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Data Scientist vs Data Analyst – Which Is RIGHT FOR YOU?

Are you on the fence about whether you should get a job as a data scientist or a data analyst? Well hold tight because in this video, you're gonna get all the insider information about what each of these roles entails, and more importantly, some serious stuff you need to consider about yourself before choosing between them. For the very best data leadership and business building advice, be sure to subscribe to my channel and hit the bell icon to be notified when a new episode drops each week.

I'm Lillian Pierson. And I am so grateful to say that through my partnerships with LinkedIn learning and Wiley, I've helped educate well over a million data professionals on how to do data science. I've been a data entrepreneur since 2012 and the mission of my company Data-Mania is to support data professionals in becoming a world class data leaders and entrepreneurs. I'm going to be real with you.

Lots has changed over the last 10 years. And the discussion we're about to have today is far different than it would have been even five years ago, the bar has definitely been raised. Now, to determine which role is best suited for your professional and personal goals, you first need to understand the responsibilities each of these jobs entails and then we're going to take into consideration some personal aspects of which you really should be aware, hold tight, because I'm going to cover all of this in just the next few minutes. Let's look first at role requirements and skill sets. If you want to become a data analyst, then you definitely should have a stem degree. And you're going to want tech skills and programming databases, predictive analysis and model building, you should have experience with R, Python and SQL and you're going to want to have consulting skills. By that I mean that you've got reporting acumen as well as strong analytical skills, the ability to manage multiple priorities, and of course, strong communication skills. Data analysts also have experience with agile development workflows, and various related pipelines.

If on the other hand, you want to become a data scientist, here's a list of typical requirements of a data scientist. You need a stem degree, you want to know how to program in Python R and SQL at the very least, you'll want to have a whole plethora of analytical techniques at your disposal. So data mining, random forests, statistical analysis, regression trees, social network analysis, these sort of things. And you'll also want to have some architecture level experience. So you definitely don't need to be a data architect by any means. But you do at least want to have some experience with data architecture level tasks. Obviously, you'll want to know the ins and outs of machine learning, artificial neural nets, decision trees, deep learning, reinforcement learning, there's a lot to machine learning.

So I'm not going to go into all the details on that you can Google it, and of course, sophisticated as anything statistical analysis skills. In order to get any form of seniority as a data scientist, you'll want to have around at least five years of experience. And it's definitely going to be helpful if you have experience working in a distributed computing environment or using tools for distributed computing, you absolutely need to have data visualization skills as well as data storytelling skills.

And you need to have pretty good communication abilities in terms of communicating your data insights to non data people. So in other words, you need to have a pretty sophisticated combination of math, statistics, programming, domain expertise, as well as communication and presentation skills that enable you to present your findings to business leaders. Now let's look at responsibilities associated with each of these roles. Of course, this depends on what company you're at and what your exact role is, but some things are consistent across the board. In its simplest form, data analysts are responsible for both analyzing and interpreting insights in data. But for more details, you'll be working to research and analyze consumer data, work and modify customer centric algorithms in order to meet the needs and expectations of customers, analyze large data sets and present actionable findings, support business decision making with findings from both ad hoc and reoccurring analysis, determined KPIs, generate financial reports, as well as design and optimize dashboards.

You of course, need to be able to visualize data goals and metrics, as well as extract data from data warehouses, using SQL. A common path is for someone to start off as a data analyst and slowly work their way up to data scientist and senior data scientist. Data scientists on the other hand, are commonly required to know more statistics than software engineers and more programming than most statisticians. A data scientist knows how to run a data project from beginning to end. Performing tasks that require storing and cleaning big data, building predictive models and generating predictive insights. Once all that's done, they have to turn it into compelling data stories.

That's what we call data storytelling. In terms of key responsibilities, data scientists, most of the time are mining and analyzing company data for making improvements to product development as well as a business strategy and even marketing campaigns. Data Scientists leverage predictive modeling. In order to optimize and increase revenue generation, customer engagement and ads targeting. Data Scientists also develop custom algorithms and data models. They also develop tools and processes that help oversee and monitor the performance and accuracy of the models they're building. They assess data gathering techniques, and evaluate new data sources for accuracy and reliability. They of course, test the quality of their models, as well as use a be testing to develop frameworks for their company. In other words, data scientists implement models and monitor the results of those models all while interfacing with various teams across the company. Based on the explanation I've just provided, looking at your skill sets, what camp do you think you fall in now? Are you more of a data analyst type or a data scientist type? Tell me in the comments below.

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