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Building A Perfect Data Science Model-Explained
In the present 21st century, the ongoing process of digitization has created enormous levels of Big Data. Bringing out a value from this enormous level of data is what poses to be the biggest challenge. This was the case until the advent of Data Science. Within a short time span, Data Science has emerged out becoming the ideal platform for extracting insights from the data making it become the major game changing technology across the IT, Industrial, Banking, Business & many other sectors.
Everyone wants to incorporate this newer technology, but not everyone understands how to go about it. So, here below is clear cut guide to build a successful Data Science model.
To begin with the data modeling process the foremost important thing is to have a clear idea about the problem at hand, while the collection of data follows next. Make sure that the collected chunks of unstructured data should be relevant to the business problem you are about to solve.
You can use web scrapping technique for extracting relevant data from the websites
Moving On To Data Cleaning-
Cleaning the data in hand is the next step to be done after extracting the data from the source.. The sooner you get rid of the redundancies, the better. Here are some common sources of data errors:
- You might have attained several duplicated entries from across many databases
- The given input data may have some errors in regard with accuracy
- The data entries were changed/updated/deleted
- Also, there could be an issue with missing values in variables across databases
Diving Deep into The Data-
Now that your data is ready the next step involved here is identifying the essential patterns involved. Advanced tools like Tableau or Micro strategy will be of a great help in this regard as they support interactive dashboards which lets us see how does data becomes a mirror to important insights.
Identifying The Right Algorithms-
The next step involves identifying the right algorithms which would help in producing accurate results from the data in hand.
Exploring The World Of Machine Learning-
We can call this as the most crucial step in the data modeling process as machine learning algorithm helps build a workable data model. Among the numerous algorithms that are available Data Scientist have got the right set of skill sets needed to identify the right algorithm.
Evaluate & Deploy the Model-
The final step that comes after picking the right algorithm is its evaluation. This validation involves checking whether or not using this algorithm provides results for your business.
It’s high time to begin with the procedure of mastering the art of Data Science by registering for the Kelly Technologies Data Science Training In Hyderabad.