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Articles by Francis Adrian Viernes
Financial Data and Its Complications: How to Expertly Preprocess Financial Data - A Comprehensive Guide
Finance. Data Science. Machine Learning. Analytics How to Handle the Complexities and Cleverly Handle Financial Datasets It is no secret in the world of data science that one of the secrets to producing great data science outputs is clean data. As they say, “Garbage in, garbage out”. We should stop, however, romanticizing data in real life: data is never really clean, even in corporate or big businesses.
Evolution in Your Code
Optimization. Data Science. Algorithms Introduction - The Inspiration Behind the Algorithm Have you ever looked at nature and wondered at how organisms evolved, adapted, and survived over millennia? What if I told you that you, sitting in front of your computer, hold the power to simulate a similar process of evolution, not over millennia, but in mere seconds? This is of course, through, genetic algorithms.
Linear Optimization Using Popular Python Packages
FINANCE. DATA SCIENCE. OPTIMIZATION. PORTFOLIO OPTIMIZATION Introduction - The Use of Packages If you have been following the linear programming series, welcome back. This is the third installment of the series, where we solve the same problem but this time using the popular packages in Python for solving Linear programming problems. You may skip to the coding portion for each of the packages for the code.
Simplex Tableau From Scratch in Python
FINANCE. DATA SCIENCE. OPTIMIZATION. PORTFOLIO OPTIMIZATION If you are here from the previous article detailing how to solve a linear programming problem using the simplex tableau method using Excel, welcome back! You can skip this portion and go straight to the coding portion. For those who are first-time readers, this article will attempt to solve the same linear programming problem from the previous article: Linear Programming Series - Simplex Tableau in Excel, only this time, using Python.
Linear Programming with Python Series
FINANCE. DATA SCIENCE. OPTIMIZATION. PORTFOLIO OPTIMIZATION Python and programming aren't the only ways to perform optimization and linear programming nowadays. Tools like Microsoft Excel now have devices that allow analysts to perform amazing feats in analytics and programming. When performing linear optimization ( even nonlinear) in Excel, the best way is to use Solver. This article, however, will not be about Solve. In this article, I will demonstrate how the Simplex method algorithm works.
The Battle of Choropleths - Part 3 - Folium
PYTHON. DATA SCIENCE. GEOVISUALIZATION Using the Folium Package to Create Stunning Choropleths In the past two articles in this series, we were able to create choropleths from Geopandas and Altair packages. Let us continue our learning but this time let us use the folium package. In this article, we have introduced the folium package as well as the advantages and disadvantages of the package.
The Battle of Choropleths - Part 2 - Altair
PYTHON. DATA SCIENCE. GEOVISUALIZATION Using Altair to Create Stunning Choropleths We first used the Altair Package in our article: The Battle of Interactive Geographic Visualization Part 4 — Altair. For those starting with this article, Altair is being likened to Plotly in that it is a high-level declarative language. This means Altair would be easier to use but harder to control in certain portions.
The Battle of Choropleths - Part 1
PYTHON. DATA SCIENCE. GEOVISUALIZATION Using Geopandas to Create Stunning Choropleths So far, in our article series, we have been discussing how to create interactive geoplots and we have discussed seven (7) options, covering a wide range of tools that one can use depending on the preferred ease of use and customizability. The options for choropleths are fewer compared to geoscatterplots and thus for our article series, we will include even the non-interactive ones.
The Battle of Interactive Geographic Visualization Part 7 - Bokeh
PYTHON. DATA SCIENCE. GEOVISUALIZATION Using the Bokeh Library to Create Beautiful, Interactive Geoplots Photo by delfi de la Rua on Unsplash In this series, we have already identified, six (6) ways of making beautiful geoscatter plots.
The Battle of Interactive Geographic Visualization Part 6 - Greppo
PYTHON. DATA SCIENCE. GEOVISUALIZATION Using the Greppo Library to Create Beautiful, Interactive Geoplots Photo by Tobias on Unsplash In this series, we have already identified, five (5) ways of making beautiful geoscatter plots.
The Battle of Interactive Geographic Visualization Part 5 - Folium
PYTHON. DATA SCIENCE. GEOVISUALIZATION.
The Battle of Interactive Geographic Visualization Part 4 - Altair
PYTHON. DATA SCIENCE. GEOVISUALIZATION. Using the Altair Package to Create Beautiful, Interactive Geoplots Photo by Clay Banks on Unsplash In this series, we have already identified three (3) ways of making beautiful geoscatter plots.
