Data Science & Machine Learning
Learn data visualization and how to do predictive analysis with machine learning.
Data Science & Machine Learning
According to Indeed.com, the average base salary for a Data Scientist in Singapore in 2023 is around S$7493 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, Apr 2023
In this data science course, you will be learning 2 important skills to become a data scientist.
# The software used in the course is web-based, no other specific software environment needs to be installed.
# This course requires basic knowledge of Python programming. For a course in Python programming, view here.
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
✓ 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
- 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:
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
- Aspiring Data Science professionals
- Professionals who are working with large data sets and want to analyze more efficiently
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.
2 days
9:00 am - 6:00 pm
6 months Post Course Support
6 months Free Refresher Course
Certificate of Completion
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
2 days
9:00 am - 6:00 pm
6 months Post Course Support
6 months Free Refresher Course
Certificate of Completion