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 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

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

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.

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.

For Self-Sponsored:

All Singaporeans or Singapore Permanent Residents (PRs) that are physically based in Singapore and successfully complete the course will be eligible.

For Company-Sponsored:

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).

This program is suitable for beginners with no pre-requisites.

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.

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.

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.