Data Analysts are responsible for helping businesses across many industries gain actionable insights from vast amounts of data. But what does it take to launch your own data analytics career – especially if you don’t have the college degree for it? Celia Fryar, expert Data Analyst and Instructor of General Assembly’s Data Analytics course breaks down how musicians, teachers, and realtors have the transferable skills to launch data careers. Plus, find out how General Assembly’s Data Analytics course is equipping students — from total beginners to upskillers — with the skills they need to launch mid-level data careers.
In order to create data-driven decisions, businesses need to find actionable insights from data that exists in their organization. Insights from data can help businesses increase revenue or decrease losses. This is where data analytics has huge potential to make an impact on the success of businesses around the world.
Typical Responsibilities of Today’s Data Analysts
The typical data analytics workflow involves getting data from systems. In an enterprise environment, data might be coming from your IT group, data engineer, or sponsor. In a startup, you might be working with something like Stripe, a popular method for paying for merchandise or other inbound data service providers. A data analyst may be responsible for:
More so than other jobs, data analysts must be able to communicate, answer questions, and speak in terms that stakeholders can understand. You don’t want the stakeholder to need to be an IT professional! As a data analyst, you have to take whatever your team needs and translate it into something that can be applied to data.
Which industries need data analysts?
You would be hard-pressed to find an industry that doesn’t rely on data in the modern world! Some data can be difficult to gather and process due to collection methods or limited tech, but nearly all businesses today rely on data-driven decisions to guide their mission. Overall, retail, agriculture, finance, and governments all rely heavily on data to help drive short-term and long-term decisions.
Some professions require skills that transfer well to data analytics. Sometimes, empathy is required to help parse out unnecessary information within a presentation. Other times, it’s necessary to give an uncomfortable answer or even accept that no answer can be given from unusable data. These skills are found in other industries and they tend to transfer well into the data analytics field.
Musicians must perform under pressure in front of crowds; much of what they do is for the benefit of others. Musicians are required to have an attention to detail and many experienced musicians are adept at picking up on patterns within complex issues that others might miss. These skills transition well into data analytics teams.
The work ethic of teachers is among the highest of any profession. Teachers need to be able to tackle complex issues with nuance and discretion, which can help them find solutions to data-driven problems. Teachers are often mission-driven by passion to help others succeed. These skills help support them in data analyst roles and help them focus on important information instead of getting distracted by superficial issues.
Real estate professionals, especially those who work in commercial real estate, have skills and experience that transfer well to data-driven businesses. Realtors often have to deal with competing interests and they’re required to have broad business acumen to be successful. The real estate business is filled with complex legal issues and regulations to sift through, which gives real estate agents valuable skills to employ in the field of data.
The tools data analysts use will vary depending on their role in an organization, the experience someone has to offer, and the size of the organization where they’re involved. If you’re a data analyst in a Fortune 100 company, your scope will be smaller and your responsibilities will be deeper. On the other end, if you’re working in a startup, you may have to take care of the data end-to-end.
If you’re extracting data, the tools will be SQL or Python. Python is great at cleaning and preparing data. SQL is mandatory for a data analyst as more people are having, at minimum, read-only access to data. These programming languages can accelerate the time to finding insights by bringing the data back to the business stakeholders more quickly.
Analysts still need to know Excel as a spreadsheet system. Analysts need techniques for quick navigation and analysis, rather than slowly using arrow keys to review data.
Analysts need something to transform raw data beyond charts and graphs, and there are currently many options to help people have an actionable takeaway within seconds of seeing your presentation. For data visualization, data analysts may use Power BI or Tableau, which can quickly create visual insights that are valuable for the organization. Tableau has similar functions to Power BI, and it has an impressive facility for data preparation and even analysis without prep. It’s built on a SQL engine, so it is fast and has tremendous capabilities for enriching data tables. Also included is the ability to interact with and query data with their powerful VizQL running behind the scenes.
For automating aggregation and staging processes, data analysts may rely on Alteryx, AtScale, and other tools that pre-stage big data for analysis.
Do you need a college degree to launch a career in data analytics?
The short answer is: it depends. If you’ve been in the industry for a while and you have domain experience before making a career change, you can do it without a degree. If you’re young and lack business experience, a degree can be something that will supercharge what you have to offer a business. The people that succeed without degrees are people that are pivoting or have something else to offer a business.
There are two halves that are non-negotiable in data analytics: toolset and a mindset. If you learn the mindset through outside experience or internships, the toolset isn’t hard to learn.
What are the typical salaries of today’s data analysts?
Salaries for data analysts will vary depending on their role, qualifications, and experience. Analysts with Python skills will have more job options and individuals providing more valuable services will see better compensation. More experienced workers might be dealing with higher risk thresholds, which will earn higher rates.
For data analysts without Python skills, the salary can range between $70K-89K per year. That number can increase drastically for senior roles with compensation closer to $150,000 per year.
General Assembly created the Data Analytics course for those new to analytics and those looking to upskill. The scope of data analytics has evolved from simply analyzing past events to making predictions based on data insights, and this is included in the General Assembly course.
Can complete beginners enroll in the Data Analytics course?
The more data literate you are, the quicker you will absorb information. That said, General Assembly does enroll complete beginners — I’ve even had a few students who have had very limited technology in their day to day lives! It’s a bigger leap for those students, but there isn’t a prerequisite that will prevent students from taking the course. The classroom always has students that have widely different levels of experience and part of the intent for the program is to allow diversity to enrich the class experience.. General Assembly offers asynchronous pre-work beyond the initial admittance challenge to the program to help students begin the live courses in a similar starting point with the toolsets.
What is included in the Data Analytics curriculum?
General Assembly’s immersive data analytics course covers end-to-end data life cycles. Students will learn Excel, Python, and SQL along with data visualization using market-leading tools like Power BI and Tableau.
Which data roles does this course prepare students for?
By completing the Data Analytics course, students will get the knowledge they need to skip over the most basic entry-level data roles. Our program graduates will be more involved in mid-level data positions. It’s the difference between being read-only and read-write. This course should enable a student to step into a place of responsibility and authority within an organization.
How does General Assembly prepare its Data Analytics students for the job hunt?
This is a specific focus for General Assembly and it’s been addressed in several ways. While many places provide tool training, we go beyond that to refine the skills needed to help accelerate our students’ effectiveness. We have outcomes coaches that are industry professionals and they provide weekly training and coaching sessions. These coaches help students with everything from personal branding and resume building to making sure their portfolios are in good shape.
We have daily morning openers to help students refine interview skills and strategies, as well as presentation skills. These can be something along the lines of describing a time when you had a group project and someone didn’t do their part. We begin the day by discussing strategies, often making a list of “best practices” based on the cohort members’ experiences. This allows students to learn from other people’s experiences and prepare for interviews and future challenging situations
When looking for your first data analytics job, it’s helpful to find an area of interest that aligns with your passions. If you can get involved there, your work won’t feel like work.
On LinkedIn, every data professional's profile lists SQL, Python, Power BI, and some collection of tools. It’s important to differentiate yourself with your knowledge of something else, such as HR, marketing and sales analysis, or another core business acumen. Differentiation can also come through community service or hobbies that demonstrate discipline and consideration of others.
Tableau offers information on their site about how different industries use data. They have eight different industries and they show how data is used within these industries. They also have a section that shows how different job functions use analytics. I usually have students look through it, as well as do a research project to help them understand how data is applied to create “wins” for various business environments..
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