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Deep learning, machine learning, artificial intelligence – all buzzwords and representative
of the future of analytics.
Basic thing about all these buzzwords is to provoke a review of your own data to identify new opportunities. Like -
While writing this blog, I realized that I have worked upon highlighted use cases. But, it didn't involved all these buzzwords.
The basic philosophy behind these things is Knowing the Unkown. Once, you know the business use case you will program the implementations. Consider it like your business problem is to sort a data.
A programmer will be able to sort the data. But, knowing various algorithms like - quick sort, bubble sort, selection sort, insertion sort, heap sort or merge sort is all behind these buzzwords.
There are already API's available that have implemented all these algorithms.
In majority of use cases, a programmer/ data scientist might not be writing an algorithm. He/ She will just integrate the API and invoke its method. So, what is the buzz about ! The buzz is all about thinking that idea and implementing it, which can benefit the business.
For example -
There can be n routes to program something. But, knowing the best route which gives best performance and implementing that is learning or intelligence.
And, all these buzzwords are backed by innovative thinking and knowing your data. If you know your data and have an idea that can benefit the business. With 2020 tools & technologies, you can implement the Science to churn the Data that will benefit the business.
of the future of analytics.
Basic thing about all these buzzwords is to provoke a review of your own data to identify new opportunities. Like -
| Retail | Marketing | Healthcare | Telecommunication | Finance |
| Demand Forecasting | Recommendation engines and targeting |
Predicting patient disease risk |
Customer churn | Risk analytics |
| Supply chain optimization | Customer 360 | Diagnostics and alerts |
System log analysis | Customer 360 |
| Pricing optimization | Click-stream analysis |
Fraud | Anomaly detection | Fraud |
| Market segmentation and targeting | Social media analysis |
Preventive maintenance |
Credit scoring | |
| Recommendations | Ad optimization | Smart meter analysis |
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While writing this blog, I realized that I have worked upon highlighted use cases. But, it didn't involved all these buzzwords.
The basic philosophy behind these things is Knowing the Unkown. Once, you know the business use case you will program the implementations. Consider it like your business problem is to sort a data.
A programmer will be able to sort the data. But, knowing various algorithms like - quick sort, bubble sort, selection sort, insertion sort, heap sort or merge sort is all behind these buzzwords.
There are already API's available that have implemented all these algorithms.
In majority of use cases, a programmer/ data scientist might not be writing an algorithm. He/ She will just integrate the API and invoke its method. So, what is the buzz about ! The buzz is all about thinking that idea and implementing it, which can benefit the business.
For example -
- You might hear from some data scientist, invoking a Python API to get the centroid of cluster and getting KMean statistics.
- A SQL Programmer might use - Hive SQL to facilitate you to gather KMean statistics.
Important is knowing what is KMean, where and how we can benefit from that.
There can be n routes to program something. But, knowing the best route which gives best performance and implementing that is learning or intelligence.
And, all these buzzwords are backed by innovative thinking and knowing your data. If you know your data and have an idea that can benefit the business. With 2020 tools & technologies, you can implement the Science to churn the Data that will benefit the business.
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