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$40 USD / hour
Flag of UNITED STATES
santa monica, united states
$40 USD / hour
It's currently 8:27 PM here
Joined February 10, 2019
0 Recommendations

Charla M.

@CharlaMyers

 5.0 (1 review)
1.9
1.9
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100%
$40 USD / hour
Flag of UNITED STATES
santa monica, united states
$40 USD / hour
100%
Jobs Completed
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Repeat Hire Rate

Data Storyteller: Writing code & narratives that bring meaning to the information.

With my focus on storytelling, I shine at shaping large datasets into business narratives that impact decisions. Whether diving into purchasing information or conducting a sentiment analysis , I absorb and dig into ideas to discover the Who, What, How, and (most importantly) Why of what's in the data. Plus, as a former content strategist / UX writer, I'm an influential communicator able to gain buy-in from all levels of stakeholders. If you like talking about emerging trends in data storytelling and analytics—or share my natural curiosity for new technologies—I welcome the chance to connect with you here or at [login to view URL].
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Portfolio

4757007
Created Python scripts to analyze over 7 years of Hawaii weather data—focusing in on a specific 12-month period to look at precipitation, temperature, and weather station locations. TOOLS: Excel, Python, Numpy, Datetime, Matplotlib, SQLAlchemy, and ORM queries IMAGE CAPTIONS: 1) At-a-glance bar chart for the amount of rain Hawaii received in one year. 2) Histogram for the temperatures of Hawaii weather stations in one year.
Aloha Weather: Climate Analysis
Created Python scripts to analyze over 7 years of Hawaii weather data—focusing in on a specific 12-month period to look at precipitation, temperature, and weather station locations. TOOLS: Excel, Python, Numpy, Datetime, Matplotlib, SQLAlchemy, and ORM queries IMAGE CAPTIONS: 1) At-a-glance bar chart for the amount of rain Hawaii received in one year. 2) Histogram for the temperatures of Hawaii weather stations in one year.
Aloha Weather: Climate Analysis
4756986
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
4757001
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
4756995
Wrote Python code to perform a sentiment analysis of various "big name" news outlets (BBC, CBS, CNN, Fox, and New York Times) on Twitter, and then created a visualized summary of results. TOOLS: Excel, Python, Tweepy, Numpy, Datetime, and Matplotlib​ IMAGE CAPTIONS: 1) Plotted sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter. 2) Compound score of sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter.
Big News: Sentiment Analysis on Twitter
Wrote Python code to perform a sentiment analysis of various "big name" news outlets (BBC, CBS, CNN, Fox, and New York Times) on Twitter, and then created a visualized summary of results. TOOLS: Excel, Python, Tweepy, Numpy, Datetime, and Matplotlib​ IMAGE CAPTIONS: 1) Plotted sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter. 2) Compound score of sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter.
Big News: Sentiment Analysis on Twitter
4756981
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
4757007
Created Python scripts to analyze over 7 years of Hawaii weather data—focusing in on a specific 12-month period to look at precipitation, temperature, and weather station locations. TOOLS: Excel, Python, Numpy, Datetime, Matplotlib, SQLAlchemy, and ORM queries IMAGE CAPTIONS: 1) At-a-glance bar chart for the amount of rain Hawaii received in one year. 2) Histogram for the temperatures of Hawaii weather stations in one year.
Aloha Weather: Climate Analysis
Created Python scripts to analyze over 7 years of Hawaii weather data—focusing in on a specific 12-month period to look at precipitation, temperature, and weather station locations. TOOLS: Excel, Python, Numpy, Datetime, Matplotlib, SQLAlchemy, and ORM queries IMAGE CAPTIONS: 1) At-a-glance bar chart for the amount of rain Hawaii received in one year. 2) Histogram for the temperatures of Hawaii weather stations in one year.
Aloha Weather: Climate Analysis
4756986
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
Using Python and its various libraries, I cleaned this large dataset for a (fake) digital game. The analysis could be used to better understand the gaming audience (gender, age) as well as retail analysis for in-game purchases (what's popular, what's the price-point). TOOLS: Excel, Python, Pandas, and Numpy IMAGE CAPTIONS: 1 )What age group(s) play this game? 2) How much money is each gender spending in the game? 3) These are the most popular in-game purchases. 4) These are the most profitable in-game purchases.
​Pymoli: Gaming Audience & Purchase Analysis
4757001
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
Analyzed (renamed) rideshare data with a breakdown of ridership and fares per city, number of drivers per city, location types (urban, suburban, and rural). TOOLS: Excel, Python, Pandas, Numpy, and Matplotlib IMAGE CAPTIONS: 1) Created with matplotlib to show average fare per city versus number of rides by geography type (urban, suburban, rural) and number of drivers in a city. 2) Shows where fares are most common (urban, suburban, rural). 3) Shows where drivers are most common (urban, suburban, rural). 4) Full breakdown by geography type, each city, number of drivers, number of rides, and average fare.
Pyber: Rideshare Analysis
4756995
Wrote Python code to perform a sentiment analysis of various "big name" news outlets (BBC, CBS, CNN, Fox, and New York Times) on Twitter, and then created a visualized summary of results. TOOLS: Excel, Python, Tweepy, Numpy, Datetime, and Matplotlib​ IMAGE CAPTIONS: 1) Plotted sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter. 2) Compound score of sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter.
Big News: Sentiment Analysis on Twitter
Wrote Python code to perform a sentiment analysis of various "big name" news outlets (BBC, CBS, CNN, Fox, and New York Times) on Twitter, and then created a visualized summary of results. TOOLS: Excel, Python, Tweepy, Numpy, Datetime, and Matplotlib​ IMAGE CAPTIONS: 1) Plotted sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter. 2) Compound score of sentiment analysis for BBC, CBS News, CNN, Fox News, and New York Times on Twitter.
Big News: Sentiment Analysis on Twitter
4756981
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis
After extracting, combining, and cleaning millions of lines of information with Python, I imported the "clean" data into Tableau to create a story. Looking at every June for the years 2013–2018, the ridership analysis included: gender, age, trip duration, and station popularity (interactive maps). TOOLS: Excel, Python, Pandas, Numpy, Datetime, and Tableau IMAGE CAPTIONS: 1) Chart that compares rider age to trip duration. 2) Chart of the most popular stations during June of analyzed years. 3) Interactive map of the most popular stations during June of analyzed years. 4) Map of the annual most popular stations in June.
​Citibike: Year-Over-Year Ridership Analysis

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GDPR in mobile healthcare applications
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@Aroja99
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5 years ago

Experience

Data Storyteller

Flexible Thoughts
Jun 2018 - Present
With my focus on storytelling, I shine at shaping large datasets into business narratives that impact decisions. Whether diving into purchasing information or conducting a sentiment analysis, I absorb and dig into ideas to discover the Who, What, How, and (most importantly) Why of what's in the data.

Content Designer

(Previous Career)
Jul 1995 - Mar 2018 (22 years, 8 months)
For 15+ years, I focused on digital experiences that balanced content strategy and creation with user experience and design. • Freelanced for start-ups, such as: Liquidnet, Ray Super Remote, and Pellucid | Los Angeles, 2011–2018 • Developed content strategy, UX writing and copy for Elastic, Warner Music Group, Viking Ocean Cruises, LG, and more.

Education

Certificate in Data Analytics from Viterbi School of Engineering

University of Southern California, United States 2018
(Less than a year)

Bachelor of Science in Film from College of Communication

Boston University, United States 1990 - 1994
(4 years)

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Top Skills

MySQL 1
Python
Excel
Data Science
Tableau

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