Bridget Hale | Data Analyst | Business Intelligence Project Portfolio

Bridget Hale

Who is

Freelance data analyst based in West Palm Beach, Florida with nearly twenty years of experience in the hospitality industry. Data Analysis & Culinarian graduate, quick on my feet, passionate team player & leader, have managed several teams to success. Aspire to apply my analytical skills to the food & beverage industry to better assist businesses in making the right datadriven & most profitable decisions.

Avocado Pricing Analysis

Excel-based project of Marketing analysis for a global video gaming company

Prepping for Flu

Season

Advanced Analytical Dashboard with Tableau & Python

GameCo

Tableau analysis for Medical staffing distribution based on historical trends In the U.S.

SQL business analysis for online movie rental service

Rockbuster Stealth LLC

Instacart

Grocery Basket Analysis

Python project on customer habits & targeted marketing

OBJECTIVE

AN IN-DEPTH EXPLORATORY ANALYSIS OF INSTACART’S SALES

TRENDS & CUSTOMER

HABITS TO FURTHER DEVELOP MARKETING & INCREASE REVENUE

DATA

CUSTOMER DATASET EXCEL DATA REPORT

INSTACART DATASET

LIMITATIONS

DATA RECORDS CEASED AFTER 2017

CUSTOMER DATA IS RESTRICTED TO ONLY GENERAL DEMOGRAPHICS SUCH AS AGE, INCOME, NUMBER IN HOUSEHOLD & MARRIAGE STATUS

SKILLS

DATA CLEANING, WRANGLING, SUBSETTING & CONSISTENCY CHECKS

MERGING & EXPORTING DATA

AGGREGATING & DERIVING NEW VARIABLES

PYTHON VISUALIZATION & EXCEL REPORT

TOOLS MS EXCEL, PYTHON, ANACONDA, PANDAS, NUMPY, JUPYTER, MATPLOTLIB, SKIPY & SEABORN

CONDITION:

max_order < 5

OBSERVATIONS TO BE REMOVED:

(First name, Last name)

1,440,295 removals

FINAL TOTAL COUNT OF ORDER_PRODUCTS_ALL :

30,964,564 records

Deriving New Variables

Creating sub-categories of products by price

Customer shopping habits by department & household income. Customer profiles grouped by geographical regions Crosstabs derived from merged data frames to better correlate relations between variables

Visualizations created with SkiPy, Matplotlib & Seaborn

Advertising: Scheduling advertising during slowest hours & days of the week

Slowest Hours: 3:00 – 6:00 PM Slowest Days: Mon-Thurs

Promotions: Busiest time for orders is during regular hours (10 AM-2 PM) & weekends. Promotions can include BOGOS to lead the user to believe they’re getting a deal

Products: Most popular items are groceries, specifically produce items like dairy & eggs as well as snacks. Ads should focus on nongrocery products to increase lagging sales

Loyalty: Majority of users are regular & new. Implementing loyalty rewards/points could incentivize less frequent users to shop more often

Segmentation: Data demonstrates a difference in customer categories. Ads should target specific demographics such as pet owners & families with babies

Data Analysis

OBJECTIVE

INTERNATIONAL MOVIE RENTAL COMPANY LOOKING TO SEGUE FROM PHYSICAL STORES TO AN ONLINE RENTAL MODEL. ROCKBUSTER HAS CALLED ON THE ANALYSIS TEAM TO HELP LAUNCH THE PROPER COURSE STRATEGY FOR TRANSFERRING PREEXISTING DATA SUCH AS FILMS, CUSTOMERS, EMPLOYEES & REVENUE TO THE NEW ONLINE SYSTEM

DATA

SKILLS

ROCKBUSTER DATABASE

ROCKBUSTER ERD

ROCKBUSTER DATA

DICTIONARY

-USING CRUD OPERATIONS, RELATIONAL DATABASE & AN ENTITY RELATIONSHIP DIAGRAM (ERD) TO SORT, GROUP, AGGREGATE & EXPORT TO EXTRACT & MANIPULATE DATA

-USING ADVANCED QUERIES IN POSTRESQL TO JOIN TABLES

-STORYTELLING WITH DASHBOARDS

TOOLS NAVIGATING THE DATABASE

VISUALIZATIONS:

TABLEAU PUBLIC

LUCIDCHART

MS EXCEL

POSTGRESQL

PRESENTATION: MS POWER POINT

• LucidChart ERD presents relations between tables

• Data Dictionary identifies variables for reader as well as provides source of data type for each variable

Entity Relationship Diagram (ERD) Data Dictionary

Click for entire Data Dictionary

INVENTORY: Catalog is inconsistent, further analysis needs to be performed to determine new insights & historical patterns of higher revenue inventory so that we may remove obsolete films (such as Texas Watch & Duffel Apocalypse) to onboard more in-demand options

BRANDING: Top categories included Sports, Sci-Fi & Animation. Company needs to have ample streaming options available of these genres on new platform to help initiate sales

MARKETING: Regional sales performed best in India, China, The U.S., Japan & Mexico; Advertising/Marketing team should pursue these areas aggressively as Rockbuster launches their new platform

LOYALTY PROGRAM: There have been loyal customers at the brick & mortar stores, now that the company is merging to an online system, Rockbuster should consider incentivizing a rewards program for roll-over customers

OBJECTIVE EXPLORING & IDENTIFYING

SEASONAL FLU TRENDS IN THE U.S. TO BETTER PREPARE MEDICAL STAFF & HEALTH FACILITIES FOR THE UPCOMING INFLUENZA

SEASON

DATA POPULATION

DATASET DERIVED FROM THE U.S.CENSUS. INFLUENZA DEATH

RATES, LAB TESTS & CLINICAL VISIT

REPORTS PROVIDED BY THE CDC

LIMITATIONS

DATA IS CREDIBLE AS IT WAS OUTSOURCED BY A GOVERNMENT ORGANIZATION (THE CDC) HOWEVER, THE DATA IS OUTDATED AS IT HALTED IN 2017. ALSO WORTH NOTING, DATA IS PREPANDEMIC

SKILLS DATA CLEANING & PROFILING FOR STATISTICAL ANALYSIS.

