How to Set Up Your Own Data Analytics Environment

Table of Contents

• What is a Data Analytics Environment?
• Using your Data Analytics Environment On The Job
• Learning Data Analytics Environments at Lighthouse Labs

Thinking about diving into a data career? The first step is setting up your data analytics environment. In this beginner’s guide, Simon Dawkins, Lead Data Instructor at Lighthouse Labs, walks you through a data analytics environment including the three main components that make up your environment. Find out how you’ll use data analytics environments on the job and the benefits of virtual environments. Simon shares how his team prepares Lighthouse Labs’s students to construct tailored data analytics environments for each project and why that’s key to getting them job-ready!

Meet the Expert: Simon Dawkins

• Simon is the Program Lead for Data Science at Lighthouse Labs
• He spends his time informing the curriculum and teaching lectures, mentoring students, and collecting feedback for the curriculum team to enhance the learning experience at Lighthouse Labs. 

What is a Data Analytics Environment?

Quick answer: A data analytics environment is the hardware and software used to create productive data workflows. A data analyst needs to have the ability to use Python, SQL, and/or Tableau.

A data analytics environment is like a toolbox: it stores the tools you know how to use. An individual’s skills with those tools and the decision-making process that goes into choosing the right tool for the job is just as important to the environment as the tool box itself.

The 3 Most Important Elements of a Data Analytics Environment    

1. Hardware components of a data analytics environment include:
   • A personal computer – either a laptop, Macbook, or dual-monitor Windows desktop. As remote work becomes more common, laptops are more practical and easier to use, especially with a keyboard, mouse, and/or trackpad. 
   • A quality camera, microphone, and headphones. At its core, data analytics and data science are communication-focused roles, so hardware should support you in effectively communicating.
2. Software in a data analytics environment include:
   • Python for general programming and machine learning.
   • SQlite and postgreSQL as your database languages.
   • Tableau is your dashboarding software, but once you understand Tableau, you can easily pick up Power BI or any other dashboarding software – they’re fundamentally the same.
3. Data analytics environments may also include programming languages (Python, SQL, Postgres), and data visualization tools (Matplotlib, Plotly, Seaborn).

Why Organization is Key in a Data Analytics Environment 

There are so many things that need to be organized in an environment, such as versions of data files, scripts, and dashboards. Organization will keep you effective and sane, especially when you are working on multiple projects!

For our Data Analytics program students, they often find that the most difficult part of setting up a data analytics environment is dealing with their own organization. If you don't have a good file structure when starting new projects, you’ll easily drown in all of the data. At Lighthouse Labs, we instill organization as part of the data environment.

What are some tools that a senior-level/advanced data analyst may need to add to their environment? 

The main differences between analysts are the tools used between companies more than their seniority and professional experience. An oil and gas company analyzing accelerometer data from their pump jacks will use significantly different tools than a customer retention analysis at a retail company. 

Using your Data Analytics Environment On The Job

How might a data professional use Anaconda or Git within a data analytics environment?

Data analysts use Git and GitHub, though it does depend if they’re working on a team or independently. On a team where multiple people are working on the same project, data analysts use Git or some kind of version control — Git can also be used on solo projects! For team projects at Lighthouse Labs, we use GitHub for collaboration.

At Lighthouse Labs, most of our projects are on GitHub. Students get an empty Git repository to complete their project in, then when they submit it for evaluation, I can see changes and take note of their collaboration abilities, which is part of what we want to teach people. I personally use Git for solo projects because I like tracking my work, being able to roll back if I break something, and being able to share it on GitHub. 

Using Anaconda is more of a personal preference. We set students up with it at the start of the program purely for convenience, but Anaconda is geared more to the STEM world where it’s important that there isn’t incompatibility between versions of packages and languages, and that realistically involves more science, math, and research, and less alteration of the programming environment. We provide information about how to fix what Anaconda fixes, so students can be self-sufficient when Anaconda isn’t able to solve a certain problem. 

How do data professionals use virtual environments on the job?

Virtual environments are important to software development and teamwork because they allow collaboration between machines without compatibility issues. Instead of having to alter a local machine to fit the standards of a project, collaborators can create a virtual environment with the exact versions of the needed languages, libraries, applications, etc. 

We teach students how to use virtual environments by default because it's generally considered  good practice, even in the less product-oriented analytics world. Conda is an Anaconda virtual environment manager that we use for most everything.

Lighthouse Labs’s Data Science Program requires a significantly larger final project, so if it's something that their laptop can't handle, they'll need to do it on a cloud service, but setting up a cloud service manually from scratch can be super annoying. Virtual environments make this process quicker and more reliable. 

Is there any difference between setting up a data analytics environment at the office vs when working remotely?

These days it’s common for people to work from somewhere other than an office on something other than a big screen, so more than ever we have to be careful with data visualization — that it's legible on smaller screens, in terms of size and text as well as clarity of video recording for presentations. The one caveat to the hardware used may be dependent on an industry like finance or government, where they’re working with classified information. Many regulations don't allow client data for financial companies to be accessed off-premises.

Are employers expecting new data analysts to know how to set up their data analytics environments?

Most companies will expect a reasonable familiarity with common tools like Python, SQL, Power BI or Tableau, but they expect to train new hires in the specifics they’re looking for. Same goes for data science hires, since there are so many tools to do all the different parts of these processes, expecting people to be familiar with all of them is impractical, but expecting people to be familiar with the concepts and general process is totally practical. People who are good at general environment set up, troubleshooting, and who know their way around their operating system will have an advantage over those that don’t. 

New hires coming right out of their program probably won’t have to worry too much about setting up their own environment. Most companies provide a machine to use that’s set up with the levels of control they want that full-time employee to have. 

Learning Data Analytics Environments at Lighthouse Labs

In the Data Analytics Program at Lighthouse Labs, students set up their data analytics environment in stages, as each part of the environment is required. At the beginning, they start working with Python and statistics, so they set up their Python environment. When they start working with SQL, they install SQL and PG admin. We break it up so that it stays relevant to what's about to happen or what they're already doing, rather than doing it all in one go.

How are you guiding Lighthouse Labs students through this process?

We stride a balance between offering all the information they’ll need up front so they never make mistakes, and allowing them some space to learn in their own way. Instead of itemizing the ways in which they should be organized, we suggest that they should keep their files organized and let them find the way that works for them. 

How long does it take students to set up their first data analytics environments?

Students set up their environments on their own using detailed instructions and walk-throughs. Usually on the first day of working with a new topic students will spend 1-4 hours tinkering around with setting up their environment. It largely depends on what programs are required, which varies, as well as their operating system and their computer’s current status. 

What are your favorite resources for beginners who are learning about data analytics environments?

There is no shortage of resources available through Google, YouTube, Stack Overflow, and Medium — the key is learning how to sift through all of that information to find what works for you. Many of the available resources are opinion-based and contradictory, so it often comes down to personal preference. Try something for a while to see if you like it, and then try something else if you don’t!

At Lighthouse Labs, we are thorough in teaching students how to set up their environment. If and when they have the extra energy for it, we dive into what's actually going on, how it's working, and why we do each part. Intro courses like our Intro to Data Analytics course offer good exposure to the topic of data analytics but don’t go into environments and the details needed to get an analytics job.

If you feel ready to switch to a career in data, check out the Lighthouse Labs Data Analytics page to download their curriculum and get more information about the program.

Find out more and read Lighthouse Labs reviews on Course Report. This article was produced by the Course Report team in partnership with Lighthouse Labs.