some tools I have heard of and other I had not. There were some of the concepts and tools that I would never seek out and there are those I want to learn more about. To be successful in this program, you will need to be committed. You need to understand that there will be things you will not understand but you need to push through it. Use all available resources. I like attempting the challenges and when stuck, I will have something for my tutor to help me resolve. That is something I did not realize until later in the program. You will definitely need two monitors! Attend office hours before and after class. The instructor and TAs were very helpful when I had an issue. Set your time for working on the assignments and stick to it.  You have to remind yourself that it is only temporary and not a life sentence. Read through the requirements of the challenges before submitting. Take advantage of resubmitting your corrected assignments. Realize that the instructor is fallible and the topics maybe challenging them to teach. They have topics that they are more knowledgeable about than others and this program is the only teaching experience they have. This program will solidify soft skills such as: critical thinking, problem-solving, time-management, and team building. As challenging as it is, try your best to remain until the end. It's completing and not always about getting the 'A'. Read less

ds课程教了很多industry related knowledge， 是之前在学校课堂里没有接触到的。而且老师都是一线公司的data scientist， 讲的知识对于找工作很实用。一开始会讲ds各个模块的基础知识，每节课后会有一个mini project，可以很好地训练coding能力并且增强理解，而且ta也会讲作业。基础知识讲完后，课程有kaggle competition，fintech，Recommender System，NLP，这些project对完整做一套feature engineering，data analysis，model building 很有帮助，而且老师会给很多tips。同时，课程还包括data scientist interview，会总结很多面试题目和技巧，还可以自己约老师一对一辅导面试~~~ btw，老师声音都很好听~而且很幽默~😝，把知识讲的很容易理解~

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I took the DS bootcamp during October, it was a great bootcamp which had almost all industry requirement to build up your technical skills and helps you go through techncial interviews. If you are really looking forwards to step into the data world, the bootcamp could be your choice. It can also helps you get familiar with some big data platform such as Hadoop, Spark and Hive. The most effective way is the instructors will take you into some experiences of the Kaggle Competiiton, whihc will ensure your Python skillls and furthermore.

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This is an overall good learning experience with an intense schedule.

The most important highlight is its practical view of the industry, as the materials comes from real world data and projects and the instructors have in-depth knowledge and experiences in industries.

Also, with a diverse composition of students, some whom are students like me and some of them are young professionals, I also gain insights of how the career development process could be like.

As a plus, the course also include ineterview trainning sessions, which is useful. Those suggestions from past experiences are good starts for my preparation for job interveiws.

In all, this course is a bridge for me, as a graduating student, to the real world working environment.

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Though a couple of teachers are pretty good, the overall course fails short of my expectations,

1. the big projects aren't very helpful. some teacher are not familar with the project thus can't help the issues we encountered.

2. The boasted clusters are only for limited usage ( hive query ), it is not for big data experiments such as spark jobs, etc.

3. the sales guy were vague about the choice of elective projects but actually it is very rigid, you can only choose 2 projects.

4. there is limited chance to get an internal reference.

5. very limited help from instructor after the course. You get answers mostly from other students and teaching assistant. rarely from instructors.

Overall, I don't feel it worth my money.

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        作为DS program 4月班的学员，最深刻的感受是这个项目最够的practical, 首先从师资角度，讲课的老师来自于一线互联网公司的data scientist,手把手教大家做数据预处理和跑模型，其次，采用业界流行的python语言进行示范，并且涉及到hadoop,spark,hive的工具使用操作，值得一提的是在project 模块中，通过老师的指导，kaggle比赛很有可能取得不错的成绩，最后的end to end project会按照企业的需求，从数据的抓取、数据的清洗到最后的模型调优和预测，完整走完一遍流程。因此，通过理论和project的结合，会把我们短时间内expose在data science的领域中，相信踏踏实实跟下来，定会有所心得~

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I really recommend the business analytics course and data scientist course at Data Application Lab.

I took these bootcamp in 2018. The courses are perfect for students who want to pursue a career in data science field but don't have education background. 

I did several hands-on projects in the bootcamp, in which we would use statistical modeling and machine learning algorithms to solve real business problems. Those projects have allowed me to drastically expand my data analytics capabilities and programming skills.

In the data scientist course, the instructor would teach us how to master the Kaggle competition with the advanced algorithm and innovative models, such the motheds are unique from industry that I couldn't find anywhere else online. I participated the Kaggle competition with the guidance of the instructor and the TAs, and got an awesome place. Finally, I got a lot of job interviews because of the Kaggle experience I put on my resume.

 

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I started the DS course in last August. In order to balance between my schoolwork and the DS training course, I made a lot of efforts.  The instructors taught a high volume of DS knowledge every class. I need to review and practice in time to remember all the points they made. If I got questions, the teaching assistant would kindly help me. There are two Q&A sessions every week. What I found most valuable about the course is the high-profile data science projects, which guided me to apply the knowledge in practical areas. Most importantly, the program also includes resume critic and job referral opportunies which helped me get my first Data Analyst internship. I really appreciate the service and help accompanied with the course. 

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