{"id":3751,"date":"2020-10-19T09:00:00","date_gmt":"2020-10-19T07:00:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/data-analyst-to-data-scientist-career-transition\/"},"modified":"2023-05-17T14:17:25","modified_gmt":"2023-05-17T12:17:25","slug":"data-analyst-to-data-scientist-career-transition","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-to-data-scientist-career-transition\/","title":{"rendered":"How to Make the Transition From Data Analyst to Data Scientist"},"content":{"rendered":"

Data analysis skills serve as an excellent starting point for anyone looking to become a data scientist. Find out how to transition from data analyst to data scientist in this guide.<\/strong><\/p>\n

With the current shift toward home working, many people are retraining in fields better suited to the 21st century economy. One field seeing major growth is data, with skilled data analysts and data scientists in huge demand.<\/p>\n

Perhaps you\u2019re considering a career in data and are keen to know what opportunities await you. Maybe you\u2019re already working as a data analyst and want to know how you can progress into a data scientist role. The good news is that, although data analytics and data science denote two distinct career paths, data analysis skills serve as an excellent starting point for a career in data science. Once you\u2019ve mastered data analytics, it\u2019s a case of adding more complex and technical expertise to your repertoire\u2014something you can do gradually as your career progresses.<\/p>\n

So: How do you transition from data analyst to data scientist?<\/strong><\/p>\n

While there\u2019s no single route into data science, this post outlines the main steps you\u2019ll need to consider if you want to make the shift. Whether you\u2019re a seasoned data analyst looking for a new challenge, or are new to the field and want to plan ahead, we offer a broad introduction to the topic.<\/p>\n

We\u2019ll cover:<\/p>\n

    \n
  1. What\u2019s the difference between a data analyst and a data scientist?<\/a><\/li>\n
  2. Why become a data scientist?<\/a><\/li>\n
  3. What additional skills do you need to learn in order to go from data analyst to data scientist?<\/a><\/li>\n
  4. How to learn data science skills<\/a><\/li>\n
  5. How to transition from data analyst to data scientist: Practical steps<\/a><\/li>\n
  6. Key takeaways<\/a><\/li>\n<\/ol>\n

    Ready? Then let\u2019s take a closer look.<\/p>\n

    1. What\u2019s the difference between a data analyst and a data scientist?<\/h2>\n

    Before you embark on your journey into data science, it can help to understand: What<\/strong> exactly is data science, and how does it differ from data analytics?<\/strong> First up\u2026<\/p>\n

    What is data analytics?<\/h3>\n

    Data analytics is the process by which practitioners collect, analyze, and draw specific insights from structured data (i.e. in a standardized format). Its ultimate aim is to inform decision-making. Although data analytics is a specialized role, it is just one discipline within the wider field of data science. You\u2019ll find a more comprehensive explanation in this introductory guide to data analytics<\/a>.<\/p>\n

    What is data science?<\/h3>\n

    Data science is a much broader scientific discipline, of which data analytics is a single aspect. Data scientists generally work with large, unstructured (or unorganized) datasets.<\/p>\n

    While a data analyst tends to focus on drawing conclusions from existing data, a data scientist tends to focus on how to collect that data, and even which data to collect in the first place. They need a far deeper level of insight into data than is required of a data analyst.<\/p>\n

    If this feels a bit vague, you can think of data science as being like the construction industry. Its purpose is to create data structures (like buildings) that can be used for specific purposes. Just as it takes many different skills to plan, design, and construct a brand new building, it takes many skills to plan, design, and construct these data structures.<\/p>\n

    Broadly, we can divide data science into the following categories, each with specific skill sets and tools associated with it:<\/p>\n

    Data theory<\/h4>\n

    which involves creating entirely new, abstract algorithms. In our analogy, you can think of algorithms like bricks and mortar.<\/p>\n

    While they\u2019re useful for building things, somebody first had to invent them. Today, it is commonly accepted that machine learning is an invaluable data tool<\/a>. But the person who invented the first machine learning algorithm had to show a great deal of foresight to understand its potential uses.<\/p>\n

    Data theory is therefore highly technical. It’s a skill (even, arguably, an artform) that not all data scientists have.<\/p>\n

    Data architecture<\/h4>\n

    This involves taking algorithms and applying them to specific use-cases or fields, e.g. scientific or business domains.<\/p>\n

    To continue our construction analogy, you can equate a data architect with a traditional architect. Their job is to combine algorithms (or bricks) in novel ways to create blueprints for specific types of data structures (or buildings)\u2014just as a real architect would do.<\/p>\n

    Data modeling<\/h4>\n

    Also coming under software engineering, this involves taking an architect\u2019s blueprints and figuring out how to put them into practice.<\/p>\n

    Data modelers are like structural engineers. They take the raw material of algorithms and code and apply these to create software structures that are fit for purpose. They\u2019re generally excellent coders since part of their job is to overcome unexpected hurdles and to fix things that don\u2019t fit together as planned. These are the folk who get their hands dirty!<\/p>\n

    Data analytics<\/h4>\n

    Finally, you can imagine a data analyst as someone who uses the finished structure (or building) to do their job. For instance, if the structure was a fire station, you could think of a data analyst as a trained firefighter who knows how to use the different specialized aspects of the building. However, they won\u2019t necessarily concern themselves with the intricate details of how the building was constructed.<\/p>\n

    As you can see, \u201cdata science\u201d is really an umbrella term for a wide range of different disciplines. Another increasingly popular domain is data engineering\u2014you can read about the difference between a data scientist and a data engineer here<\/a>. So, if you\u2019re thinking about a move from data analytics, consider which aspect of data science most interests you. This will help as you formulate a career plan. One thing\u2019s for certain\u2026whichever path you choose, you\u2019ll have plenty to get your teeth into!<\/p>\n

    Want to know the difference between data analytics and data science? Check out the following video:<\/p>\n