If you want to advance your career in Machine Learning and AI, WeCloudData’s Machine Learning Engineer bootcamp can help you achieve the goal. We’ve developed a well-structured curriculum that not only teaches students SOTA ML algorithms, but also help students learn by implementing hands-on projects, build real client project experiences, and receive one-on-one career mentorship to land machine learning engineer jobs. WeCloudData’s bootcamps are consistently ranked among top coding bootcamps. Inquire today to become a machine learning engineer with WeCloudData.

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Part-time

Online Live

6 months

260

Upcoming Start Date

Feb 22

Registration Deadline:

February 22, 2024

Select a start date that fits your schedule

About the Program

The Machine Learning Engineer Diploma Program (ML Engineer Bootcamp) is a part-time learning program that helps data scientists, software engineers, and career switchers break into Machine Learning Engineering. The learning package includes 26 weeks of part-time study, six months of project experience building, and six months of mentorship and job support after graduation. WeCloudData is committed to an extensive support period to help our learners achieve their career change/kickstart dreams. If you want to advance your career in AI/ML and MLOps, the up-skilling program will help you build the experience you’ll need to successfully achieve your goal.

POTENTIAL JOB TITLES

Machine Learning Engineer

Artificial Intelligence Engineer

Machine Learning Developer

Data Scientist

Machine Learning Software Engineer

MLOps Engineer

Machine Learning Operations Engineer

Software Developer (MLOps)

WeCloudData is the perfect place to grow your career

Choose your network & mentor wisely

Interacting with expert instructors, engaging with classmates, working on group projects, meeting with real clients and networking with a community of like-minded professionals. You'll be able to build your network and collaborate with people from all backgrounds, strengthening bonds and making friends in the process!

Solving real-world problems

In our bootcamp, we'll give you an opportunity that many graduates don't have: work on something meaningful and important right away. You will be able to even contribute ideas or solutions that make an impactful change! Teamwork is an essential part of data career. In our bootcamp, we'll have you work with other students and a Project Manager to complete a Real-Client project.

Comprehensive bootcamp with a focus on skills that are in high demand

No other bootcamp offers the flexibility and variety of topics, the number of hours and instructors, and the depth of knowledge in this industry. WeCloudData is a one-stop destination to learn data science - from basic concepts to building data-driven applications. Your learning is personalized and all your questions are answered by our expert instructors.

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Ranked #1 Data Training Program

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A path more than just courses

BOOTCAMP

Learn cutting-edge skills from expert instructors

PROJECT

Gain hands-on experience with Real-client Project

career

Get 1-on-1 mentorship from our professionals

REAL CLIENT PROJECTS

Gain Hands-on Experience with Real-Client Projects

The data & AI job market is highly competitive. It’s hard to stand out and get noticed without relevant background and experience. This program focuses on applied deep learning techniques with a relentless focus on hands-on projects and MLOps. WeCloudData has gone the extra mile to bring real-life projects into the classroom. Our Part-Time ML Engineer Bootcamp students will work on real business scenario-based projects.

CLIENT

Supply Chain AI Startup

project

Demand Forecasting for Supply Chain

We worked with several supply chain AI startups implement deep learning-based forecasting models to help the supply chain companies improve demand forecasting accuracies.

CLIENT

Real Estate AI Platform

project

Model Deployment on Google Cloud

In this project, we helped a real-estate AI platform improve model performance and deploy the MLOps solutions on GCP.

Portfolio Project

Build real project experience to differentiate

We also have a capstone project that gives our students the chance to synthesize their learning and build a portfolio piece they can showcase on their resume or LinkedIn profile. This helps them stand out from other applicants when applying for jobs or opportunities.

LEARNING EXPERIENCE

Drive Success with Interactive Learning Experience

Weekly Schedule

Monday

Self-paced Learning

Learners work on material review and take-home exercises. Students who cannot attend live classes will watch the recordings, complete labs, and ask questions on Slack.

Tuesday Evening

Office Hours (7pm-9pm Eastern)

Learners attend weekly live office hours to ask questions. Our teaching assistants will provide 1-1 help on lab exercises and give project mentoring as well.