TIME-SERIES

FORECASTING & INTERACTIVE VISUALIZATIONS PERFORMED FOR DASHBOARD

STORYTELLING

TOOLS MICROSOFT EXCEL VLOOKUP & TABLEAU

Vulnerable population (over 65) develop complications from the flu. The analysis team is tasked with preparing hospitals to adequately aid medical staffing & facilities

Vulnerable population represents highest ILI related deaths

Total flu deaths in 2017 by Month & Age Group

Vulnerable age group is over 65 years old; seniors make up over 2/3 of flu deaths

California, New York & Texas top 3 states with highest influenza mortality rates

For full presentation click here

Preparation: Ensure health facilities are equipped with proper medical resources before upcoming flu season

Staffing: Medical staff needed in vulnerable states with highest mortality rates like New York, California & Texas; particularly during Flu season (November-March)

Monitoring: Keep eye on changes in flu outbreaks/deaths post-pandemic. Staffing aid may need to relocate now that Covid-19 has skewed flu outbreaks

Next Steps: Apply new findings to offer better resources for vulnerable patients, such as vaccine information & availability

OBJECTIVE

VIDEO GAME COMPANY

STAKEHOLDERS ARE CALLING ON THE DATA TEAM TO PERFORM DESCRIPTIVE ANALYSIS ON CURRENT & PAST VIDEO GAME SALES BY POPULARITY & REGION IN ORDER TO DEVELOP INSIGHTS FOR LAUNCHING NEW GAMES IN THE MARKET

DATA

SKILLS DATA PROFILING & CLEANING, REMOVING DUPLICATES GROUPING & AGGREGATING IN MS EXCEL PIVOT

TABLES

PRESENTING FINDINGS IN POWERPOINT PRESENTATIONS

TOOLS

MS EXCEL: DATA CLEANING & VISUALIZATIONS

POWERPOINT: STAKEHOLDERS

PRESENTATIONS

Share of Regional Sales in 2006 & 2016

Gaming System Platforms

Trend indicates decreasing sales over past decade across 3 top performing regions

Most popular genres across the top 3 regions include Action, Sports & Shooter games

In 2016 Europe overtook North American sales

2016 Regional Sales

13.7%

22.66%

Top 3 Regions

North American Sales

European Sales

26.76%

Japanese Sales

Budgeting: Capitalize on top selling genres like Action, Sports & Shooter games across most profitable regions

Advertising: Further marketing investigation in Europe to focus on upswing of sells, consider increasing revenue where demand is highest

Growth: Investigate as to why there has been a steep decline in North American & Japanese sales to pursue a bigger reach on customers by way of newer releases

Next Steps: With the consistent decrease in sales, GameCo should consider implementing gaming apps as an answer to slumping sales over past decade

OBJECTIVE THESE DAYS THE AVOCADO IS KING , BUT WHAT DOES THE FUTURE HOLD FOR THIS VERSATILE & IN DEMAND

PRODUCT? IN THIS ANALYSIS WE’LL TAKE A LOOK AT PRICES & DEMAND ACROSS THE US

DATA

Avocado Prices Kaggle

Dataset

Clean Excel Dataset

SKILLS DATA CLEANING, WRANGLING & CONSISTENCY CHECKS.

ADVANCED VISUAL ANALYTICS WITH GEOSPATIAL, CLUSTERING & LINEAR

REGRESSION

ANALYSIS

TOOLS

PYTHON

JUPYTER NOTEBOOK

PANDAS

NUMPY MATPLOTLIB

SEABORN

FOLIUM

Ms EXCEL

TABLEAU PUBLIC

A correlation heatmap showcases through shaded colors the strength of relationships between all variables

Is there a linear relationship between sizes sold by volume?

Regional National Average $1.40

Highest Average Priced Regions:

San Francisco $3.25

Tampa $3.17

Miami/Ft. Lauderdale $3.12

Lowest Average Priced Regions:

Cincinnati/Dayton $0.44

Phoenix $0.46

Detroit $0.48

1st line demonstrates the level; where we see data along with its components

2nd line demonstrates the trend; it steadily increases over time 3rd line demonstrates the seasonality. Peaks are observed in summer months and dips in the winter (seasonality will be especially important in this data analysis as the product will have higher availability in certain months compared to others

4th line demonstrates the Resid. Shows consistent pattern throughout the year

Decomposing a time - series into its main components is how we can properly analyze individual factors such as trend & seasonality

When 2 variables show a linear relationship, we can allow for algorithms to perform predictive analysis based on test variables

Advertising: Highest priced avocados are in California & Florida, which also have the largest distribution; thus, people want their avocados regardless of price, marketing team should target their whales & continue to grow their regions

Relationships: According to our analysis, there is a strong positive correlation between total volume & size of avocado sold. Extra large conventional avocados have the highest distribution

Next Steps: Marketing team should focus on mass producing avocados into commercial chains, avocado as toppings, smoothies, side of guac, the demand is there, it just needs to be supplied to the consumer, current & new

PLEASE REACH ME AT THE FOLLOWING: BRIDGETHALE27@GMAIL.COM

BRIHAL4457@YAHOO.COM