Data Science Bootcamp
Make use of the COVID downturn to learn a new in-demand skill.
Up to 90% IBF Funding & SkillsFuture Credits Claimable.
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.
Eligible Singaporeans & PRs can obtain up to 90% funding support for our above IBF-accredited courses.
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
- Introduction to NumPy
- Properties of Ndarray
- Basic Operations of Ndarray Object (Arithmetic Operations)
- Indexing and Iterations
- Importing Packages
- 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
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.
For this individual capstone project, students will 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 explanation of your predictive model and the processes you took to create it
Analyst @ Google
Masters in IT Business (Analytics) & BAcc
Singapore Management University
Machine Learning Engineer @ DC Frontiers
Double BEng & BBA
National University of Singapore
Analytics Consultant @ SIFT Analytics
Bachelor of Engineering
Singapore University of Technology & Design
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.
Course Fee & Government Subsidies
|For classes commencing before 31st December 2021|
|Full Course Fee||$2,500|
|Course Fee After 90% IBF Funding||$250 nett (for Singaporeans & PRs)|
|For classes commencing after 1st January 2022|
|Full Course Fee||$2,500|
|Course Fee After 80% IBF Funding||$500 nett (for Singaporeans aged below 40 years and all PRs)|
|Course Fee After 90% IBF Funding||$250 nett (for Singaporeans aged 40 years and above)|
*All Singaporeans aged 25 years old and above can use their SkillsFuture Credits to offset the remaining $250 or $500 after funding.
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. Is there funding support available?
Yes, there is up to 90% funding support from The Institute of Banking & Finance (IBF) for our IBF-accredited programmes:
- P210816IRN – Data Analytics Bootcamp
- P210806QMF – Data Science Bootcamp
- P210802XCG – User Experience Design Bootcamp
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
Q. Can I use SkillsFuture Credits to offset the remaining course fee after funding?
Yes. For self-sponsored Singaporeans aged 25 years old and above, you can use your SkillsFuture Credits to offset the remaining course fee after funding.
After you have registered for a course, a VI representative will reach out to guide you with the SkillsFuture Credits claim application.
Q. Who is eligible for funding support?
All Singaporeans or Singapore Permanent Residents (PRs) that are physically based in Singapore and successfully complete the course, are eligible for up to 90% IBF funding support.
Company-sponsored participants 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.
- Be a Singaporean or Singapore Permanent Resident (PR) that is physically based in Singapore
- Successfully complete the course (including passing the assessment)
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. This nett fee can be 100% paid with your SkillsFuture Credits.
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$250 nett, which can be paid with your SkillsFuture Credits.
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$250 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).
Q. Are there requirements for funding support?
For you to receive 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 suitable for beginners with no pre-requisites.
Q. 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.
Q. Why learn Python?
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.
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