The Battle of Interactive Geographic Visualization Part 2- Interactive Geoplot Using One Line of Code
PYTHON. DATA SCIENCE. GEOVISUALIZATION Photo by Timo Wielink on Unsplash In the last article: The Battle of Interactive Geographic Visualization Part 1 — Interactive Geoplot Using One Line of Code, we discussed the importance of producing geoplots/geovisualization for data exploration as it may reveal interesting insights and patterns of the data that may elevate your data science projects to a whole new level.
The Battle of Interactive Geographic Visualization Part 1 - Interactive Geoplot Using One Line of Code
PYTHON. DATA SCIENCE. GEOVISUALIZATION Photo by GeoJango Maps on Unsplash Geospatial data is probably one of the most complex forms of data that currently exists. For one reason, this form of data is not used in the usual conversations between analysts. For example, analysts do not usually talk about latitude and longitude, and/or, shapefiles. For another reason, the pattern exhibited by geospatial data is not easily apparent.
Calculating the Distance between Two Locations Using Geocodes
PYTHON. GEOSPATIAL ANALYTICS. LOCATION DATA. Photo by Tamas Tuzes-Katai on Unsplash You’ve heard the famous phrase “Location, Location, Location” for instances where people want to emphasize the centrality of the location to business and real estate. In data analysis and computing, however, this phrase is a bit ambiguous. The way computers understand the concept of “location” is through what we know as “geocodes.” These are the longitudes and latitudes and are specific to a particular location.
Generating Geocodes Using Google Maps API
GEOSPATIAL ANALYSIS. DATA SCIENCE. ANALYTICS.PYTHON. Photo by KOBU Agency on Unsplash Geocoding is the process of translating text addresses into geographic latitude and longitude coordinates which in turn make it easy to manipulate and analyze massive amounts of geospatial data. For a data scientist, knowing the geocode makes it easy to plot it in visualization and create other features, such as distances and time differences between two points.
Never Skip This Step in Your Exploratory Data Analysis (EDA)!
DATA SCIENCE. STATISTICS. EDA. PYTHON. Photo by Lucas Sankey on Unsplash If you are new to data science and have taken a course to do preliminary data analysis, chances are one of the first steps taught into doing exploratory data analysis (EDA) is to view the summary / descriptive statistics. But what do we really intend to accomplish with this step? Summary statistics are important because they tell you two things that are important for modeling data: location and scale parameters.
Nonlinear Optimization Using Halley’s Method
Optimization. Data Science. Analytics. Python. Photo by Volkan Olmez on Unsplash Halley’s Method for solving nonlinear equations is considered by some as an extension of Newton’s method. The story for its discovery is quite wonderful actually but we leave that for a separate reading. The advantage of this over other optimization algorithms is that this is considered to converge faster. The hidden cost, however, is that it requires the calculation of the second derivative function.
Handling “Missing Data” Like a Pro - Part 3: Model-Based & Multiple Imputation Methods
DATA SCIENCE. ANALYTICS. PYTHON Photo by Matt Walsh on Unsplash As we mentioned in the first article in a series dedicated to the study of missing data, the knowledge of the mechanism or structure of “missingness” is crucial because our handling method would primarily depend on it. In Handling “Missing Data” Like a Pro — Part 1 — Deletion Methods, we have discussed deletion methods. In Handling “Missing Data” Like a Pro — Part 2: Imputation Methods, we discussed simple imputation methods.
Handling “Missing Data” Like a Pro - Part 2: Imputation Methods
Data Science. Analytics. Statistics. Python. Photo by Jon Tyson on Unsplash As we mentioned in the first article in a series dedicated to missing data, the knowledge of the mechanism or structure of “missingness” is crucial because our responses would depend on them. In Handling “Missing Data” Like a Pro — Part 1 — Deletion Methods, we have discussed deletion methods. For this part of the article, we will be focusing on imputation methods.
Handling “Missing Data” Like a Pro - Part 1 - Deletion Methods
Data Science. Analytics. Statistics. Python. Photo by Emily Morter on Unsplash As we mentioned in the first article in a series dedicated to missing data, the knowledge of the mechanism or structure of “missingness” is crucial because our responses would depend on them. While the list of techniques is growing for handling missing data, we discuss some of the most basic to the most celebrated techniques below.
