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RyanGoh's Eportfolio
Ryan Goh
Work Experiences
2019 - 2020: TDCX
2018 - 2019: Schroders
2017 - 2018: Accenture
2016 - 2017: Citi Singapore
2013: DBS Bank
2008 - 2010: Singapore Police Force
Skillset
Marketing
1. Marketing
2. Digital Media Metrics
Data Sciences
1. Data Mining and Predictive Analytics
1. CRISP-DM
2. Predictive Modeling Fundamentals
3. Confusion Matrix
4. Decision Trees
5. Regression
6. Model Assessment
7. Research & Report
2. Text Mining and Analytics
2.1. Text Mining (SAS Enterprise Miner & Text Miner)
1. Collect Data
2. Text Parsing / Text Filtering
3. Transformation
4. Text Mining
4.1 Text Clustering
4.2 Topic Extraction
5. Text Mining Research & Report
2.2 Sentiment Analysis
2.3. Text Mining (SPSS Modeler)
2.4. Text Mining (R-Programming)
3. IBM SPSS Modeler
4. SAS Enterprise
5. Excel
1. Standard Deviation
6. Statistics (R-Programming / Excel)
1. Binomial Distribution
2. Poisson Distribution
3. Normal Distribution
4. Covariance
5. Correlation
6. Linear Regression
7. Multiple Regression
8. Hypothesis Testing
8.1 Bartlett Test
8.2 Shapiro-Wilk
8.3 ANOVA (Analysis of Variance)
8.4 Kruskal-Wallis Test
9. Principal Components Analysis (PCA)
10. Missing Data in R
11. Statistical Analysis for Predictive Analytics
12. Permutations and Combinations
Visualizations
Tableau
1. Connect and Join data sources
2. Creating a storyboard
3. Custom Calculations and Fields
4. Aggregate Data
5. Forecasting future values
6. Sort and Filter Data
7. Define Groups and Sets
8. Develop Crosstabs
9. Create Visualizations
1. Column and Stacked column charts
2. Line charts and Area fill charts
3. Pie charts
4. Scatter plots
5. Data clustering
6. Histograms
7. Treemap
8. Highlight Table
Power BI Desktop
Charts
Charts with Slicer
Text Visualizations
Word Cloud
SQL
1. ERD Diagram
2. SQL Functions
1. Limit
2. Counting rows
3. Inserting rows
4. Updating rows
5. Deleting rows
6. Filtering row (data)
7. Removing Duplicates
8. Ordering rows
9. Conditional Expressions
3. Managing Tables
1. Creating a table
2. Deleting a table
3. Handling the Null value
4. Inserting new column
4. Relationships
1. Inner Join
2. Left Join
5. String
1. Length of a String
2. Selecting part of a string
3. Removing spaces of string
4. Lowercase & Uppercase in String
6. Number
7. Date and Time
8. Aggregates
9. Transactions
1. Rollback
10. Trigger (Automating data)
1. Trigger with Timestamps
11. Subselect
1. Search within the subquery
2. Creating a view
12. SQL-Python
Python
1. Python Knowledge
1.1. Python Data Types
11.1.1 String Properties and Methods
1.2 Python Data Structures
11.2.1. Lists in Python
11.2.2. Tuples in Python
11.2.3. Sets in Python
11.2.4. Dictionaries in Python
1.3. Comparison Operators in Python
1.4. Python Statements
1.5. Loops in Python
1.6. Basic Python Practices
2. Writing a Function
1. Arguments & Scope
2. Lambda functions
3. Error-handling
4. Lambda functions & Error-handling
3. Importing Data in Python
1. Flat files using NumPy
2. CSV files using pandas
3. Excel files using pandas
4. SAS files using pandas
5. Loading multiple files
4. Relational database in Python
5. Data Manipulation with pandas
1. Transforming Data
1. Sorting
2. Subsetting
3. Adding new columns
4. Concatenating data
5. Merging data
6. Tidying Data (pd.melt())
2. Aggregating Data
1. Counting Variables
2. Dropping Duplicates
3. Grouped Summary Statistics
4. Pivot tables
3. Slicing and indexing
1. Slicing with .loc
2. Subsetting with .iloc
3. Pivot tables with slicing & subsetting
