Data Science is More Than Excel Spreadsheets.
Many assume that an excel spreadsheet is the center of a data scientist’s world.
This is untrue in every way possible. Data science, is a broad discipline with a fundamental focus on the desired and right result. And the data science experts strive valiantly to get that result. Different data analytics methods are used, including SQL queries, statistical analysis, predictive analysis, and other techniques.
They use Excel sheets, which is just a minor component of their overall labour. There was a period when utilizing formulas and calculations; excel sheets were significant in coming to conclusions and conducting analysis. Most data scientists now spend much of their time coding rather than working on excel sheets because programming tools like Python and R are so accessible.
More Data Does Not Always Imply More Accuracy
More data does not necessarily imply more insight or added value. The secret is to use smart data.
Assume we have a dataset with the precise amount of information required for a proper analysis. This dataset would be perfect. If we add more data, the complete dataset will need to be rebuilt, considering the additional data. Cleaning the new data and taking the time to comprehend their divergence from the old set, if any, will be necessary for reconstruction.
There is a chance that some new elements may remain unclean but identifiable even after the new data has been cleaned and then integrated with the perfect dataset already in existence. The overall quality of the analysis or outcome will suffer as a result. In this instance, less data was unquestionably preferable to more data.
Data Scientists Are Not the Same as Data Analysts.
This is a widespread misconception held by those with little understanding of data science. In actuality, data scientists and data analysts’ jobs are very different.
Data scientists work on determining the source of a trend and predicting future trends, as opposed to data analysts, who focus on identifying patterns and analyzing data. Since data science is a developing field, certain misunderstandings may inevitably surface.
It is important to remember that the two complement one another. They complement one another and strive toward the same objective.
Data is Never Clean
Analytics without actual data is nothing more than a collection of assumptions and guesses. Data enables testing and selecting the most appropriate one for the given end-use. Real-world data, however, is never perfect.
Data is not always clean, even at firms with decades-old data science labs. Besides missing or incorrect numbers, one of the main issues involves combining various datasets into a logical whole.
And it is not on purpose. Data storage businesses are frequently separately formed, built and tightly coupled with front-end software and the user creating data. Data scientists typically just “accept” the data as-is and are not involved in the design process because they arrive on the scene relatively late.
Data Science Is Not Fully Automated.
There are no pre-written scripts or buttons to press to create an analytic model since the data is not clean and needs a lot of processing. Every set of facts and issues is unique.
Data exploration, model testing, and validation against business judgment and subject matter experts are essential steps. You might not get as messy depending on the issue and your past knowledge, but you still will.
The only exception is if you obtain data in a particular format and repeatedly carry out the same action, but that already sounds tedious.
With data growth in virtually every sector, data science is becoming needed. It provides a promising professional path. Anyone who appreciates problem-solving and has data empathy may want to choose data science as a profession.
As incredible as it may sound, it has enormous business and employment possibilities. However, it is advised to avoid believing any false information concerning the subject.