By 2020, the number of job listings in data analytics and data science is projected to grow to 2.7 million.
But what’s the difference between the two career paths, and which one is right for you? Which one pays more? Which requires prior experience? Don’t worry, we’ve got you. Data Science Vs. Data Analytics–head to head–let’s break it down.
Interested in our Data Science Bootcamp?
Explain each role in one sentence, please.
Tall order for the first question, but we’ll do our best. A data analyst looks through data to identify trends and figure out the stories the numbers are telling. Data scientists both interpret and figure out ways to model the data. Basically, data analysts live in Excel, data scientists work with machine learning.
That was more than one sentence each, but fine. How do I know if I’m right for your course?
Tough crowd! If you’re interested in our Data Science program, you probably already have a background in programming, statistics, or a STEM-related field. Data Analytics is more your speed if you’re new to data. A bachelor’s in STEM isn’t a prerequisite, either.
Don’t spare me the details–what will you actually be teaching me?
If you join our Data Analytics program, you’ll be focusing on how to use different tools like SQL and Tableau, how to scrape, collect, analyze,and present data, plus you’ll even get an introduction to Python.
You’ll build projects based on scenarios around different types of data. Projects are graded by a team of mentors to make sure you understand how to present and articulate your results. You will also learn how to prepare for interviews and be guided through a number of assessments, case study and behavioral mock interviews, all designed to prepare you for a career in data analytics.
The Data Science program does a deep dive into Python and its mathematical toolset, statistical analysis, and big data techniques including machine learning. You’ll also get a chance to dive into the field’s most popular specializations. Data science is a highly contextual discipline, so you’ll spend time expanding your knowledge of Advanced NLP, Deep Learning, Time Series, Network Analysis, Biostatistics, Economics or Social Science, or Big Data. By the time you graduate, you will have the full skillset of a professional data scientist.
Both programs are offered on full-time and flexible learning schedules. If you are looking to get done as soon as possible, and can dedicate 50-60 hours a week, you could be done in four months. If you only have 20-30 hours a week to spare outside of other commitments, the flexible model will see you finished in six months. In both cases, you’ll be paired with a Program Manager and a Mentor to help keep you on track for graduation.
Let’s get to the good stuff–how much will I be making after I graduate? Who is even looking for data pros in this day and age?
You’re in luck––data analyst and scientist jobs are trending. In fact, Harvard Business Review named data science the ‘sexiest job in the 21st century’ (we’ll leave it up to you to decide what that means). If there’s a field you’re interested in, there’s a good change they need data people. From business, to finance, to healthcare, to tech, the job market is bountiful––and importantly, the jobs are hard to fill. According to Forbes, the average DA job takes 45 days to fill, making it an employee’s market.
Enough small talk. How much do data scientists and data analysts get paid?
Ok ok, let’s talk numbers. According to Glassdoor, the starting salary for a Data Scientist is $97,000 while a Data Analyst can expect a base rate of $67,000 a year. This has a lot to do with the pre-existing education and skills you need to bring to each profession before you begin.
And who’s hiring?
As we mentioned above, every field is looking to incorporate more and more data into their work. There are current open jobs in data science at the MLB, Amazon and Spotify. For analysts, you can launch a career at DoorDash, Charity:water, or Taboola.
I think I got it. Can you wrap it up with a punchy line I can quote at dinner parties?
Umm how about — “In God we trust. All others must bring data.” You can thank statistician W. Edwards Deming for that one.
Ok, I’m in. What’s next?
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