For a beginner in data, you’ll typically start out as a Data Analyst. But what does it take to pivot from Data Analysis to Data Science? Andrew Berry from Lighthouse Labs is here to clarify the difference between these two roles and how to climb the career ladder in data. Andrew shares which languages and skills a data analyst would need to add to their tech toolbox to become a qualified data scientist – and how to learn those skills at a bootcamp.
Technically, there’s no strict definition for what makes a data scientist or data analyst, but the industry has somewhat narrowed down the roles. As with many positions in the tech industry, responsibilities for data analysts and data scientists can vary for these job titles depending on the company.
A data analyst is a jack-of-all-trades that receives data, manipulates it, and communicates those results in an easy and digestible way. A data analyst could work with internal data, survey data, sales/marketing data or data on a relational database – it just depends on what and where the analyst role is. For example, if the data analyst were in a siloed marketing department, their job would be doing analytical work with marketing data.
In large organizations, data scientists are responsible for data modelling and the deployment of those models. Data scientists spend a lot of time communicating with important stakeholders to understand the business scope before diving into the data. Then, data scientists will gather and analyze data to figure out business problems before implementing machine learning solutions. At smaller companies or startups, data scientists may perform similar responsibilities as data analysts. In these cases, data scientists will use the available data to figure out what business value they can add.
Data scientists use machine learning to solve business problems in all sorts of industries – some of these are incredibly advanced and technical. For example:
Overall, data scientists are trying to figure out the best solution for a given problem. Data scientists understand that there are a variety of tools used for the job, and they choose the most appropriate one. They will frequently communicate with stakeholders as well as use their technical skills.
Data Analyst Tools vs Data Scientist Tools
Since the responsibilities of a data analyst can range wildly, data analysts need a variety of skills. For example, they need to know how to use Excel or Google Sheets to create things like visualizations and pivot tables. A business intelligence (BI) analyst is similar to a data analyst – BI Analysts use tools like Power BI or Tableau to create dashboards.
Data scientists use a wider range of tools to do their job than data analysts. Of course, a data scientist will have a deep knowledge of SQL and Python, but what really sets data scientists apart is their ability to understand the right machine learning model for the given business problem.
Senior Roles: Data Analysts vs Data Scientist
Senior data analysts have at least 1-2 years of data analytics experience. They understand toolkits, how to operate quickly, how to build dashboards, and know exactly what their stakeholders want and look for. Senior data analysts are people who have some experience, so they can help mentor the junior analysts.
Senior data scientists have been working in the data field for at least 1-2 years. In larger companies, data scientists generally work as a team under a data science manager. A senior data scientist generally handles more important responsibilities, like interacting with stakeholders and product managers. Senior data scientists help mentor junior data scientists in addition to managing them.
Data is an enormous industry, and skilled professionals can secure well-paying jobs. An analyst’s first role may not have spectacular pay, but the salary improves after one or two years. Data scientists generally earn more than data analysts and their salaries can be 20-50% higher. Both data analysts and data scientists typically start around $54K for junior-level roles, but the salary trajectory for a data science professional with some experience is steeper. Keep in mind that compensation can change depending on location and the company.
Data analysts who want to shift to data science have a bit of an advantage because there’s plenty of overlap between the two fields. Depending on how dedicated you are to upskilling, a data analyst could realistically transition into data science after 1-2 years of study and practice.
Expert Tip: To set yourself apart in the data science field, specialize in concepts like statistics, machine learning algorithms, and deep learning algorithms.
These key data science concepts aren’t simple to learn, but they’re easier for a data analyst to learn on the job as they go. Having a data analytics background may make it easier to transition into data science, but it certainly isn’t required. If you’re already a data analyst, enrolling in a data science bootcamp can be a great way to become a data scientist.
Whether you’re a data analyst or a total data beginner, you can learn data science if you are committed and driven. Lighthouse Labs offers a Data Science Bootcamp designed to give students, both career changers and upskillers, a solid foundation in data science.
The curriculum covers Python programming and querying with SQL as well as classical algorithms and deep learning algorithms. Towards the end of the curriculum, we go into more niche data science topics that focus on potential use cases or projects students might be working on in a data career. For example, working with language and text data is different from working with image data; the workflow is the same, but the skills and tools required are slightly different.
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