Data Scientist: Analytics Specialist
Data Analysts and Analytics Data Scientists use Python and SQL to query, analyze, and visualize data — and communicate findings.
Includes Python 3, SQL, pandas, scikit-learn, Matplotlib, Tableau, Excel, and more.
Data Science vs Data Analytics
Data science is a broad field that encompasses data analytics and includes other areas such as data engineering and machine learning. Data scientists use statistical and computational methods to extract insights from data, build predictive models, and develop new algorithms. Data analytics involves analyzing data to gain insights and inform business decisions.
What Is Data Science?
Data Science is the application of tools, processes, and techniques such as programming, statistics, machine learning and algorithms towards combining, preparing and examining large datasets. The datasets are often a mix of structured and unstructured data.
What Is Data Analytics?
Like data science, data analytics is the use of tools and processes to combine and examine datasets to identify patterns and develop actionable insights. And, like data science, the goal is to help organizations make better, data-driven decisions. The key difference is that for data analytics, the focus is typically much more on answering specific questions than open exploration.
Which data career is right for you?
Data analysts and data scientists have job titles that are deceptively similar, despite the many differences in role responsibilities, educational requirements, and career trajectory.
However, no matter how you look at it, Schedlbauer explains, qualified individuals are highly coveted for data-focused careers in today’s job market thanks to businesses’ strong need to make sense of—and capitalize on—their data.
Once you have considered factors like your background, personal interests, and desired salary, you can decide which career is the right fit for you and get started on your path to success.
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