Hi, I am Murong (Sophie) Cui.

call me Sophie

Welcome to

Sophie’s Site

I am Murong Cui and go by Sophie. I am a Data Analyst with a background in Mathematics and Statistics. I hold a Bachelor’s degree with a double major in Mathematics and Economics, and Master’s degree in Statistics (University of Colorado Denver, Boston University).

Getting insights out of data is something that I really like. And I am skilled at data Science/Analytics, Machine Learning, Business Intelligence, Cloud Technology.

01


Data Management

Improve performance, scalability, and stability of databases and application processes. Design, develop and maintain database.

02


Data Wrangling

Data Cleaning, Data Reshaping and Data Enrichment. Development of automated workflows.

03


Data Science

Build pragmatic, scalable, and statistically rigorous solutions to business problems by leveraging or developing statistical and machine learning methodologies.

04


Data Visualization

As a Tableau Desktop Certified Specialist, I worked with a diverse range of subjects and created static and interactive plots and dashboards and reviewing of scientific figures.

Blogs

Random Walk and Plotting
My Takeaway from MITx 6.00.2 One of the key idea in Data Science is Stochastic Thinking, which allow you estimate probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. Let's start by simulating a random walk Random Walk are …
Graph Centrality Measures – part 2 from Network Analysis
My Takeaway from module 3: Network Analysis MITx 6.419x (Data Analysis: Statistical Modeling and Computation in Applications) Introduce the notion of centralityIntroduce degree centrality and eigenvector centrality and study approaches to computing these measuresUnderstand how eigenvector centrality does not work for directed acyclic graphs (DAGs) and …
Graph Problems
My Takeaway from MITx 6.00.2 What's a Graph: Set of nodes (vertices)Might have properties associated with themSet of edges (arcs) each consisting of pair of nodesUndirected (graph)Directed (digraph)Source (parent) and destination (child) nodesUnweighted or weighted Why Graph: To capture useful relationships among entitiesRail link between Pairs …
Optimization, the Knapsack Problem, Decision Trees and Dynamic Programming
My Takeaway from MITx 6.00.2 You have limited strength, so there is a maximum weight knapsack that you can carryYou would like to take more stuff than you can carryHow do you choose which stuff to take and which to leave behind?Two variants0/1 knapsack problemContinuous or …