Challenges in front of Data Scientist

Data scientist are a new outcome of analytical data expert who possess the technical skills to solve the complex problem and the curosity to find the soloution for the given problem while playing with tonnes of data.

Data scientist face lot of Challenges in day-to-day life while dealong with huge amount of data. Following are the Challenges faced by Data scientist and how they deal with those challenges.

1. Deducing Issue

Problem: Data Science is used to deal with paricular problems. The first challenge faced by data scientist while dealing with real time problems is deducing the issue. There main responsibilty of data scientsist is to understand the given data but also make sure that others have understood the data. The outcome of their analysis about data is to resolve the issues in business, create efficient supply chain , improve customer relationships, starting new revenue opportunities.

Solution: In order to resolve above issue, it is very important to understand each and every aspect of given problem. For data visualization, Data scientist uses various software viz. Tableau, infogram, chartblocks, data wrapper etc in order to make data meaningful.

2. Handling data from multiple sources

Problem: The area of data landscape is very vast. Big data allows data scientist to reach to that wide range of data landscape using varous data platforms and resources. However, dealing with vast amount of data from multiple sources is a great challenge for data scientist

Solution: The solution for above problem is to use various cloud based platforms viz amazon cloud,google cloud and manymore. It is used to connect data from innumerable locations, places, in any format, at any time , that are collected in realitime as well as in batches.

3. Predicting Outcomes

Problem: Finding and predicting the outcome isn’t always gives the final result as expected. Even the dataset is given, one cannot get the outcome as expected.

Solution: In such situations, data scientist are focused on supervised learning for model selection and appropriate algorithm that will be used.

4. Data Security

Problem: In today’s world, maintaining security of data is a big issue. Since data comes from various interconnected sources , social networking and other nodes and hence increases the threat of hacker attacks. And therefore because of confidentaility data scientist faces many obstacles in data extraction, usage and framing algorithms.

Solution: For the above issue, there are no shortcuts. One has to go with traditional apparoach for the protection of data. There is need for additional data security mechanisms, Organisations need to use Machine learning approach in order to maintain security against cybercrimes and fraudulents.

Gaurav Gujarathi

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