Wednesday

Self-paced Learning

Self-paced Learning (same as Monday)

Thursday Evening

Lecture (7pm-10pm Eastern)

Learners join live zoom sessions to learn new topics of the week. The instructor will spend 2 hours on theory, use cases and 1 hour on hands-on demo. Students are encouraged to follow along and ask a lot of questions.

Friday

Self-paced Exercises

Self-paced Learning (same as Monday)

Sunday Morning

Lecture (9:30am-12:30pm Eastern)

Sunday Afternoon

Lab (1:30pm-4:30pm Eastern)

During the lab session, our lab instructor will guide learners through hands-on lab exercises. Students can ask lecture related questions and also work on peer challenges. If the learners have more questions after the lab session, they can talk to the TAs on slack or attend weekly office hours.

* Mon-Fri Regular Hours

Real Client Projects

* For learners who are also enrolled in the real projects, they work with our project leads and real life projects. Full-time job seekers will spend more time during the week and professionals with full-time jobs will collaborate asynchronously with the project team throughout the week.

Bootcamp Journey

FACULTY TEAM

Learn from the best instructors & TA

We’ve brought together a team of highly skilled and experienced instructors to help you learn effectively. Our instructors have a passion for teaching and a wealth of real-world experiences in their respective fields, so you can be confident that you’re learning from the best.

Holly Xie

Machine Learning Scientist @ Integrate.ai

Holly is currently a Senior Applied Scientist at Xero. She has a background in Math and Statistics from University of Waterloo and worked as a Machine Learning Scientist at Intergrate.ai. She is passionate about machine learning and computer vision and has been teaching the Machine Learning course with WeCloudData for the past 3 years.

Yi Zhang

Senior AI Applied Research Scientist at ServiceNow ATG

Dr. Yi Zhang is a Computer Scientist specializing in NLP, Machine Learning, and AI, working as a Senior AI Applied Research Scientist at ServiceNow Advanced Technology Group. He has years of academia and industry experience and has architected solutions to convert the latest technology to robust Business Intelligent. Prior to his previous experience as the Head of Engineering at NLPMatics, he also assumed team lead role at Tencent and Research Scientist at Chan Zuckerberg Institute.

Farnoosh Khodakarami

Machine Learning Researcher, Cyclica

Experienced computer scientist with a demonstrated history of working in the research industry. Skilled in application development with experience in machine learning applications. Strong research professional with a Doctor of Philosophy (Ph.D.) focused in Computer Science from the Amirkabir University of Technology – Tehran Polytechnic. Creative, self-motivated and committed to working with a team-player attitude, great problem-solving skills and ability to quickly grasp new concepts.

Ali Madani

Machine Learning Team Lead, Cyclica

Ali is the head of machine learning at Cyclica Inc. and leads the team to further improve Cyclica’s deep learning technology for predicting interaction between ligands and target proteins. As a computational biologist and machine learning specialist, Ali has worked on a series of scientific articles in high impact scientific journals and international conferences covering such fields as transfer learning, dimensionality reduction and unsupervised clustering. He earned a Ph.D degree from the University of Toronto, and a master of a mathematics degree from the University of Waterloo.

Rhys Williams

AI Engineer @BeamData | AI Instructor @WeCloudData

Rhys Williams is the lead AI engineer at BeamData. He is an AI and Robotics lover. He’s been teaching the Python for Data Science courses with WeCloudData and currently is in charge of the Deep Learning Capstone project course. He loves robotics, raspberry pi, deep learning, and has developed his AI robot Mr-B. Check out Mr-B’s videos on his Instagram: https://www.instagram.com/rhysdgw/

CURRICULUM

Be ready for the new economy

WeCloudData Bootcamps are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends.

Module 1

Neural Networks & Deep Learning

The first module in this course begins with a quick machine learning review, and then introduces the basics of neural networks and commonly used deep learning frameworks such as PyTorch, Torch Lightning, and Tensorflow.

