You would want to pursue a career as a data analyst or data scientist if you have an analytical mentality and enjoy decoding data to tell a tale. Data analysts and data scientists, after all, are two of the hottest tech occupations (and pay pretty good, too). Jobs in data science and analytics (DSA) are in strong demand. According to data science experts, by 2020, the number of job openings for data science and analytics is expected to rise by almost 364,000 openings to almost 2,720,000. But what is the distinction between data analytics vs. data science, and how do the two positions vary? Let’s find out!
Table of Contents
- What Are Data Scientists and Data Analysts
- Data Scientist vs. Data Analyst: What They Do
- Skills Required for Data Analyst vs. Data Scientist
- Who is More Valuable?
Without further ado, let’s get started!
What Are Data Scientists and Data Analysts
The positions of data scientists and data analysts have been misunderstood by even people who have a clear understanding of data analysis. So, what is the distinction between a data analyst and a data scientist? Both deal with data, but what they do with this data is the main distinction.
Data analysts sift through data and aim to identify patterns. What tales are revealed by the numbers? Based on these observations, what business choices should be made? In order to help display what the data shows, they can also produce visual representations, such as charts and graphs.
Data scientists are specialists in the analysis of data, but they also appear to have skills in coding and mathematical modeling. Many data scientists have an advanced degree, and many have directly gone from being data scientists to data analysts. They can do a data analyst’s job but are still hands-on in computer learning, proficient in advanced programming, and can develop new data modeling methods. They can work with algorithms, predictive models, etc.
Data Scientist vs. Data Analyst: What They Do
To clarify what insights the data hides, data analysts sift through data to provide reports and visualizations. If anyone helps people understand particular questions through maps from around the organization, they fill the role of a data analyst. You may think of them as junior data scientists in certain respects or the first move on the road to a career in data science.
At its heart, the role of a data scientist is to gather and interpret knowledge, gain actionable insights, and share those insights with their business. A data scientist is someone who spends a lot of time gathering, cleaning, and munging information since information is never safe.
Once the data is safe, exploratory data processing, which incorporates visualization and data meaning, is a vital component. They need to define trends, templates, and algorithms, others to consider the use of the product and the general health of the product, and some to act as samples that are finally baked back into the product. They will design tests, and they are a vital part of decision-making that is guided by results. They’ll work with members of the company, developers, and executives.
So, not only does a data scientist need to know how to capture and clean data, but they also need to know how to create algorithms, identify correlations, plan tests, and share the data findings in a readily digestible way with team members.
Skills Required for Data Analyst vs. Data Scientist
In analytics, there is some variation between the expertise of data scientists and data analysts, although the key differences are that data scientists use programming languages such as Python and R, while data analysts can use SQL or excel in querying, cleaning, or making sense of their data. Another distinction is the methods or instruments they use to model their data, data analysts usually use Excel, and machine learning is used by data scientists. It is important to remember that some specialized analysts can use languages for programming or are familiar with big data.
Here are some of the typical work skills of data analysts and data scientists to obtain a better understanding of the distinctions between data analysts and data scientists:
Data Analyst Skills
- Data Mining
- Data Warehousing
- Math, Statistics
- Tableau and Data Visualization
- Business Intelligence
- Advanced Excel skills
Data Scientist Skills
- Data Mining
- Data Warehousing
- Math, Statistics, Computer Science
- Tableau and Data Visualization/Storytelling
- Python, R, JAVA, Scala, SQL, Matlab, Pig
- Big Data
- Machine Learning
Who is More Valuable?
A data analyst with less than three years of experience will start out in an entry-level job where monitoring and designing dashboards are their key responsibilities. After five years, the next step could be to assume a position that requires a policy or specialized analytical approaches, such as a senior financial analyst.
Taking it a step further, after working for over nine years, an experienced data analyst might be interested in a management role and become an analytics manager. In certain circumstances, to become a computer scientist, a data analyst can continue their studies and sharpen their ability.
The importance of a data scientist grows as they acquire more knowledge. In data science, there is still an expertise shortage where most data scientists have fewer than five years of experience, but businesses are searching for experienced experts with ten years or more of experience.
Data analysts and data scientists have work names that, considering the many distinctions in position roles, educational qualifications, and career trajectory, are deceptively identical.
Regardless of how you look at it, talented candidates are highly sought in today’s work market for data-focused jobs due to the strong desire for organizations to make sense of their data and build on it. When considerations such as your history, personal preferences, and ideal salary have been weighed, you will determine which profession is right for you and embark on your road to success. Enroll for a data analytics certification, data science certification today!