Home Classroom › Python for Data Science


Python for Data Science

Learn how to use Python for Data Science. You will also learn how to do predictive analysis with machine learning.

Python for Data Science

According to Indeed.com, the average base salary for a Data Scientist in Singapore in 2021 is around $7100 monthly. Data science is a skill in demand in this modern age with buzzwords like big data, artificial intelligence, machine learning, deep learning etc.

Indeed.com, Oct 2021


In this data science course, you will be learning 3 important skills to becoming a data scientist.



Python has been ranked the top programming language in 2021 by IEEE Spectrum. IEEE Spectrum’s rankings are one measure of what languages are worth investing time in to learn.

Python is so popular because it is easy to read, understand and use. This makes it a great place to start for people new to programming. With basic python, you can then move on to use the powerful data science packages available.


Data preparation, visualization, and analysis

Go through basic statistics, then move on to use Pandas, a Python library, to prepare your data for analysis.
Communicate insights by creating diagrams and charts that describe large amounts of data but are easy to understand.

House prices in Singapore


Machine learning and AI

Next, learn about supervised and unsupervised machine learning. Use machine learning, Python libraries again, to go through data and try to predict future values or extract insights. Next, try different libraries to see which does predictions best.

Skills You Will Learn

✓ Acquire basic mathematical and statistical knowledge
✓ Set up your Python coding environment
✓ Evaluate and pre-process data
✓ Access financial data sets
✓ Perform data mining using Pandas
✓ Perform data visualization using Matplotlib
✓ Perform machine learning using scikit, Tensorflow, Keras


Curriculum For This Course

  • Coding environment (e.g. Anaconda, Jupyter notebook ,Spyder, VSCode) and IDE
  • Different Integrated Development Environment (IDEs)
  • Python data types and variables e.g. int, string
  • Syntax e.g. comments, print
  • Control flow e.g. if/else, for, while
  • Lambda functions
  • Python data objects and usage e.g. get, join 
  • Useful functions for data mining/data preprocessing
  • Combine, merge, combine, append different data sets
  • Evaluate data integrity and quality
  • Clean, preprocess data 
  • Plotting functionalities
  • Basic quantitative analysis for stocks using Python e.g. candlestick, Bollinger bands
  • Identifying various data sources e.g. Kaggle.com, Amazon’s AWS datasets
  • Linear regression model vs Decision trees
  • Recurrent Neural Network (RNN)
  • Random Forest Regressor
  • Cross validation to do better model evaluation
  • Machine learning data pipeline
  • Perform and validate test hypothesis 

Content Of Lesson

More About the Course

Hands-on practice includes:


  • Set up an environment in line with best practices
  • Debug common installation issues
  • Use an IDE e.g. editor, console, debugger
  • Fixing control flow, functions, data objects problems

Data mining: Pandas

  • Use common python libraries
  • Getting and consolidating data from Yahoo Finance/Quand/Intrinio
  • Calculating risks of a stock portfolio
  • Change parameters to do “what-if” analysis to see how portfolios perform

Data Visualization: Matplotlib

  • Change parameters to do “what-if” analysis to see how portfolios perform
  • Estimate prices e.g. Facebook stock

Machine Learning: scikit, Tensorflow, Keras

  • Change parameters to do “what-if” analysis to see how portfolios perform
  • Estimate prices e.g. Facebook stock, house prices
  • Analyze same problem using different methods
  • Discuss how to improve modelling approach to improve returns

Who Should Attend?

  • Anyone keen to learn about Data Science or Python programming
  • Aspiring Data Science professionals
  • Professionals who are working with large data sets and want to analyze more efficiently using Python

Post-Course Support

At emarsity, we want to make sure our learners have a full grasp of the course attended. Therefore, post-course support is critical to ensure your learnings are clearly understood.

Our comprehensive Post-Course Support Package comprises the following:

✓ 1 x Free Refresher Course
Attend the course again within 6 months to refresh/reinforce your knowledge. Limited to 2 refreshers per course, you are advised to reserve your seat in advance.

✓ 6 Months Email Support
Email us your queries after the course, we will help with your queries.


Course Funding Available

Fees before funding support*: $1337.50 w/GST

Fees after funding support*:
Singapore Citizens or Permanent Residents: $133.75 w/GST
i.e. Pay $133.75 to sign up, not $1337.50.

We will submit the funding support application.

The balance of $133.75 can be paid using SkillsFuture credits.

* Funding support for eligible participants who:
– Are Singapore Citizens or Permanent Residents
– Are self-sponsored
– Attend the full course and pass the assessment

About this programme

About the Institute of Banking and Finance Singapore
The Institute of Banking and Finance Singapore (IBF) is the national accreditation and certification agency for financial industry competency in Singapore under the IBF Standards. Find out more on www.ibf.org.sg

About the IBF Standards
The IBF Standards are a set of competency standards for financial skills. These Standards are developed in partnership with industry leaders and provide a professional development and skills roadmap for financial sector practitioners to excel in their respective job roles. They currently cover 12 industry segments in the financial sector.

About IBF Certification for Level 2 and/or 3 Programmes
A practitioner who successfully completes an IBF Level 2 and/or 3 Programme with at least 3 years of relevant experience will be conferred the IBF Advanced (IBFA) certification status. T&Cs apply. Find out more about IBF certification and its benefits on bit.ly/whybeibfcertified


9:00 am - 6:00 pm

6 months Post Course Support

6 months Free Refresher Course

Certificate of Completion

Funding Support*: 90% funding for Singapore Citizens and PRs upon successful completion and passing the assessment

Start Learning

Your Course Instructor



Mr. Chirag is an experienced finance lecturer teaching Finance and Applied Statistics/Data Science techniques in Finance for the last 15 years. Having graduated from the prestigious Ivy League Columbia University, he proceeded to work for large investment banks in New York and Tokyo.

He enjoys training learners in modern data science & machine learning techniques with a focus on practical applications and applying concepts learned to solve real-world problems.
He is well versed in Python, C++ and enjoys making computer science concepts easy to understand for learners.

Being an industry professional, he stresses on preparing learners for the workplace. He is extremely approachable and is always willing to help students when encountering study-related problems and highly encourages learners to adopt a “think out of the box” attitude towards problem-solving.

Testimonials From Learners


Course Rating


JayTan Lai

5 ★

This course is an informative, detailed, and useful breakdown of the many components of the multi-faceted and booming social media platform that is WeChat. Thank you Daniel. High recommended.


Course Details


9:00 am - 6:00 pm

6 months Post Course Support

6 months Free Refresher Course

Certificate of Completion

Funding Support*: 90% funding for Singapore Citizens and PRs upon successful completion and passing the assessment

Call Us (+65) 6795 0985

Contact our training consultant for funding information. Click Here To WhatsApp Us

Start Learning

Learners from around the Singapore