LEARNING OUTCOMES

Review Classical ML algorithms such as Linear Regression, Decision Trees, XGBoost, Random Forest, and K-Means Clustering
Review Feature Engineering techniques and ML Training Pipeline
Understand neural networks internals such as MLP (backdrop), optimization (SGD, Adam, etc.), regularization (batch normalization, dropout)
Learn and apply neural networks using PyTorch
Train and tune basic deep neural networks for classification and regression problems

Key Skill:

Deep Neural Networks, MLP, Optimization, ML Pipeline, Model Training, Model Tuning

Tools:

PyTorch

Lightning

Tensorflow

Colab

Pandas

Numpy

Tools:

OpenCV

PyTorch

Detectron

HuggingFace

Module 2

Computer Vision

This module focuses on the Computer Vision applications of deep learning. It covers the fundamentals of Convolutional Neural Networks and different CNN architectures, teaches image augmentation and processing using TorchVision and OpenCV, and introduces common CV tasks such as image classification, object detection, semantic segmentation, image augmentation, transfer learning, and generative models such as neural style transfer.

LEARNING OUTCOMES

Learn the fundamentals of deep convolutional neural networks
Get hands-on with various CNN architectures such as AlexNet, VGG, Inception, RestNet, and Xception
Apply CNN to solve image classification problems
Apply YOLO and R-CNN to solve object detection problems
Apply FCN and DeepLab algorithms to solve semantic segmentation problems
Learn how to label and augment image data using various tools

Key Skill:

CNN, Computer Vision, Convolutional Neural Networks, Image Augmentation, Image Classification, Object Detection, Semantic Segmentation, Instance Segmentation, Neural Style Transfer

Module 3

NLP (Natural Language Processing)

This module teaches SOTA NLP methods and applications. Students will learn some of the most exciting new development in modern NLP such as Transformers and GPT-3.

LEARNING OUTCOMES

Learn basic text-processing techniques
Become familiar with traditional NLP methods such as N-gram, topic modelling, text clustering, NER
Learn word embedding techniques such as skip-gram, word2vec
Sequence-to-Sequence models with recurrent neural networks (RNN) and LSTM
Apply state-of-the-art (SOTA) attention models such as BERT transformers for transfer learning
Apply generative models such as GPT-3 for text generation and Question-Answer systems
Build a mini-ChatGPT application using GPT-3 and PyTorch

Key Skill:

Text Processing, Natural Language Processing, Large Langage Models (LLM), Topic Modeling, Transformers, BERT, GPT-3, Roberta, Text Classification, Sentiment Analysis, Search Engine

Tools:

HuggingFace

PyTorch

GPT-3

Tools:

AWS

Ubuntu

Linux

Docker

Kubernetes

Module 4

ML Infrastructure

This module introduces the infrastructure tools required for building scalable and robust machine learning solutions in production. Topics include Linux, Docker, AWS, and Kubernetes.

LEARNING OUTCOMES

Get comfortable with Linux operating systems and command line
Launch Compute instances on Cloud infrastructure such as AWS EC2
Learn the basics of virtualization and docker containers
Build docker images using docker compose
Learn Kubernetes and container orchestration fundamentals

Key Skill:

Docker, Linux, Machine Learning Infrastructure, Cloud, AWS, SageMaker, Kubernetes, Serverless

Module 5

Model Engineering

This module teaches students how to build machine learning training and evaluation pipelines. Students will refresh their ML knowledge and learn how to build baseline models and detect issues in the model/feature pipelines early on, and then work with model experiment frameworks such as MLflow and Weights & Biases. Model interpretation and validation will be covered extensively before students learn how to package models using various formats.

LEARNING OUTCOMES

Understand end-to-end machine learning pipelines
Gain hands-on experience with custom feature transformers
Learn how model tracking and monitoring tools work in real life
Learn how to build baseline models and detect issues in data and models
Learn how to package machine learning models using different formats such as ONNX

Key Skill:

Model packaging, ML Pipeline, Model Experimentation, Model Validation, Model Monitoring, Baseline Model, Feature Transformer

Tools:

W&B

Sagemaker

ONNX

MLflow

Tools:

VSCode

FastAPI

Nginx

Serverless

API-Gateway

Lambda

Kubeflow

SageMaker

Ray

Module 6

ML Software Engineering

This module teaches students the necessary software engineering skills for model deployment. Students will learn the basics of web applications, REST APIs, model serving and inference. Students will not only learn how to create inference APIs but also how to deploy the prediction services in a local docker container, AWS Lambda, Sagemaker, as well as AWS ECS/Fargate. The scaling part will be introduced at a later module.

