5 Common Data Science Misconceptions Debunked

By Author: Pooja
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What is Data Science?
To put it simply, data science is the process of applying models and algorithms to derive information from data available in various formats. The data may be big or little, organized (like in a table), or unstructured (like in a document with text and images incorporating spatial information). It may also be huge or tiny. The data scientist's job is to examine this data and glean knowledge from it that may be applied to decision-making.
Now, let's examine a few of them:

Building machine learning and deep learning models is the foundation of data science.
Although creating models is an important part of the job, a Data Scientist's responsibilities do not end there. Before developing these models, you must put in a lot of work. "Rubbish in, garbage out" is a proverb used frequently in this industry. In order to make real-life data relevant for creating models, much work is put into preprocessing information because it is rarely available in a clean and processed state. This process can take up to 70% of the total time.

This entire process may be broken down into a number of ...
... stages, such as collecting, cleaning, and preprocessing the data, visualizing, comprehending, and analyzing it. Only then can you create models that make sense of the data. The code for your model may end up being less than ten lines if you are developing machine learning models utilizing widely accessible libraries! It is not a complicated component of your workflow, then. For further details, explore the top-notch data science course available online.

Data scientists can only be those with a background in math or programming.
Another misconception is that only persons from particular backgrounds may pursue a career in it, which is untrue! Data science is a useful tool that businesses can use to improve practically every aspect of their operation.

For instance, although the discipline of human resources may be unrelated to programming and statistics, it has a very effective use of data science. By gathering employee data, IBM has created an internal AI system that uses machine learning to forecast when an employee might leave. For constructing this model, a person with expertise in the human resources industry will be the most suitable.

No matter your prior knowledge, you can acquire it online with our highly regarded courses.
Get started by joining the best data science course online right away to launch your data-related career.

The tasks carried out by data analysts, engineers, and data scientists are the same.
Roles for data analysts and data scientists overlap in certain areas. Data analysts do descriptive analytics, gathering up-to-date information and applying it to make wise conclusions. For instance, a data analyst might observe a decline in sales and use the gathered business data to identify the root cause. Data scientists also make these well-informed commercial decisions. They do, however, include making future predictions using statistics and machine learning!

Data scientists use the same data set to create predictive models, which can forecast future choices and direct the business toward the best course of action before an event occurs. On the other hand, data engineers create and maintain data infrastructures and systems. They are in charge of creating databases and data warehouses to contain the gathered data.
More accurate models are produced with large data.
This myth could be somewhat true and partially false. Large amounts of data do not always imply greater model accuracy. More often than not, how well you handle dataset cleaning and feature extraction determines your model's performance. No matter how much you expand your dataset, your model's performance will eventually begin to converge.

According to the adage "garbage in, garbage out," the model's accuracy will probably be subpar if the noisy, improperly processed data you gave for it is used. Therefore, you must ensure that the quality of the data you are supplying is up to par to improve the accuracy of your models. The accuracy of your model will only improve with more relevant data!
The easiest part of data science is data collection.
You would frequently visit open data sources when learning how to create machine learning models and instantly click a button to download an Excel or CSV file. Data is not as easily accessible in the actual world, though. Therefore you might need to go to great lengths to get it."

You must preprocess it to give it structure or significance because it won't be formatted or organized once acquired. Data sourcing, collection, and preprocessing can be time-consuming, demanding, and laborious. However, this is crucial because, without any data, you cannot create a model.

Data is typically gathered over time by employing automation or manual resources from various sources. For instance, information regarding a patient's visits will be recorded to create a person's health profile. Their health device's sensors and other telemetry components can be used to collect data. For only one user, this is the situation.
Every day, a hospital may deal with thousands of patients. Consider all the information!

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