Data Science Bootcamp
Learn Python coding from expert data scientists in Singapore.
Learn To Code In Python, The Most In-Demand Language
Ever wondered how YouTube’s recommendation engine works? Or how TikTok knows what to show you next? Two words: machine learning.
Python is the most popular machine learning language. Compared to many languages, Python is easy to learn and efficient. This is why it is one of the most sought-after skills by employers.
Our graduates from this data science course include individuals from JP Morgan, Oracle, PwC, Accenture, Shopee, Walt Disney and more. Learn directly from expert data scientists in Singapore.
Data Science Course Details
Vertical Institute’s Data Science course in Singapore is an introduction to Python programming, machine learning and artificial intelligence to drive powerful predictions through data. Participants will culminate their learning by developing a capstone project to solve a real-world data problem in the fintech industry.
✓ This data science course is beginner-friendly. No prior background or experience is required.
✓ Gain an accredited Data Science Certification in Singapore.
What to expect?
- Learn new concepts and tools through expert-led lectures, discussions, assignments and project work
- Apply what you’ve learned to create a capstone project solving a real-world data problem
- Learn new concepts and tools through expert-led lectures, discussions, assignments and project work
- Apply what you’ve learned to create a capstone project solving a real-world data problem
- Dedicated career support and matching with hiring partners New
- Receive individualised feedback and support from your instructional team with free 1-on-1 consultations
- Be part of the VI community where members can leverage connections with alumni, instructors and experts
- Receive individualised feedback and support from your instructional team with free 1-on-1 consultations
- Be part of the VI community where members can leverage connections with alumni, instructors and experts
- Dedicated career support and matching with hiring partners New
Course Fee & Government Subsidies
Company-Sponsored
| Full Course Fee With GST | $2,675 (for Foreigners) |
| Course Fee After 70% IBF Subsidy With GST | $925 (for Singaporeans aged below 40 years and all PRs) |
| Course Fee After 90% IBF Subsidy With GST | $425 (for Singaporeans aged 40 years and above) |
| Full Course Fee With GST | $2,675 (for Foreigners) |
Data Science Course Schedules
7 lessons,
3 hours each lesson
Online classes
via Zoom
1 Instructor and 2-3
Teaching Assistants
Data Science Course Curriculum
Pre-Work
As a VI student, you will be given access to online learning materials in our e-learning portal.
To get you ready for learning, this essential pre-work will familiarize you with the basics of the key concepts and tools we will be using throughout the course.
Although you will learn these topics remotely before you arrive in class, you won’t be far away from the resources of the VI community. Make use of our Telegram channel to leverage connections with students, alumni, instructors and experts. At the end of your pre-work, you’ll be ready for the fast pace on campus!
After the course, you can choose to participate in follow-up sessions with your instructor, either in a group and/or individually, included as part of the course fee.
Module 1: Data Science Fundamentals
Tutorials:
- Introduction to Data Science
- What is Data Science?
- Data Science Life Cycle
- What is Python and Why Learn It?
- Introduction to Jupyter Notebook
- Introduction to Python Fundamentals
- Introduction to Data Types
- Python Variables (In-built Functions)
- Arithmetic, Relational and Logical Operators
- Python Datatypes (String, Lists, Tuples, Dictionaries)
- None and Casting
Module 2: String Methods & Python Control Flow
Tutorials:
- String Methods
- String Indexing
- String Concatenation
- String Formatting
- List Slicing
- Python Iterations, Control Flow, and Functions (if…else statements, for and while loops)
Module 3: NumPy & Pandas
Tutorials:
- Numpy
- Introduction to NumPy
- Properties of Ndarray
- Basic Operations of Ndarray Object (Arithmetic Operations)
- Indexing and Iterations
- Importing Packages
- Pandas
- Introduction to Pandas
- Basic Operations of Series (Arithmetic Operations, Evaluating Values)
- Basic Operations of Dataframes (Mathematical Operations)
- Importing Files into Dataframes
- Joins in Pandas (Merge & Concat)
Module 4: Data Cleaning, Visualization & Exploratory Data Analysis
Tutorials:
- Data Visualization- Matplotlib & Seaborn
- Introduction to Matplotlib