LEARNING OUTCOMES

Learn the fundamentals of web applications and Microservices
Learn how to build and deploy basic Python-based applications using FastAPI and Flask
Learn how to create an inference API using FastAPI
Learn how to package and structure ML projects
Learn how to deploy ML Models in different types of infrastructures

Key Skill:

Model Serving, Prediction Service, Inference API, Model Deployment

Module 7

ML Operations

This module teaches students the DevOps part of MLOps and ML Engineering. Students will learn the basics of Data Version Control, CI/CD, infrastructure scaling, and infrastructure automation.

LEARNING OUTCOMES

Learn the principles of MLOps
Learn how CI/CD pipelines work in the Machine Learning context
Version control data for machine learning using DVC
Practice infrastructure automation and scaling using Terraform, AWS Cloud Formation, and Kubernetes
Learn how to build and maintain feature stores using Feast

Key Skill:

Continuous Integration, Continuous Deployment, CI/CD , Infrastructure as Code, Automation, Data Version Control, DVC, Feast, Feature Store, SageMaker, Vertex AI, Terraform, Kubernetes

Tools:

Docker

K8S

Feast

DVC

Git

Github-Action

Kubeflow

SageMaker

Terraform

Tools:

Leetcode

Hackerrank

Presentation

Module 8

Career Preparation & Mentorship

Before entering the 1-1 career mentoring, students will learn about the ML Engineering job market and build job search skills. Career coaches will teach graduates how to structure resumes, apply for jobs, and ace the interviews. Students work in groups for peer mock interview practice.

LEARNING OUTCOMES

Career services included in the bootcamp include

Resume workshops
Group interview practice
Portfolio project mentoring
Coding interview practice and additional resources (Leetcode/hackerrank)
Peer programming practice and code reviews

Career services included after graduation (6 months)

One-on-one career mentoring sessions with data scientists and ML Engineers for 6 months after graduation
One-on-one resume critique
One-on-one mock interview sessions with career mentors
Job referrals and networking sessions

Key Skill:

Leetcode, Data Structure & Algorithms, System Design, Communications, Presentation Skills, Business Acumen

SCHEDULE

Upcoming Start Dates

US (East)

US (West)

Toronto (ET)

Vancouver (PT)

US (East)

US (West)

Toronto (ET)

Vancouver (PT)

View Tuition, Financing Options, and Scholarship in the Program Package

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First Name

Last Name

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Career Services

Career success takes more than just courses

Taking courses alone don’t guarantee career success. WeCloudData’s career mentoring service, community events, and workshops are top-notch! We put in lots of effort outside of the classes to help learners grow their knowledge, confidence, job skills as well as network.

1-on-1 Mentorship

Available in all bootcamp programs, the career mentorship service helps close job market knowledge gap and provides the support our learners need to land a job.

Networking & Community

WeCloudOpen is a community built for tech leaners, practitioners who want to share thoughts, tips, and best practices with fellow learners and grow together.

Events & Workshops

Catching up with the latest tech industry trends by attending WeCloudOpen Workshops and community events. Learn practical tools and always stay relevant.

OUR ALUMNI ARE WORKING AT

OPEN RESOURCES

Start Learning With WeCloudOpen

WeCloudOpen is here to help you unlock your full potential in tech, with our free courses and workshop. Learn the fundamentals of coding and data, and become a proficient tech professional in no time!

