Data Science Bootcamp

They follow the same curriculum. The full-time program is for learners who have time to study full-time and complete the bootcamp in 12 to 14 weeks. The part-time program is 26 weeks long and is suitable for learners who have a full time job and want the flexibility. Both options are intense and require dedication.

Data engineers help the organization ingest, collection, and transform large amounts of data. Data engineers deal with raw data and understands data models very well. The output of DE’s work are usually data warehouse or data lakes that store cleansed and transformed data. Data scientists and analysts will consume those data for business intelligence and machine learning/predictive analytics. Data analysts will focus on analyzing data to address ad-hoc business questions. They are good at SQL and database queries and know the business metrics well. Data analysts will also build KPI dashboards that are consumed by business teams and executives for day to day operations. Data scientists perform advanced analytics and machine learning to help the business address predictive questions. It goes beyond answering the what and why questions, and help business make personalized recommendations to improve sales, identifying high-risk transactions to prevent loss, finding good customer segments to generate more revenue, etc.

Yes. It’s possible to watch the recording only without support. It is also possible to watch the recordings and get learning and career mentorship. Please talk to our learning advisors for more details.

Whether you go with a full-time or part-time Bootcamp depends on a few factors. 1. are you able to afford quitting your current job? If not, go with the part-time Bootcamp. 2. How soon do you want the career switch to materialize? If you want it to happen sooner, full-time is a better option. 3. Do you have the grit to have a full-time job while grinding through a busy part-time Bootcamp? If yes, maybe part-time is a better option. Otherwise, full-time Bootcamp gives you the disciplined environment you need to push forward. Quitting your full-time job is generally not recommended during a tough job market or when the market is not bullish. Financial stability is very important and make the decision to quit your full-time job only when you can afford 6-12 months of runway, when your existing busy job doesn’t allow you the luxury to study part-time, or when you need the handholding and concentration to fully immerse yourself in school for a few months.

WeCloudData offers a unique project-based learning approach. Unlike other bootcamps, we give students the opportunity to work on real-life consulting projects as part of the learning journey. The real projects help students gain confidence, show real experience on the resume, get more interviews, have real experience to talk about during the interviews, and adapt to new jobs quickly.

Learners joining the data science bootcamp don’t need to have strong data science experience. The bootcamp is supposed to prepare you for the job market. However, being able to demonstrate basic data handling skills using excel or experience working with tables and spreadsheets are the minimum requirements. Basic data sense and logical thinking is also important. There will be a screening test that the learners need to pass in order to get admitted into the program. If you come from a non-tech, non-STEM, non-CS background, it’s totally fine. However, pre-bootcamp preparation is the key. We highly recommend learners take the Data Fundamentals course before joining the data science bootcamp. It equips them with the right skills to become successful in the bootcamp. If you won’t be available for the data fundamentals course, we recommend you take the free SQL and python courses at your own pace. Completing the data fundamentals course also give learners the opportunities to be eligible for additional scholarship because it shows the commitment to success.

Students who have graduated from the data science bootcamp are a good mix of backgrounds. About half the cohort will be career switchers and professionals who don’t come from data and tech background. 30% of the learners are recent graduates. Some of them are from CS, statistics, and engineering background. 10-20% of learners already have some data analytics skills via self-learning or previous work and they are in the bootcamp to up-skill and learn more about machine learning and big data. About half of the learners have a bachelor’s degree and 40% have a Master’s degree and 10% have PhD background. Almost everyone’s goal is to land a job in data science.

To get the most up to date information about the Data Science job market, we recommend you check out the Job Market Report and blog posts on our website. Typically, data scientists are expected to be paid an average base salary of $120k USD in the U.S. and $100k CAD in Canada. Senior data scientists can be compensated well over $150k USD.

The job market demand for data scientist is very high. It is getting very competitive because there’re more junior data scientists competing for entry level jobs, which are somewhat limited in a tough job market in recent years. However, as an applicant, all you need is an advantage/edge to beat 95% the career switchers and junior DS out there. The best way to achieve this is through project experience building. As long as you have the right skills and relevant industry experience you will have a very big advantage. This is exactly what WeCloudData’s project-based learning can offer. Our 1-1 career mentoring service will also make sure that you have the right interview experience to stand out to employers.

PhD is not required for all DS jobs. Only about 12% of the data scientist jobs specifically mention PhD in the description. 40%-50% of the DS jobs require a minimum of Bachelor’s degree and 30% of the DS jobs require Master’s degree. Of course, having a higher degree will always be an advantage but employers are more interested in experienced candidates. So if you have strong project portfolio and even real project experiences, it weigh much more than education background.

