Data Analyst vs Data Scientist: What is the Difference?
Organizations want to leverage their stores of data to derive actionable information to drive competitive advantage, operational improvement, faster time-to-market, and more. This has led to the emergence of the IT professional roles of data analyst and data scientist. As a measure of their importance, a 2020 World Economic Forum report identified data analysts and scientists as the #1 emerging job role in demand across all industries.
In this article, we are going to look at these roles, discuss how they differ and look at the career paths and opportunities for both.
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Comparing the Data Analyst and Scientist Roles
How do you compare the roles? First, let’s look at the two basic types of data: structured and unstructured:
Structured data is defined, collected, and stored in databases, from which it can be readily retrieved, mined, and pulled into spreadsheets or data visualization tools for analysis.
Unstructured data is undefined and cannot be used for analysis without being converted into structured data. Examples are printed reports, PDF files, voice and video recordings, social media, websites, sensor logs, etc.
Words used to describe these data types are both clean and messy — and that’s a simplistic way to differentiate between the data analyst and the data scientist roles. The data analyst deals typically with clean structured data, whereas the data scientist’s role is to find ways to capture and transform messy unstructured data for use by analysts.
Data analysts work to understand their business leaders’ information requirements. They then identify the data required and determine how they are sourced, be it from line of business databases, CRM systems, etc.
Data analysts work with data that’s already structured and mostly clean. They use SQL and languages such as Python and the R statistical programming language to retrieve and organize the required data. Then they use analysis, reporting, and data visualization tools to analyze the data, spot trends, and present actionable findings to the business leaders.
Data analysts typically will have a 4-year degree in a subject such as mathematics, statistics, computer science, or finance. Beyond that, they should have experience with data modeling and be comfortable using SQL, Python, and R. They should be competent users of Excel, SAS statistical software, and common business intelligence and visualization tools.
The data scientist role is more “free-form: than that of the data analyst. For the scientist, the data is not always sitting nicely to be worked on. The data scientist may need to use advanced techniques like machine learning and predictive modeling to provide usable, reliable, and accurate data. They may also design and write programs that automate the collection, processing, and quality control of source data.
Data scientists typically have a master’s degree in data science or business analytics. They must be skilled in advanced statistics and predictive analytics, object-oriented programming, machine learning, and data modeling. They should also be competent users of big data technologies such as the Hadoop distributed data framework, TensorFlow AI/machine learning software, and the Spark analytics engine.
Career Paths and Certifications
To get on a data analyst/data scientist career path, it’s helpful to have a four-year degree in mathematics, statistics, or a similar analytical subject. Look for a position as an entry-level data analyst. At this stage, you will not need formal certifications, but you will need to have experience working with databases and/or spreadsheets as proof of your organizational and analytical capabilities. It’s a good idea to put together a portfolio of your skills, training, and achievements related to the data analyst role. If you want to grab the attention of hiring managers, then consider going for the entry-level Google Data Analytics Certificate in order to demonstrate your commitment.
Once you’re on a data analytics career path and have real-world experience, you’ll want to pursue a formal certification path. Cloud certifications are among the most popular, with Microsoft and Amazon offering certifications from intermediate to advanced levels. As the certifications become progressively more advanced, the role they certify also progresses from analyst to scientist! Let’s look at some of them.
Microsoft Power BI Data Analyst Certification
This intermediate-level certification is for data analysts with a few years of experience. It focuses on preparing data for analysis and using Microsoft’s Power BI tool to model, visualize, and analyze data. Although the cert has recently been updated, our online courses Analyzing and Visualizing Data with Microsoft Power BI and Azure Analytics Workloads Fundamentals are valuable training resources.
Microsoft Certified: Azure Enterprise Data Analyst Associate
This certification is for experienced data analysts who design, develop, and deploy enterprise-scale Power BI data analytics solutions using the Azure cloud. The cert exam tests the candidate’s ability to implement and manage the data analytics environment, query and transform data, design and implement appropriate data models, and then use them to analyze and visualize the data.
AWS Certified Data Analytics – Specialty
This specialist-level data analytics cert is for data analysts with five years of experience in general data analytics, including two or more years with AWS cloud and data technologies. The certification ensures that successful candidates are fully conversant with AWS data analytics services and how they collect, store, process, and visualize data to provide actionable business intelligence.
Microsoft Azure Data Scientist Associate
This data scientist certification is for experienced data analysts (or junior data scientists) who are using Azure’s machine learning services to plan, design, and implement ML-based solutions. In the certification exam, candidates are tested on their ability to create Azure-based data science workloads, run data experiments and train predictive models, and to deploy responsible machine learning solutions.
Data analytics is a hot career opportunity and that’s expected to continue as organizations make investments through the next 10 years. A recent search of the Indeed job site returned over 40,000 data analyst job openings nationwide, with a further search revealing 20,000 data scientist openings. For those with a logical and enquiring mindset, data analytics represents an expansive career outlook.
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