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Online Instructor-Led Classes

Data Science Bootcamp

Learn Python coding from expert data scientists in Singapore.

✅  Up to 90% IBF Subsidy For All Singaporeans & PRs
✅  Balance fees claimable with SkillsFuture Credits or NTUC UTAP
✳️  Beginner-friendly, zero background knowledge required

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.

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 with free 1-on-1 consultations

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

Course Fee & Government Subsidies

Self-Sponsored - June Intakes

Full Course Fee With GST $2,675 (for Foreigners)
Course Fee After 80% IBF Subsidy With GST $535 (for Singaporeans aged below 40 years and all PRs)
Course Fee After 90% IBF Subsidy With GST $267.5 (for Singaporeans aged 40 years and above)

*All Singaporeans aged 25 years old and above can use their SkillsFuture Credits to fully offset the remaining $267.5 or $535 after funding.

*In addition, NTUC members can utilize UTAP to offset 50% of remaining fees (capped at $250 per year).

Self-Sponsored - July Intakes

Full Course Fee With GST $2,675 (for Foreigners)
Course Fee After 70% IBF Subsidy With GST $802.5 (for Singaporeans aged below 40 years and all PRs)
Course Fee After 90% IBF Subsidy With GST $267.5 (for Singaporeans aged 40 years and above)

*All Singaporeans aged 25 years old and above can use their SkillsFuture Credits to fully offset the remaining $267.5 or $802.5 after funding.

*In addition, NTUC members can utilize UTAP to offset 50% of remaining fees (capped at $250 per year).

Company-Sponsored

For June intakes - Companies in Finance and Banking Industries
Full Course Fee With GST $2,675 (for Foreigners)
Course Fee After 80% IBF Subsidy With GST $675 (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)

Companies NOT in the Finance and Banking Industries
Full Course Fee With GST $2,675 (for Foreigners)

For July intakes - Companies in Finance and Banking Industries
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)

Companies NOT in the Finance and Banking Industries
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

      Clarence

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

      Zane

      Zane

      Data Scientist @ Gojek
      Masters in Knowledge Engineering, NUS
      BEng in Aerospace Engineering, NTU

      Daniel

      Daniel

      Data Scientist @ Apple
      Master’s in Computer Science, BsC
      Georgia Institute of Technology, SMU

      Stan

      Stan

      Data Scientist @ Standard Chartered
      Masters in Computing, B.Sc
      National University of Singapore

      Sifat

      Sifat

      Analytics Consultant @ SIFT Analytics
      Bachelor of Engineering
      Singapore University of Technology & Design

      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:

      (a) Full Course Fees with GST $2,675
      (b) Less IBF Subsidy (e.g. 90%) ($2407.50)
      (c) Course Fee Payable (after IBF Subsidy) $267.50
      (d) SkillsFuture Credit ($200)
      (e) NTUC Member pays $67.50
      (f) NTUC Member claims 50% of (e) from UTAP $33.75

      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:

    • Financial Institutions that are regulated by the Monetary Authority of Singapore (MAS) (either licensed / exempted from licensing)
    • Fintech companies that are registered with the Singapore Fintech Association
    •  

      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 certificate granted at the end of the course?

      Upon successful completion of this IBF-accredited 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 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.

          Why-learn-Python-Queries-in-Stack-Overflow-2021
          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.

          Zhi Wei

          Data Science Alumni

          My experience of taking the Data Science Course was excellent. The instructors were patient and friendly. It was enjoyable and productive.

          Data Science Capstone Project done by Hriday Mistry

          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.

          What Our Alumni Say

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

          Instructors & Students from