4. Cleaning data
1. Converting of data types
2. Functions to clean data
3. Duplicate & Missing data
4. Missing values
5. Testing data with asserts
5. Visualizing DataFrames
6. Creating DataFrames
7. Reading and writing CSVs
6. Unsupervised Learning (Clustering)
1. Evaluating a clustering
2. Transforming features for better clustering
7. Unsupervised Learning (Association Analysis)
8. Supervised Learning
1. K-Nearest Neighbors (KNN)
2. Measuring Model Performance
3. Linear Regression
Linear Regression (Service Level) 1
Linear Regression (Service Level) 2
Linear Regression (Service Level) 3
4. Logistic Regression
5. Naive Bayes Classifier
Applied Artificial Intelligence
1. Essentials of Machine Learning
1. Introduction to Machine Learning Toolkit
1. Scatter plot
2. Histogram & Histograms Overlayed
3. Boxplot & Pairplot
2. Introduction to Machine Learning
1. Count Plot & Histogram
2. Scatter Matrix, Pair Plot & Box Plot
3. Data Modelling (Validation)
4. Filtering & Merging
3. Machine Learning Process
1. Random vs Stratified Sampling
2. Experimenting with Attribute Combinations
3. Data Cleaning
4. Select and Train a Mode
5. Evaluation using Cross-Validation
6. Model Fine Tuning
2. Data Science Foundation
1. Reading Image
2. Probability
3. Statistics
4. Correlation
5. Hypothesis Testing
6. XML & XPath, HTML & CSS Selector & JSON
7. Web Scrapping (ConsoleWriterPipeline)
8. Web Scrapping (JsonWriterPipeline)
9. Regular Expression
10. Text Replacement
11. Text Preprocessing
12. Image Data
Project Management Professional (PMP)
1. Introduction
2. The Environment in which Projects Operate
3. The Role of the Project Manager
Knowledge Areas & Process Groups
4. Project Integration Management
5. Project Scope Management
6. Project Schedule Management
7. Project Cost Management
8. Project Quality Management
9. Project Resource Management
10. Project Communications Management
11. Project Risk Management
12. Project Procurement Management
13. Project Stakeholder Management
Other Skillset
1. Call Centre Metrics
2. Search Engine Optimisation and Fundamentals
2.1. Search Engine Optimisation
2.2. Search Engine Optimization Fundamentals
3. Agile Methodology
4. HTML
5.. Other Skillset
School Experiences
National University of Singapore (NUS)
Singapore University of Social Sciences (SUSS)
2018: Final-Year Project
2017 - 2018: Accenture's Campus Ambassadors 2017 - 2018
2016: Citi's Corporate Social Responsibility (CSR) Involvement
2016: Citi Mentorship Program 2016
2016: Japan Overseas Study Missions (OSM) Trip
2014 - 2017: Service-Learning / Activities
Republic Polytechnic (RP)
Specialist Diploma in Business Analytics (2018 - Present)
Diploma in Business Application (2011 - 2014)
Personal
Recommendations & Testimonials
Certifications & Awards from Republic Polytechnic
Additional Certifications Acquired
Personality Test
Hobby / Interest
Events Attended
Volunteer Experiences
2019 - Present: SUSS, Alumni Volunteer Groups
2012 - Present: Volunteer Special Constabulary (VSC)
2015 - 2018: Republic Polytechnic - Alumni Committee (RPAC)
Contact Me
CV / Resume
Interview Video
RyanGoh's Eportfolio
9. Principal Components Analysis (PCA)
9. Principal Components Analysis (PCA)
PCA is a data reduction technique that transforms a larger volume of correlated variables into a smaller set of uncorrelated variables called principal components
To determine the number of principal components:
Deployed a scree plot
Eigen value has to be greater than one
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