WeCloudOpen Course

Our comprehensive courses on Python and SQL are the perfect way to start your journey into the world of tech. WeCloudOpen ensures you learn the basics without any hassles

WeCloudOpen Workshop

Our free workshops offer topics like Business Intelligence, Data Science, Data Engineering, DevOps, Machine Learning – allowing you to get a head start in tech career

student success

What our graduates are saying

Celio Oliveira

Reviewed in 2019 | Overall ⭐⭐⭐⭐⭐

This program is fantastic! I didn’t have a coding background and the way they prepared each module made it easier to understand from basic concepts to Advanced SQL, Python, ML and Data Science that I just finished. It is all very exciting. They really do prepare you and offer great support (includes problem sets, individually and in group and quizzes). Teachers are well connected and help you, no matter the prior experience. When you graduate, you have a portfolio of projects, a very good literature and also hands on practise what is important as they showcase your writing capability.

 5/5

Minjung Koo

Reviewed in 2018 | Overall ⭐⭐⭐⭐⭐

A great place to learn and practice data science. I am taking Machine Learning course currently, and the instructor Vanessa is amazing, and I get a lot of hands-on exercises, and feedback. I like that the course is not only teaching you how to code, but also teach you the fundamental theories of each tool, and how to apply in the real life business problems. I highly recommend all their courses to anyone who wants to become a data scientist.

 5/5

Krittika Sarkar

Reviewed in 2021 | Overall ⭐⭐⭐⭐⭐

The course is very well versed in terms of gaining hands on knowledge and experience for people entering into the tech field and for technical people as well. It was detail oriented and provides with a clear concept on various programming languages with very experienced and professional faculty. Overall I would say they are doing an amazing job!!

UPCOMING EVENTS

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Want more details about this program? Unsure about which path to take? Apply now to reserve a spot or make an appointment with our learning advisor.

Start learning with WeCloud Open

Join WeCloud Open and start learning today! We provide open courses, career guide, and learning resources. It’s a great way to start your career in tech!

FAQ

Frequently asked questions about the bootcamp

What is the difference between ML Engineering and Data Science?

Both data scientists and machine learning engineers know how to train and tune ML models. However, data scientists usually work on experimentations, research, data analytics, and working closely with the business teams to answer advanced analytics questions. The ML models created by data scientists can be used for root cause analysis and predictive analytics. ML engineers on the other hand will focus more on training and tuning ML models at scale and also automating the ML pipelines and help deploy the models in production. The output of an ML engineer’s work is usually integrated into a production system. Some ML engineers also need to build ML systems and monitor ML models in production. ML Engineers tend to spend more time on scaling ML pipelines and dealing with ML infrastructures. In some companies, data scientists will do exactly what the ML engineers do. It really depends on the role profile. Job title doesn’t always fully define what a DS or MLE does.

What are the pre-requisites for joining the machine learning engineering bootcamp?

Learners are expected to join the program with solid python programming and decent ML knowledge already, at least the classic ML algorithms. If you don’t meet the pre-requisites, we recommend you go through the AI fundamentals course and ML Engineer pre-bootcamp material to get prepared. Going through the pre-bootcamp will make sure you can comprehend most of the new materials taught in this program quickly, which will leave you more time to focus on practice instead of struggling with the basics.

What is the typical student profile in the Bootcamp?

We’ve seen a good mix of learners from various background. Most students already have python programming and data science experiences. It doesn’t have to be related work experience though. We have learners who has completed self-paced ML courses or other data science bootcamps as well. Typically learners come from the following background: 1. data scientist who want to harness the MLE and MLOps skills. 2. Software engineers who have machine learning knowledge. 3. DevOps engineers who are interested in operating ML systems. 4. New graduates from computer science or computer engineering who have taken ML courses in school or who can complete the pre-course materials on their own. 5. Non-tech background learners who have completed WeCloudData’s AI short courses as a pre-requisites. 6. PhD graduates who want to fast track their job search and meet the technical pre-requisites

What’s the average salary an ML Engineer make?

To get the most up to date information about the MLE job market, we recommend you check out the Job Market Report and blog posts on our website. Typically, junior ML engineers are expected to be paid a base salary of $110k CAD in Canada and $150k USD in the U.S.. Senior ML engineers can be compensated well over $200k USD. The salary will be even higher for ML engineers who specialize in generative AI.

How competitive is the MLE job market?

ML Engineer jobs have high entry bars. The successful candidates are expected to have 3+ years of experience. If you’re new to this field or switching career, the experience gap can be closed by working on real client projects via WeCloudData’s bootcamp. It’s not as competitive as other data fields such as data analytics and data science because there’re less qualified job applicants who have the right skills.

