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Debunking 5 Data Science Myths

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Debunking 5 Data Science Myths

Here are five misconceptions about data science that you can confidently sidestep to make data science a practical addition to your own toolkit.

· Big Data Zone ·
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Tech evangelists are currently pounding their pulpits about all things AI, machine learning, analytics — anything that sounds like the future and probably involves lots of numbers. Many of these topics can be grouped under the intimidating term data science — but all that really refers to is testing hypotheses to make better business decisions.

Not so bad, right?

Let’s dispel any dreads you may have regarding data science even further. Here are five misconceptions about data science that you can confidently sidestep to make data science a practical addition to your own toolkit.

1. It’s Hard to Find Data Scientists

Companies don’t need to hire folks with PhDs in Math or Statistics to apply data science to their business. They can start with the resources they already have: dedicated and disciplined software development teams. These teams specialize in providing business solutions that deliver value, so pivoting a team to focus on data science is not an unreasonable request.

2. Data Science Is Only Meant for Large Organizations

Data science doesn’t require expensive hardware, software, or expertise. It’s not about the number of resources' it’s about having smart people who can apply data science competently.

3. Data Science Is a Fad

Data science didn’t burst onto the scene from nothing. It’s a cumulative result of decades, if not centuries, of statistics, forecasting, and more. What makes data science unique today is the nearly unfathomable amount of data available, impressive computing power, and widely available predefined models.

4. Complex Models Are Better Than Simple Ones

Simpler models can be more efficient, cheaper to process, and easier to conceptualize and explain than complex ones. Unnecessary complexity can yield diminishing returns and endless model tweaking.

5. You Need a Deep Understanding of Statistics and Statistical Methods

It may have been a few years since your developers last touched statistics, but they can always refresh their knowledge through online resources like e-books (here’s one on statistical learning, and there are a few from Syncfusion hereherehere, and here), YouTube, and courses. From there, they can build models that fit the unique needs of the organization.

Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Our Chief Data Scientist discusses the source of most headlines about AI failures here.

big data ,data science ,data analytics ,statistics

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