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 2022 is around S$7371 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, May 2022
In this data science course, you will be learning 3 important skills to becoming a data scientist.
# The software used in the course is web-based, no other specific software environment need be installed.
Python
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 data sets e.g. financial, digital marketing
✓ 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 using Python
- 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:
Python
- 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 e.g. Yahoo Finance/Quand/Intrinio
- Perform calculations e.g. calculating risks of a stock portfolio
- Change parameters to do “what-if” analysis
Data Visualization: Matplotlib
- Change parameters to do “what-if” analysis
- Perform predictive analysis e.g. estimate stock prices
Machine Learning: scikit, Tensorflow, Keras
- Change parameters to do “what-if” analysis
- Perform predictive analysis e.g. estimate stock prices
- Analyze same problem using different methods
- Discuss how to improve modelling approach to improve results
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*: S$1337.50 w/GST or US$897 (for international learners)
Fees after funding support*:
SCs 40 years old & above: S$133.75 w/GST
SCs below 40 years old & PRs: S$401.25 w/GST
i.e. Pay S$133.75 / S$401.25 to sign up, not S$1337.50.
We will submit the funding support application. The balance of S$133.75 / S$401.25 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
3 days
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
Siew Yee
Instructor
Leader in data science and digital transformation with more than 20 years of experience in team setup and strategic business insights leading to data-driven results for Asia Pacific and global markets.
Data Science coverage: Predictive Modelling, Machine Learning, Deep Learning, Blockchain, Computer Vision, Natural Language Processing, Digital Music, Knowledge Graph, Multi-agent Systems, Internet-of-Things (IoT).
Industry experiences: Banking, eCommerce, B2B, Retail, Distribution, Media & Advertising, IT, Aerospace.
Testimonials From Learners
5.0
Course Rating
Alan Goh
5 ★
Jan 2022
Rongita Bhattacharyya
5 ★
Jan 2022
Course Details
3 days
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