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.

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

Tutorials:

• String Methods  
  • String Indexing
  • String Concatenation
  • String Formatting
  • List Slicing
• Python Iterations, Control Flow, and Functions (if…else statements, for and while loops)

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)

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)

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

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

• 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

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

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

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 

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.

Yes, there is 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 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.

• 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.

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.

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

This program is designed for beginners with no prerequisites.