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What do Data Analysts and Data Scientist do on a daily basis?

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Jenny’s Answer

Data analyst will focus more on understanding the data, making meaningful observations and correlations of the data. Basically getting an in depth understanding of what the data means and how it can be used. Typically data analyst will share their results and decisions/ideas that they have found after analyzing the data. Usually a Data Analyst position is good opportunity for those who are interested in data but want to further expand their data career but need a good starting point to being. Starting off with a Data Analyst is a good position if you are trying to figure out where you want to go in the Data world.

Data Scientists use more advanced analysis techniques to make future predictions based on current data. Data Scientists also have to understand the data they are working on and be able to make meaningful observations, but they are more focused on using the data they have to make predictions. A lot of data scientists tend to build predictive models using Machine Learning Capabilities. Data scientists tend to have a strong mathematical or statistical background in order to make data predictions
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Michael’s Answer

In my Company Data Analysts work on reports an tools that can assist our decision making and process efficiency. They work with programs like SQL, Tableau, Excel etc. I work in Real Estate and we manage a national portfolio of properties. If I want to know specific metrics about how that portfolio is performing or how to model performance in the future I work with my Data Analytics team.
Thank you comment icon Thank You :) Mona
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Nikhil’s Answer

Its a great question - both roles are crucial as data analysts are professionals in handling volumes of data and then make them available for review.

Data Scientists focus on drawing intelligent patters from historical data. This can help in building models which are predictive in nature and also allows intervention when its needed the most.
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Kamile’s Answer

Data analysts work with large sets of data every day with the goal of learning about the story the numbers tell and using that story to solve problems. They use many different tools to help analyze, wrangle, summarize and make manageable these data sets, including Tableau (visualizations), Alteryx and PowerBI (like Excel x 100) and various coding software. Data science can be applied to any market, industry or company so it is very versatile, because everyone needs to be able to understand data, patterns and trends to be able to come up with solutions to problems in business and society.
Thank you comment icon Thank You for the info on the tools used :) Mona
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Jacky’s Answer

It really depends on the company and the teams. Overall, data scientist will need more knowledge of machine learning and build the data pipelines.
Thank you comment icon Hi Jacky! I’m sure this is true - but could you give a couple examples you’re familiar with? That might get the student started as far as conceptualizing the day-to-day experience. Alexandra Carpenter, Admin
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Sean’s Answer

In addition to the answers above, I think it's important to note that data scientists and data analysts both spend a lot of their time communicating with others. As a data analysts/scientist, you need to be empathetic to the end user of your work and be in constant communication with the business partners or stakeholders for the projects you are working on. You need to be able to understand their requirements, read between the lines when they don't have the vocabulary to ask for something specific, and communicate with them the output/process/value of your work. You also need to be able to take feedback to improve your analyses, sometimes scratching completely what you've already done and starting over.

Just keep in mind, the best way to communicate something is to show it, whether you do a quick analysis with visuals to show what a sample output looks like, and craft a story to tell of the value and importance of your work.
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Krishna Chaitanya’s Answer

Data analyst role is more about processing the datasets to build a retrospective analysis.
Data science is a mix of data analysis, business knowledge and statistics to create predictive and prescriptive analyses.
You would use tools and technologies like SAS, Python and R in data analyst and data scientist roles.

Data analytics is more about building a story from data and presenting it to the business users. You would use tools like Tableau, Power BI, Qlikview to create visualizations.
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