Marketing Analytics Visualization - WordCloud
PYTHON. DATA VISUALIZATION. ANALYTICS. MARKETING. Image by Author WordCloud displays the most frequent words used in a text where the size of the text is proportional to the frequency it was used in the text; the larger the font, the more times the word appeared in the document. So for an exploratory data analysis, WordCloud may provide some interesting insights to follow up on or investigate For this exercise, let us try the Tripadvisor_Hotel_Review dataset.
Data Visualization Hack -Lessons from FiveThirtyEight Graphs
PYTHON. DATA VISUALIZATION. CHARTS Photo by Clay Banks on Unsplash FiveThirtyEight (sometimes written as 538), is a website that produces opinion polls on topics such as politics, economics, and sports. It takes its name from the number of electors in the United States electoral college. But one of the things that contribute to their popularity is the effectiveness of their visualization in relaying poll results.
Data Scientists Must Embrace Mathematics
Data Science, Mathematics The data scientist needs to embrace mathematics in order to build reliable models using data Image by Benjamin O. Tayo Data science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data. The field of data science has several subdivisions such as data mining, data transformation, data visualization, machine learning, deep learning, etc.
Optimize Your Company’s Cash Flow with Scipy
PYTHON. OPTIMIZATION. OPERATIONS RESEARCH. FINANCIAL ANALYTICS Using Scipy and Linear Programming to Solve For Your Optimal Budget Photo by Sharon McCutcheon on Unsplash Cash flow plays a key role in the success of the company’s operations. While cash flow obligations may be fixed, there are multiple ways to meet these such as borrowing from a line of credit or raising short-term commercial paper.
How Important Are Technical Skills in Hiring a Data Science Leader?
LEADERSHIP. TEAMS. DATA SCIENCE. A Guide on Making the Critical Leadership Decision Photo by Jo Szczepanska on Unsplash The Fourth Industrial Revolution has, in no doubt, awakened the importance of having a data strategy for companies. Companies are either figuring out whether to build their own or to outsource data science (DS) capabilities. Photo by Markus Winkler on Unsplash For those building their data science teams, the most important decision is the choice of the leader.
Improving the Performance of Your Imbalanced Machine Learning Classifiers
MACHINE LEARNING. DATA SCIENCE. PYTHON. One of the mistakes I made as a rookie data scientist was placing heightened importance on the accuracy metric. Now, this is not to dismiss the importance of accuracy as a measure of machine learning (ML) performance. In some models, we aim to have high accuracy. After all, this metric is the one most understood by executive and business leaders.
Spatial Distance and Machine Learning
“Life is like a landscape. You live in the midst of it but can describe it only from the vantage point of distance” — Charles Lindbergh Distance metrics are essential in understanding a lot of machine learning algorithms and therefore resolution of real-world problems. There are numerous distance metrics out there and data scientists should be able to understand most of them to make models more meaningful.
US Home Sales, House Prices, Homeownership Rates
Markets in 162 of the approximately 340 metro areas across the US returned to or exceeded their last normal levels of economic and housing activity in 3Q 2016, ac- cording to the National Association of Home Builders/First American Leading Markets Index (LMI) released Tuesday.In 2017, sales of existing homes are forecast to grow roughly 2 per cent to around 5.46 million and continue with a more prominent jump of 4 per cent in 2018 (5.68 million), according to the National Association of...
Designing for Digital Twins
Photo by Faye Cornish on Unsplash The future of Digital Twins is a diverse one — both in meaning and in set-up. Ignoring this reality will hamper the success of Digital Twins. Here, the focus is on set-up and design considerations for successful implementations of Digital Twins within a disperse service and data landscape (thus excluding here the IoT ingest and data model part). An initial technology mapping is presented. Digital Twin What is regarded as a Digital Twin depends on its goal.
Visualizing Built-Up Areas Using Satellite Images
GEOVISUALIZATION / PHILIPPINES / REMOTE-SENSING Using Satellite Images From Google Earth Engine to View Built-Up Areas Over Time Big data is incredible! The way Big Data manages to bring sciences and business domains to new levels is almost sort of magical. It allows us to tap into a variety of avenues to access the information we normally do not access in order to gain fresh insights. Satellite images are an amazing unconventional source to tap into for these fresh insights.
Visualizing the Philippines’ Population Density using GeoPandas
GeoVisualization /Philippines. Using GeoPandas to Visualize the 2015 Population Density of the Philippines Population density is a crucial concept in urban planning. Theories on how it affects economic growth are divided. Some claim, as Rappaport does, that an economy is a form of “spatial equilibrium”: that net flows of residents and employment gradually move to be balanced with one another.
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