Yes, statistics is important knowledge to have if you want to become a data scientist. But there’s no need to complete a statistics book or course. It’s more important to understand how to use statistics for specific data science scenarios such as AB testing, understanding metrics, understanding statistical learning algorithms. Those don’t require very deep knowledge of statistics and can be picked up quickly. The bootcamp will prepare you for math and statistics. We also highly recommend learners go through the pre-bootcamp.

Most career switchers who don’t have STEM background will have doubts. Based on WeCloudData’s experience, lack of tech background can be quickly overcome by working on practical projects. There’re definitely learning curves to overcome during the bootcamp but that 3-6 months will be worth the effort. Considering that it will have a profound impact on your future career path, it’s worth the pain! Getting into data science or other data professions give you exposure to some of the best job opportunities and you will also have more options to become product managers and project managers in this space. You can also go pretty far down a technical career path. WeCloudData will give you the support and care you need along the way and help you become successful.

Yes. The recent hype in generative AI is real. We’ve seen the power of ChatGPT. Some have fear that jobs like data scientist and analyst will be replaced by ChatGPT. However, one needs to realize that chatGPT is just a language model. It won’t be able to replace a data scientist any time soon. Of course, there’re code interpreters that can even generate code to do analysis, but real analytics tasks require understanding of business context, rules, and data itself. Generative AI is not close when it comes to reasoning and analytics and to create real business value we still need data scientists. The demand for DS and AI talents will also increase due to many business starting to realize the power of data and AI. Being able to write a few lines of code DOES NOT equal data science. Data scientists often need to exert influence on decision making and having emotional intelligence is also extremely important. That is not something ChatGPT can do. But it will help data scientists become more efficient so we should all be adapting to it.

This course is designed to be very hands-on. It’s impossible to become good at data science without actually digging into the data, analyzing it, and developing solutions that will impact business. There will be lots of lab exercises and homework to keep students busy and the projects will help learners gain practical experience. If you prefer a more academic environment, we recommend you consider a Master’s program. If you want to gain practical experience and build portfolio projects, this is the perfect course.

Learners in the part-time program usually spend 15 to 20 hours each week (including the lectures and labs) and learners in the full-time program usually spend 40-60 hours per week.

Yes, during the regular weeks we have office hours and labs sheets students get to follow labs and ask questions. During the project weeks, students will join the project mentoring sessions to interact the project mentors.

Yes, all learners in the full-time program are eligible to work on real client projects. There are two types of projects: personal projects (also called capstone or portfolio projects) and real client projects. All students in the course will need to complete the capstone project. The real client project is a different training and career service offered at WeCloudData via our partner Beamdata. Learners will become a trainee and receive project-based training. Learners will be assigned to a real project team to work with clients and get mentored and trained by our project managers and project leads. It’s a great learning opportunity and also allow the students to gain real experience to stand out in the job market. You can talk to our learning advisors to find more details.

Yes. The Data Science Part-time Bootcamp comes with 6 months of career mentorship. During the bootcamp, we host resume workshops, interview preparation sessions and help students with their resumes and portfolio projects. After the Bootcamp, students will enter a 6-month career mentoring phase where they work with an industry mentor on a one-on-one basis for job search. Mentoring sessions are one of the most important advantage of WeCloudData’s bootcamps. It offers services beyond resume help and the mentors are all data industry professionals instead of regular career counselors.

Our career services include: resume workshop, interview workshop, industry guest lectures, job recommendation and referral, 1-1 career mentoring.

You can find our alumni reviews and stories by visiting the student success pages.

Upon successfully completing the Bootcamp, Canadian learners will get a diploma issued by WeCloudData Academy (under Ministry of Education). Please contact our learning advisor for more details. For US and international learners, you will get a Bootcamp certificate.

Yes. Scholarship is available for students who meet the requirements. A scholarship test needs to be completed and the learner needs have a 20-minute live assessment with the program manager. Alumni who have completed courses that meet the pre-requisites will also be eligible for scholarships. We also provide newcomers, new grad, and women-in-tech scholarships. Please contact our learning advisors for details.

Yes. We offer flexible payment plans and work with 3rd parties to offer loans as well. Please fill out the inquiry form to access the course package page. It has the payment plan details. Or you can contact our learning advisors for more details.