Do I need to have a Master’s or PhD’s in order to do this program?

PhD is not a must have for MLE roles. About 10% of the MLE jobs specifically mention PhD in the description. 30-40% of the MLE jobs require Master’s and 50% of the MLE jobs require bachelor’s degree.

Do I need to know software development before taking this course?

Software engineering knowledge is not necessary but definitely helpful. Many students have successfully completed the course without a CS background. We highly recommend learners pick up some basic CS knowledge before attending this course.

How many hours of study is required?

On average, our students spend between 6 hours in lectures, 3 to 4 hours in labs, and 5 to 10 additional hours on self-paced practice per week in the part-time program.

Can I choose my specialization in this bootcamp?

There are 3 main courses in this Bootcamp. Computer vision, NLP, and MLOps. Though the bootcamp requires students to complete all 3 courses, you can choose to focus on one or two areas. For example, if you’re more interested in CV, you can focus on training and deploying CV applications. The MLOps project can be built on what you build in the CV course.

Do I need to know deep learning before taking this course?

No. But learners need to have prior ML experience. Knowing the basics of ML process is very important. During the bootcamp, we will cover ML review and AI fundamentals.

Which ML/DL frameworks are covered in this bootcamp?

While students have the freedom to use any libraries they want, this course mainly focuses on PyTorch and PyTorch Lightning, which are the most popular Python-based deep learning libraries. PyTorch is developed and open sourced by Facebook. Students will also get exposed to Tensorflow and Keras in some labs.

What MLOps frameworks are covered in the MLOps part of the bootcamp?

Here’re some of the MLOps and ML Engineer tools we teach in this program: docker, data version control, ONNX, Github actions, AWS, SageMaker, Lambda, API Gateway, Spark, MLflow, Kubeflow, Kubernetes, Seldon, etc.

What cloud platforms are covered in the bootcamp? Can I learn more than one cloud?

This program focuses on ML tools/services in the AWS ecosystem. The skills you gain will be quite transferrable to other cloud platforms. It’s better to get good at one tool than being average at two.

What if I miss a few sessions?

Every live-online lecture is recorded so that you can watch them anytime. In addition, you can always book a one-on-one meeting with our TAs to help you catch up with the session you missed.

How in-depth does this Bootcamp cover MLOps?

The MLOps course in the bootcamp is very practical and focuses on implementation. Therefore we don’t spend too much time on explaining the theory of computer system, cloud, and software engineering. MLOps is relatively new and it evolves fast. Most companies want to hire MLOps engineers who know how to apply industry tools to solve real challenges. Since building ML systems is a very practical topic, we will focus on the HOWs and teach students to build things that work as close to best practices as possible.

How hands-on is this course?

This course is designed to be very hands-on. It’s impossible to become good at MLE and MLOps without actually trying to deploy ML solutions. 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.

How many hours am I expected to spend every week?

Depending on your existing skill sets and experience with machine learning, learners usually spend 15 to 20 hours each week (including the lectures and labs)

Do you have a full-time immersive MLE bootcamp?

Yes. This Bootcamp has a part-time class schedule. Both full time and part time students will be in the same class. The difference is that full time learners will spend the weekdays on real client project training, so their progress will be faster than the part time students. But the learning schedule will be the same. If you cannot make the live classes and prefer to watch the recordings, you can do so. You can also inquire about a recording-only Bootcamp with more flexible schedule.

Do I get project mentoring?

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.

Do we get to be involved in real-life 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.

Is career support included in the package?

Yes. The ML 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.

How do you support students find jobs after graduation?

Where can I find your alumni success stories?

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

Can I opt out of career service?

Yes. If you’re doing the bootcamp to up-skill yourself you can opt out of the career service. Fees can be deducted from the tuition.

Do I get a diploma after completing this course?

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.

Do you offer scholarship?

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.

Do you offer payment plan for this course?

Yes, payment plan is available for this course. You can fill out the form on this course page to access the course package details. It has funding related information. Our learning advisor can also help you with your questions.

WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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