How to Make the Transition from Data Analyst to Data Scientist

What’s the Difference Between Data Analyst and Data Scientist?

Data analysts do analyze data, but data scientists have skills that allow them to process data in innovative ways. They deal with both structured and unstructured data with a heavy dose of coding and math, allowing them not just data manipulation, but a new program or methodology for processing.

Working As a Data Analyst

Data analysts examine the data as it is. Not everyone has the talent to draw meaningful insights from numbers or manipulate the data to reveal patterns and realities that might be missed.

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Working as a Data Scientist

Many of your skills as a data analyst translate well to data science. Knowledge of R and/or Python is a must. SQL and data management skills are also a big part of data science. Where the two diverge sharply is the purpose of the question and the method of answering.

Making the Switch to Data Science from Data Analytics

To be ready for your newest position as a data scientist, you don’t necessarily have to have an advanced degree, but there is a bit of work involved in making the switch. Here’s how to go about it.

  • machine learning models (regression, neural networks)
  • distributed computing frameworks like Hadoop
  • API interaction
  • data visualization tools
  • cloud computing tools
  1. Market yourself — If you don’t have a Github, it’s necessary now. Companies are using Github for version control, and it’s better if you’re already there. You may also want to start your listing on LinkedIn Or AngelList.

Making The Transition

Data analytics is an excellent foot in the door for an aspiring data scientist, and getting to work as soon as you can on a real-world problem is the way to go. You’ll not only master new concepts faster, but you’ll also be able to market your skills and connect with companies and leaders in the field.

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