It is interesting to see how modern businesses have embraced data science positively for their various Operations. Irrespective of the size and scale of the business, data science serves all functions in the organization very well.
According to a recent survey on how job seekers see Data Science as a career option, 95% think that they fear not learning data science and related applications could land them jobless in the next 10 years. And, the industry insiders resonate with the same sentiments.
In this article, we will brush up top applications in Big Data Science and Deep Learning that you should master in the next 2-3 years. This is your go-to beginner’s guide to Data Science applications.
Why Learn Data Science?
There are 15,000 petabytes of data on the internet and many more added right now as you read it. All these data needs to be analyzed to derive valuable intelligence for decision making. Somehow, we all know that every data that we have needs to be ‘understood’. But, you will ask about how to understand data?
Earlier, data analysts would manually dissect every data to arrive at a result. Today, machines and humans interact to come to the conclusion. These results are constantly refined for accuracy, efficacy, and predictions. That’s where data science does a great job! Using data science applications, you can make future predictions and forecast events nearly with 99% accuracy!
Imagine every event that we can think of can be studied and analyzed for predictive forecasting using tons of data from history – called historical data.
Is Big Data Just Hype?
We always had the data but never had the creativity or science to extract value of that data. In the last 10 years, things have changed a lot. Focus has moved from aggregating data to extracting analytics from these data. The management of the fine balance between data volume to data value is what your role would be as a Data Science engineer and analyst.
Data Science Python Courses and data visualization training are essential to achieve measurable results for a wide range of businesses, including in the Manufacturing, B2B Commerce, E-commerce, Media and Entertainment, and Healthcare. We are only counting industries that are considered the leaders in the adoption of data science applications. We can’t ignore the industries that have adopted the science but are unable to push the limits in the tech front.
In the end, it all boils down to three things in Data Science applications.
- First, who is using data science and what tools are used to generate, cleanse and manage analytics
- Secondly, what habits and data science behaviors are you using to turn from supervised learning to fully unsupervised machine learning
- Thirdly, is enough money pumped into data management, storage, security, and governance – all these, to help companies and data science talent pursue the next challenge in the big data race?
Talent and resources, combined together can help such lagging companies to come ahead in the race.