Tutorial #1
You’ll take on the role of a data analyst and work with a real dataset to solve a business challenge. By the end of the course, you will:
Be familiar with all the key steps in the data analysis process
Understand, and be able to apply, some fundamental analysis techniques
Have a first-hand glimpse of what it’s like to work as a data analyst
So how does it work, and what’s in store?
What is data analytics?
What does a data analyst do?
Getting ready: What tools do you need for this course?
Practical exercise: Opening your dataset
Key takeaways and further reading
1. What is data analytics?
It’s the process of analyzing raw data in order to draw out meaningful, actionable insights. It’s a form of business intelligence, enabling companies and organizations to make smart decisions based on what the data is telling them.
Data analytics encompasses
the extraction (or collection) of raw data
the preparation and subsequent analysis of that data,
and storytelling—sharing key insights from the data, using them to explain or predict certain scenarios and outcomes, and to inform decisions, strategies, and next steps.
Let’s bring it to life with an example.
Imagine you’re a data analyst working for a public transport network—think MTA in New York City, or TFL in London. There’s a major sporting event coming up in the city, and you know that people will be flying in from all over to attend. In order to avoid absolute chaos, you need to adapt the usual public transport schedule to accommodate for this influx of people and increase in travel throughout the city. How do you plan ahead with accuracy? 🤔
You guessed it…data analytics! You analyze data from similar events that have happened in the past and use it to predict the number, frequency, and types of journeys that are likely to occur around this event. With these insights, you’re able to ensure that public transportation continues to run smoothly 🚌
You’ll find a more comprehensive explanation of what data analytics is in this guide, and it’s also worth reading up on the four different types of data analysis.
2. What does a data analyst do?
As a data analyst, it’s your job to turn raw data into meaningful insights. Any kind of data analysis usually starts with a specific problem you want to solve, or a question you need to answer—for example, “Why did we lose so many customers in the last quarter?” or “Why are patients dropping out of their therapy programs at the halfway mark?”
To find the insights and answers you need, you’ll generally go through the following steps:
Define your question or problem statement
Collect the necessary raw data
Clean the data so that it’s ready for analysis
Analyze the data
Create visualizations
Share your findings
At each stage, data analysts use a range of different tools—such as Microsoft Excel for data wrangling and Tableau for data visualizations. We explain each step in more detail in this guide to the data analysis process, and you’ll apply it first-hand throughout this course 😊
As a data analyst, you’re the bridge between incomprehensible raw data and useful insights, empowering people in all areas of the organization to make smarter decisions and ultimately reach their goals. As such, you’ll work closely with managers, product owners, and department leads to identify goals, prioritize needs, and shape strategies. And, in addition to actually analyzing data, you may also be responsible for building databases and dashboards, ensuring data quality and best practices, and maintaining relevant documentation.
Where can data analysts work?
Data analysts work anywhere and everywhere. They are critical to almost any kind of organization and industry you can think of—from large corporations to fledgling startups, from financial institutions to government, healthcare, and non-profit organizations. Wherever data is being collected (and that’s pretty much everywhere these days!), there’s a need for data analysts.
For a closer look at where a career in data might take you, check out this round-up of the top industries hiring data analysts right now, and this guide to some of the most common data analytics job titles.
Are data analysts in demand?
The big data market is growing rapidly and exponentially—it’s estimated that, by 2025, it will be worth a whopping $229.4 billion USD. The more data we generate, the more we rely on data analysts to make sense of that data.
The Jobs of Tomorrow report published by the World Economic Forum in 2020 identifies data and artificial intelligence (AI) as one of seven high-growth emerging professions, showing the highest growth rate at 41% per year. And, if you research the most in-demand tech skills for 2021 and beyond, you’ll find that data analytics crops up time and time again. Still not convinced? Take a look at this recent study where employers predicted that data science and analytics will be one of the most challenging areas to recruit for in the future—second only to cybersecurity.
What is the average data analyst salary?
4. What tools do you need for this course?
Google Sheets and, a little bit later on, Google Slides.
5. Practical exercise: Defining the challenge and opening your dataset
Citi Bike is the largest bike-share program in the United States, with 20,000 bikes and over 1,300 pick-up stations across Manhattan, Brooklyn, Queens, the Bronx, and Jersey City. As stated on their website, the service was designed for quick trips with convenience in mind, offering a fun and affordable way to get around town. Users can sign up for annual membership, or buy a short-term pass through the Citi Bike app. Once they’ve joined, they simply locate a nearby bike, ride around as they please, and return it to a nearby station once they’re done 🚴
Citi Bike is constantly looking for ways to improve their business model and provide an even better experience for their customers. Through the Citi Bike app, they are able to gather loads of useful data which, when analyzed, reveals great insights into things like user demographics and behavior—for example, when and where people pick up and drop off their bikes and how long the average journey lasts.
For example, at what rate is the customer base growing and how many more bikes should they install across the city to accommodate this growth? Where should they install the most bikes? Who should they tailor their marketing and advertising to? Essentially, data helps them to determine where and how their money and efforts can be invested for maximum impact.
Your mission is to analyze data collected by Citi Bike (for the purposes of this short course, we will call them NY Citi Bike to make things consistent) and help key stakeholders to make smart, data-driven decisions based on the insights you uncover. Here’s what you’ll seek to investigate:
What are the most popular pick-up locations across the city for NY Citi Bike rental?
How does the average trip duration vary across different age groups?
Which age group rents the most bikes?
How does bike rental vary across the two user groups (one-time users vs long-term subscribers) on different days of the week?
Does user age impact the average bike trip duration?
Make sure you’re logged into your Google account, and then access our view-only dataset by clicking here.
Note: Citi Bike is a real company, and you’ll be working with real data.
6. Key takeaways and further reading
In the next tutorial, we’ll introduce a crucial step in the data analysis process: Data cleaning. We’ll show you how to clean your dataset and prep it for analysis.
Keen to learn more? Continue your exploration of data analytics with these articles:
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