- Barplot, Histogram/Density Plot, Line Chart, Scatter Plot, Boxplot, Heatmap
- Graph Parameters (Changing Size, Color, Style Markers, Titles, Legends and Label Orientation) in Matplotlib
- Data Cleaning and Exploratory Data Analysis
- Introduction to Data Cleaning
- Common steps in Data Cleaning
- Exploratory Data Analysis (Filtering and Sorting, Column Manipulation, Group By/Aggregate Functions, Handling Missing Data, using Functions)
Module 5: Linear Regression and Feature Scaling
Tutorials:
- Linear Regression
- Modeling and Predictions
- Introduction to Linear Regression
- Introduction to Scikit-Learn Package (Fitting the Data, Evaluation of Model and Comparing Models)
- Introduction to Statsmodels
- Feature Scaling
- Standardization
- MinMax
Module 6: Classification Models
Tutorials:
- K-nearest neighbors
- Introduction to Classification
- Introduction to KNN
- Advantages and Drawbacks of KNN
- Training KNN using Scikit-Learn using Loan ApprovalDdataset
- Logistic Regression
- Binary Class
- Probability Estimation Dilemma
- Odds Ratio
- Log Odds
- Decision Trees and Random Forest Classification
- Algorithm Walk-Through
- Advantages and Drawbacks of Decision Trees
- Training Decision Tree using scikit-learn using the Loan Approval dataset
- Training Random Forest Scikit-Learn
Module 7: Capstone Project Discussion & Summary
- Prologue to GridSearch
- Introduction to GridSearch
- Review of Initial EDA Strategies
- Implement Changes and Updates to KNN Model using GridSearch
- Find Optimal Hyperparameters of a model
- Apply GridSearch to Classification Model using Loan Approval Dataset
- Sklearn Pipelines
- Inspecting Pipelines
- Pipelines with GridSearch
- Cross Validation
- Capstone Project Discussion
The Capstone Project
Participants will be required to address a data-related problem and create a predictive model. You will acquire a real-world dataset, form a hypothesis about it, and then clean, parse, and apply modelling techniques and data science principles.
For this individual open-book capstone project, students will culminate their learning by applying the new tools and concepts learnt to create a short report that includes:
- A clearly articulated problem statement
- A summary of the data acquisition, cleaning, and parsing stages
- A clear explanation of your predictive model and the processes you took to create it
Instructors
Clarence
Analyst @ Google
Master’s in IT Business (Analytics)
Singapore Management University
Zane
Data Scientist @ Gojek
Masters in Knowledge Engineering, NUS
BEng in Aerospace Engineering, NTU
Daniel
Data Scientist @ Apple
Master’s in Computer Science, BsC
Georgia Institute of Technology, SMU
Stan
Data Scientist @ Standard Chartered
Master’s in Computing
National University of Singapore
Sifat
Analytics Consultant @ SIFT Analytics
Bachelor’s in Engineering
Singapore University of Technology & Design
Lindsay
Data Analyst @ Foodpanda
Bachelor’s in Mathematical Science
Nanyang Technological University
Funding Support
IBF Standards Training Scheme (IBF-STS)
This programme has been accredited under the IBF Standards, and is eligible for funding under the IBF Standards Training Scheme (IBF-STS), subject to all eligibility criteria being met.
Find out more on www.ibf.org.sg.
Frequently Asked Questions
Are there course fee subsidies available?
Yes, there is up to 90% funding support from The Institute of Banking & Finance (IBF) for our IBF-accredited programmes:
The IBF Standards Training Scheme (“IBF-STS”) provides funding for training and assessment programmes accredited under the Skills Framework for Financial Services.
For our training programmes that are IBF-accredited, eligible Singaporeans & PRs may receive funding support under the IBF Standards Training Scheme (IBF-STS), subjected to all eligibility criteria being met.
For more information on the funding support, please visit: https://www.ibf.org.sg/programmes/Pages/IBF-STS.aspx
Can I use SkillsFuture Credits to pay for the remaining course fee after funding?
Yes. For self-sponsored Singaporeans aged 25 years old and above, you can use your SkillsFuture Credits to fully offset the remaining course fees after funding. This means that you do not need to pay any cash.
After you have registered for a course, a VI representative will reach out to guide you with the SkillsFuture Credits claim application.
Can NTUC Union members use UTAP to offset the remaining course fee after funding?
Yes, all our courses are eligible for Union Training Assistance Programme (UTAP) Funding. NTUC Union members can use UTAP to offset 50% of unfunded course fees (capped at $250 per year).
