A Study on Strengths and Drawbacks for the Different Approaches (With Sample Code)

Note: The code for this post can be found here

Sentiment Analysis (image by Author)

Sentiment Analysis, or Opinion Mining, is a subfield of NLP (Natural Language Processing) that aims to extract attitudes, appraisals, opinions, and emotions from text. Inspired by the rapid migration of customer interactions to digital formats e.g. emails, chat rooms, social media posts, comments, reviews, and surveys, Sentiment Analysis has become an integral part of analytics organizations must perform to understand how they are positioned in the market. To be clear, Sentiment Analysis isn’t a novel concept. In fact, it has always been an important part of CRM (Customer Relationship Management)…


With Sample Code for Enhancements to Inspire Your Charting Creativity

Note: The code for this post can be found here

Different Approaches to Visualizing a Distribution (Image by Author)

Understanding how the variables are distributed in the data is an important step and should happen early in the Exploratory Data Analysis (EDA) process. There are a number of tools available to analyze the distribution of data. Visualization aids are likely the most popular because a well constructed chart can quickly answer important questions regarding the data. For example:

  • What are the central tendencies? mean, median, and mode(s).
  • What are the dispersion measures? range, IQR, variance, and standard deviation.
  • What are the shapes of the distributions? …


An Analysis Using Tableau

Closer Look at Airlines Safety (image by Holger Detje from Pixabay)

Inspired by the recovery of air traffic demand, I looked at airline safety records to see if there are noticeable patterns. Many consumers say or at least claim that they would avoid airlines that have had incidents in the past. In my analysis, I look to explore the validity of this thinking. The Story (Tableau) can be found here:


Step-by-Step Guide to Build an API w/ Python, Flask, and MongoDB on Heroku

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Build SpaceX Launch History Web API with Python, Flask, and MongoDB (image by Author)

API stands for Application Programming Interface. It is a software intermediary that allows systems to communicate with each other. For example, when a user enters a URL into their browser, e.g. www.medium.com, they are making a GET request to Medium’s server. Medium will then give back a response, which may contain HTML, CSS, JavaScripts, or other assets to be consumed, executed, and rendered on the user’s system. Similarly, when a system makes a request to SpaceX’s Launches…


Step-By-Step Guide To Build and Deploy a Data Collection App w/ Python and PostgreSQL on Heroku

Note: The code for this post can be found here

Scheduled Data Collection Application Workflow (image by Author)

In spite of the Data Proliferation observed in recent years, too often do we find ourselves being roadblocked by the lack of data for a project. Throughout a typical day of web browsing, we come across countless pieces of information (data) that hold potential for great Data Science ideas such as current weather, traffic, disaster warning, stock prices, vote counts, covid counts, view counts, social media comments — the list goes on. We know that the data is maintained and made available for public consumption, but a convenient compilation is…


2-Part Guided Case Study using Student/Customer Satisfaction Survey Data

Note: The code for this post can be found here

Closer Look at Rating Scales (image by author)

Rating Scales are an effective and popular way to gauge attitudes and opinions. They are easy to implement and widely used in surveys, feedback forms, and performance evaluations. Yet, misuses and mistakes often occur in the implementation and analysis of this seemingly intuitive tool. The ability to understand and synthesize information from Rating Scales empowers decision making in an ever-changing environment. The goal of this 2-part series is to demonstrate basic concepts needed to effectively utilize Rating Scales data as well as warn about common pitfalls. …


Sharing A Snippet to Experiment Plot Configurations Quickly

Note: The code for this post can be found here

In Data Analytics/Science, the Exploratory Data Analysis process generally includes constructing visualizations that represent and summarize the data set. Good visualizations can be very powerful in describing complex trends and stats in the data, but finding them can be an iterative process. The goal of this short article is to share how I use subplots to quickly experiment different configurations to create charts that tell great stories.

Quickly Creating Charts w/ Subplots (Image by Author)

The anatomy of a matplotlib object includes a Figure, an Axes, and the Plot itself. Typically for a single plot, the plot sits…


With Performance Comparison Analysis and Guided Example of Animated 3D Wireframe Plot

Note: The code for this post can be found here

Photo by By_Jo on Pixabay

Python is famous for its vast selection of libraries and resources from the open-source community. As a Data Analyst/Engineer/Scientist, one might be familiar with popular packages such as Numpy, Pandas, Scikit-learn, Keras, and TensorFlow. Together these modules help us extract value out of data and propels the field of analytics. As data continue to become larger and more complex, one other element to consider is a framework dedicated to processing Big Data, such as Apache Spark. In this article, I will demonstrate the capabilities of distributed/cluster computing and present a…


Explore the Motivation and Capabilities of ML Through the Game of Rock Paper Scissors

Note: The code for this post can be found here

Machine Learning with the Rock Paper Scissors Game (Image by Author)

In this article, we’re going to build a simple Rock Paper Scissors game in Python with two different approaches: Rules-Based System vs. Machine Learning. Through this comparison, I hope to express how Machine Learning works and its motivations. To be clear, this is not an illustration of Image Recognition or Pattern Recognition (which hand will a player choose next) but rather Machine Learning concepts in general. As automation continues to revolutionizing the future of work across industries, companies must explore different ways to streamline their operations. …


Guided Example of Model Deployment using Python and Flask

Note: The code for this post can be found here

Photo by mohamed_hassan on Pixabay

The last step in the Machine Learning Life Cycle is to put the model into production, also known as “operationalizing” the model. It often means enabling the model to generate outputs based on new data given. In the context of a real-world application, to deploy the Machine Learning model into production is to integrate it into the existing environment, allowing other systems to call it for making inferences. …

Kevin C Lee

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