{"id":22615,"date":"2021-02-10T07:41:00","date_gmt":"2021-02-10T07:41:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/master-data-analytics-first-before-becoming-a-data-scientist\/"},"modified":"2023-09-05T07:25:28","modified_gmt":"2023-09-05T07:25:28","slug":"master-data-analytics-first-before-becoming-a-data-scientist","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/master-data-analytics-first-before-becoming-a-data-scientist\/","title":{"rendered":"You Should Master Data Analytics First Before Becoming a Data Scientist"},"content":{"rendered":"\t\t
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Here are 4 reasons why\u2026<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Table of Contents<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  1. Introduction<\/li>
  2. Exploratory Data Analysis<\/li>
  3. Stakeholder Collaboration<\/li>
  4. Feature Creation<\/li>
  5. Mastered Visualizations<\/li>
  6. Summary<\/li>
  7. References<\/li><\/ol>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    While it may seem obvious at first to state that knowing Data Analytics before learning Data Science is key, it might surprise you then how many people jump right into Data Science without the right foundation of analyzing and presenting data. There are certain benefits to having either an internship, entry-level position, or any position really in Data Analytics beforehand. It is also important to note that this form of experience can be acquired by completing online courses and specializations in Data Analytics. That being said, if you already have a formal education in Data Science, you might already be learning the foundation of Data Analytics in one course only \u2014 most likely, which is why it is essential to add a few Data Analytics-focused learnings into your portfolio. However, the best way is to have some sort of Data Analytics practiced with other people as you will see below when I discuss the top four benefits of mastering Data Analytics before learning Data Science.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Exploratory Data Analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Photo by Lukas Blazek<\/a> on Unsplash<\/a> [2].<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    As you specialize in Data Analytics, it is no surprise that you would become efficient at exploring data. As a Data Scientist, this is usually the first step of the Data Science process, so if you skip practicing this step, your model could result in error, confusion, and misleading results. You must keep in mind that garbage in creates garbage out. Just because you throw a dataset at a Machine Learning algorithm does not mean it will answer the business question at hand.<\/p>\n

    You will have to find anomalies in the data, aggregations, missing values, transformations, preprocessing, and much more. Understanding the data first is of course important so being a master at Data Analysis is crucial. There are a few Python (and R as wel<\/em>l) libraries that help do this automatically. However, I often find, with large datasets that they take way too long and can cause your kernel to crash and you have to restart. That is why it is important to have a manual eye at the data too. That being said, there is a large dataset mode for the library that I will present below that can skip some of the expensive and longer-lasting computations. The parameter for this situation is within the profile report of the Pandas Profiling library: minimal=True<\/code>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Here is one particular library that is plenty easy to use:<\/p><\/blockquote>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    from pandas_profiling import ProfileReport\n\nprofile = ProfileReport(df, title=\"Pandas Profiling Report\")\n\nprofile.to_widgets()\n\n# or you an do the followingdf.\n\nprofile_report()<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Pandas profiling<\/a> [3], can be viewed in your Jupyter Notebook. Some of the unique features of this library include, but are not limited to type inference, unique values, missing values, descriptive statistics, frequent values, histograms, text analysis, and file as well as image analysis.<\/p>\n
    Other than this library, overall, there are countless ways to practice exploratory data analysis, so if you have not already, find a course and master analyzing data.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Stakeholder Collaboration<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Photo by DocuSign<\/a> on Unsplash<\/a> [4].<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Data Scientists can often learn complex Machine Learning algorithms pretty quickly in their education, skipping the important part of communicating with stakeholders to achieve a goal and articulate the Data Science process. If you have not noticed already, you will have to become a master at translating a business use case into a Data Science model. A Product Manager or other stakeholder will not come up to you and ask you to create a supervised Machine Learning algorithm with 80% accuracy. What they will do is tell you about some data, and what problem they keep seeing, you will have little guidance on Data Science, which of course is expected, because that is your job. You will have to come up with the idea of regression, classification, clustering, boosting, bagging, etc. You will have to work with them as well in order to set up success criteria \u2014 for example, what does 100 RMSE mean \u2014 and how can you address and translate it to meaningful business problems to stakeholders.<\/p>\n
    So, how can you learn collaboration? Working as a Data Analyst beforehand often requires plenty of collaboration more often than that of a Data Scientist. You will create metrics, make visualizations, and develop analytical insights from working with others almost daily or at least weekly as a Data Analyst. This practice is vital in becoming a better Data Scientist as we have learned from above.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Benefits of stakeholder collaboration practice through Data Analytics roles:<\/p><\/blockquote>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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