For example: If your remaining course fees are $267.50, you can offset $133.75 with UTAP.
This claim must be done after completion of the course. Please refer to the UTAP FAQ for more information.
Can I use SkillsFuture Credits and UTAP at the same time?
Yes, you can utilize both at the same time. UTAP can only be applied after SFC has been applied.
For illustration purposes only:
Who is eligible for IBF funding support?
For Self-Sponsored:
All Singaporeans or Singapore Permanent Residents (PRs) that are physically based in Singapore and successfully complete the course will be eligible.
- Be a Singaporean Citizen or PR physically based in Singapore
- Minimum of 75% attendance (this means that you must attend at least 6 out of 7 lessons)
- Pass the assessment
For Company-Sponsored:
- Be from Financial Institutions that are regulated by the Monetary Authority of Singapore (MAS) (either licensed / exempted from licensing) or Fintech companies that are registered with the Singapore Fintech Association.
- Be a Singaporean Citizen or PR physically based in Singapore
- Minimum of 75% attendance (this means that you must attend at least 6 out of 7 lessons)
- Pass the assessment
How do I obtain IBF funding support? Do I need to pay the full fee first?
This funding support works on a nett fee model. This means that participants only need to pay the remaining course fees after IBF funding. This nett fee can be fully paid with your SkillsFuture Credits.
For Self-Sponsored:
You must be a Singaporean or Singapore Permanent Resident (PR) that is physically based in Singapore. You will only need to pay the course fees minus the funding support. For example, if you are eligible for 90% funding support, you will only be paying S$267.50 nett, which can be paid with your SkillsFuture Credits.
For Company-Sponsored:
You must be a Singaporean or Singapore Permanent Resident (PR) that is physically based in Singapore and working in an eligible company:
Your company will pay the course fees minus the funding support. For example, if you are eligible for 90% funding support, the company will be paying S$425 nett for company-sponsored participants.
Eligible companies will further be able to claim the remaining course fees after funding with the Training Allowance Grant (TAG) (S$10/hour of eligible training and assessment hours).
Are there any pre-requisites for this course?
This program is suitable for beginners with no pre-requisites.
Is there a data science certificate granted at the end of the course?
Upon successful completion of this IBF-accredited course, participants will be awarded a data science certification recognized by employers in Singapore. VI alumni use their course certificate to demonstrate skills to employers and their LinkedIn network.
Our programmes are well-regarded by top companies, who contribute to our curriculum and use our courses to train their own teams.
Why learn Python?
The below graph shows the popularity of various coding languages. Python is the most popular coding language today and highly demanded by employers.
1. Python is the fastest growing programming language.
2. Python is extremely versatile, with multiple uses.
3. Python is in high demand skill for jobs.
4. Python is easy to read, write and learn.
5. Python developers are paid competitively.
6. Python has an incredibly supportive community.
What softwares do I need to download?
You will need to install Anaconda to run Python programming.
You instructional team will guide you step-by-step on the installation process before class begins.
What if I cannot attend a class?
If you miss a lesson, you are allowed to attend a makeup class. This would be the corresponding class of another intake that same week, subject to availability. If you cannot make it for the makeup class, then you can simply watch the recorded lesson video.
Your attendance is only counted if you attend a makeup class.
Why are data science skills relevant today?
‘Singapore Tech Salaries May Soar Up to 30% in Two Years’ – The Straits Times, 2022
Businesses today understand the importance of data and are now placing immense emphasis on data science skills to drive innovation and make data-driven decisions.
Data science is one of the most coveted skills by employers across various industries such as finance and tech. The high demand and low supply of such skills also makes data science a highly lucrative career option.
Kickstart a career in Data Science with the beginner-friendly Data Science Bootcamp by Vertical Institute. The course allocates 3-4 instructional team members per class to ensure dedicated peer and mentor support for your Data Science journey. You will graduate with an IBF-accredited Data Science certification, as well as a polished, portfolio-ready project to showcase your new Python and machine learning skills.
Zhi Wei
My experience of taking the Data Science Course was excellent. The instructors were patient and friendly. It was enjoyable and productive.
What Our Alumni Say
Our Students’ Works
Predicting Future Stock Prices is a Data Science Capstone Project by Vertical Institute’s student, Hriday Mistry. Using the Linear Regression model to predict future stock prices based on date.
Instructors & Students from
