Stay safe and learn from home. All our programmes are now conducted virtually.

Data Science Bootcamp

Make use of the COVID downturn to learn a new in-demand skill.
Up to 90% IBF Funding & SkillsFuture Credits Eligible.

Master Python Programming,

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.

In this course, students will get hands-on with Python programming, machine learning, and more to drive powerful predictions through data.

Why Learn Data Science Skills?

Highly in Demand

Data scientists are now one of the top five in-demand hiring roles in Singapore.

High Demand = High Pay

A data scientist is in-demand globally and paid competitively.

Flexible Job Paths

The Python skillset is versatile and applies to virtually all industries.

Up To 90% Funding Support For Eligible Singaporeans & PRs*
Vertical Institute is accredited by the Institute of Banking and Finance (IBF) as an IBF-STS Accredited Training Provider.

Eligible Singaporeans & PRs can obtain up to 90% funding support for our above IBF-accredited courses.

Course Details

Vertical Institute’s Data Science Bootcamp is an introduction to the field of data science and machine learning. The course will be a hands-on approach to the fundamental data analysis techniques and machine learning algorithms that enable you to build robust predictive models for business insights. For the capstone project, you will apply machine learning techniques to solve a real-world data problem in fintech. This course is suitable for beginners. No prior background or prerequisite is required.

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

  • Receive individualised feedback and support from your instructional team

  • Be part of the VI community where members can leverage connections with alumni, instructors and experts



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


  • 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


  • 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


    • 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


    • 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


      • 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


      • 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 finance data set, form a hypothesis about it, and then clean, parse, and apply modelling techniques and data science principles.

      On the last lesson, students culminate their learning by applying the new tools and concepts learnt to create a report that includes:

      • A clearly articulated problem statement
      • A summary of the data acquisition, cleaning, and parsing stages
      • A clear presentation of your predictive model and the processes you took to create it




      Analyst @ Google
      Masters in IT Business (Analytics) & BAcc
      Singapore Management University

      Han Qi

      Han Qi

      Machine Learning Engineer @ DC Frontiers
      Double BEng & BBA
      National University of Singapore

      Upcoming Course Schedules

      The bootcamp consists of 7 lessons with each lesson lasting 3 hours long. Classes will be conducted virtually, done face-to-face with our Instructor via Zoom. You will have intimate access to our instructional team that’s ready to answer your questions and a strong peer community.


      November/December Schedules (Limited Slots Left)

      Weekend Morning Intake
      ✅ 9am – 12pm
      ✅ Saturdays & Sundays
      ✅ November 20, 21, 27, 28, December 4, 5, 11

      Weekend Afternoon Intake
      ✅ 1pm – 4pm
      ✅ Saturdays & Sundays
      ✅ November 20, 21, 27, 28, December 4, 5, 11

      December Schedules (New Release)

      Weekday Evening Intake
      7pm – 10pm
      Tuesdays & Thursdays
      December 2, 7, 9, 14, 16, 21, 23

      Fast Track Morning Intake
      ✅ 9am – 12pm
      ✅ Monday – Sunday
      ✅ December 13, 14, 15, 16, 17, 18, 19 

      Fast Track Afternoon Intake
      ✅ 1pm – 4pm
      ✅ Monday – Sunday
      ✅ December 13, 14, 15, 16, 17, 18, 19 

      Course Fee & Government Subsidies

      Course fee before 90% IBF funding: S$2,500

      Course fee after 90% IBF funding: S$250 nett (for Singaporeans & PRs)

      All Singaporeans aged 25 years old and above can use their SkillsFuture Credits to offset the remaining $250.

      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

      Q. Can I use SkillsFuture Credits to offset the remaining course fee?

      Yes. For Singaporeans aged 25 years old and above, you can use your SkillsFuture Credits to offset the remaining course fee.

      After you have registered for a course, a VI representative will reach out to guide you with the SkillsFuture Credits Claim Application.

      Q. Is there funding support available?

      Yes, there is 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 accredited under the IBF Standards, eligible Singaporeans & PRs may receive funding support under the IBF Standards Training Scheme (IBF-STS), subjected to all eligibility criteria being met.

      For training programmes commencing between 8 April 2020 and 31 December 2021 (both dates inclusive):

      • Funding Support: 90% of direct training cost

      For more information on the funding support, please visit : https://www.ibf.org.sg/programmes/Pages/IBF-STS.aspx

      • Training Allowance Grant (TAG): $10/hour (only for Financial Institutions/FinTech Firms)

      The Training Allowance Grant (TAG) aims to help Financial Institutions and FinTech firms manage manpower costs and support the skills upgrade of their employees.  FIs/FinTechs will be able to further claim their course fees after funding (in this case, $250) upon successful completion of the course.

      Q. Who is eligible for funding support?
      • Both self-sponsored individuals and company-sponsored* individuals.
      • All Singapore Citizens or Singapore Permanent Residents physically based in Singapore.
      • Successfully complete the IBF-accredited Data Science Bootcamp (including passing the capstone project assessment).​

      *Please note that company-sponsored individuals have to 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.

      Q. How do I obtain funding support?

      This funding support works on a nett fee model. This means that participants only need to pay the unfunded portion of the course fees (10%). 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 which is 10% of the course fees. For example, if the course fees are S$2500, you will only be paying S$250 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 which refers to: Financial Institutions that are regulated by the Monetary Authority of Singapore (MAS) (either licensed / exempted from licensing) and Fintech companies that are registered with the Singapore Fintech Association.

      Your company will pay the course fees minus the funding support which is 10% of the course fees. For example, if the course fees are S$2500 , the company will be paying S$250 nett for company-sponsored participants.

      Companies will further be able to claim the remaining $250 paid with the Training Allowance Grant (TAG) upon successful completion of the course.

      Q. Are there course requirements for funding support?

      For you to receive the funding support, please take note of the following:

      • Minimum of 75% attendance (this means that you must attend at least 6 out of 7 lessons)
      • Pass the Capstone Project Assessment
      Q. Are there any pre-requisites for this course?

      This program is designed for beginners with no prerequisites.

      Q. Is there a certificate granted at the end of the course?

      Upon successful completion of the course, participants will be awarded a digital certificate by Vertical Institute. VI alumni use their course certificate to demonstrate skills to employers and their LinkedIn network.

      Our data science course is well-regarded by top companies, who contribute to our curriculum and use our courses to train their own teams.

          Vertical Institute is the official training partner of the Government Technology Agency of Singapore, upskilling the government’s workforce with in-demand tech skills.

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