Why Data Science

Your greatest regret at the end of your life will be the lions you didn’t chase. You will look back longingly on risks not taken, opportunities not seized, and dreams not pursued. Stop running away from what scares you most and start chasing.

— Mark Batterson

I love this quote. I admit I enjoy chasing after opportunities that might seem improbable for me to take. There’s a certain kind of satisfaction in going after an unexpected challenge. 

I’m a brand strategist. My fluency is in cultural trends, behavioral insights, and creative solutions. (Read: I solve problems with words and images far more often than through spreadsheets and equations.) So, one could argue from a practical perspective, there’s no true need for someone like me to study data science.  

Personally, though, I’ve always had this fascination around TV shows like Halt and Catch Fire or movies like Moneyball and The Imitation Game. Why? Because they tell great stories but also because they celebrate the potential of vision paired with engineering skills. Protagonists in each story saw a potential and future most others could not, and they were willing to throw themselves into complexity and continuous experimentation to find a better way – be that a better way to video game, play baseball, or attack enemy lines.

Few think about branding like a system, but just as inputs flow through an algorithm, brand choices ideally flow through a single brand architecture and belief system that produces a justifiable response that overtime is consistent, memorable and ideally ownable. 

In short, not always but often there’s a role for engineering within advertising, and in hindsight, this would rationally explain why I decided to spend 6-months poured over a screen trying to study data science during a pandemic. 

And now, almost 6-months after graduating, I wanted to create a highlight reel of top takeaways, inspiration, and our teams’ final projects.  

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01 Organizing Data 

Medium Python with Pandas 

Goal Make the information usable 

Concept Bins, Conditionals, Dictionaries, Functions, Groupby

Practice 

• Analyzing survey data for beginners 
• 30 Days of Python: Conditional Basics
• Top Data Science Courses 

Brand Strategy Applications 

• D2C: Creating Age Bins for Customer Responses
• Binning customers based on their RFM scores 
• Analyzing Medium articles 

02 Charting Data Relationships 

Medium Pandas and Matplotlib with Python 

Goal Summarizing & comparing information    

Brand Applications & Resources 

• Instacart Market Basket Analysis 
• Mastering Matplotlib 

03 The Art of Data Visualizations 

Medium Tableau, PowerBI, etc.   

Goal Storytelling

Practice & Resources  

• Makeover Monday: Improving How We Visualize and Analyze Data
  • Find new data sets weekly here 

Data Visualization Portfolios

• Personalized Profile: 2019 Year in Books 
• John Burn-Murdoch: visual portfolio
• Uber’s head of data visualization 

Data Visualization Applications 

• Spotify Listen Together Campaign 
  • See the site // Fast Company 
• Vancuver Property Value 
• Largest Cities in the World 

04 Final Team Projects: 2U Data Science & Analytics Bootcamp 2020

• Defining Danceability with Spotify Data 
• Visualizing Real-Estate Choices 
• Predicting the next Blockbuster 

Image source: Datalands