Data Science: Data Science is a domain which involves working with a huge amount of data and use it for developing prediction, prescriptive and prescriptive model for analysis. It is about digging the information from data, capturing the data, (creating the model) analyzing(validating the analytical model) and utilizing the data(implementing the best model). It is a blended field of Computer Science, Business and Statistics altogether. It is an intersection of Data and computation.
Applications of Data Science
1. Search on Internet
Search engines uses various data science algorithms to display the best results for search queries within seconds.
2. Advertisement on digital platform
The digital marketing uses the data science methods (from display banners to digital hoarding). This is the significant reason that digital advertisement platform have higher click-through rates than traditional advertisement platform.
3. Systems for recommendation
The recommendation systems not only make it easy to search related products from millions of available products, but they also add more to the user experience. Many companies use this system to promote products and suggestions in accordance with the customer's demand and related information. The recommendations are based on the user’s previous demand.
Data Science Challenges and solutions:
1. Identifying the problem
One of the major task in analyzing a problem and designing a solution is to determine the problem properly and state each aspects of it. Mostly, data scientists opt for a manual approach and start working on data and tools without a clear information of the business problem or the client demand.
Solution:
There should be a well-defined workout before starting the actual data analysis work. The first step in this process is to identify the problem, then designing a solution, and finally analyze the results.
2. Access to the right data
For correct analysis, it is significant to lay the hands on the right type of data. Acquiring access to data in the most appropriate form is very difficult as well as time-consuming task. There could be an issues ranging from concealed data, insufficient volume of data or less variety of data. Data could be spread unevenly across variety of business so getting the access of that data can also a challenging task.
Solution:
Data Scientists has to be smart in data management systems and other data integration tools like Stream analytics software which is helpful for filtering and classifying of data. Many Data integration software also permit connection with external data sources and the seamless inclusion in the workflow.
3. Data Cleansing
Working with data which is full of inconsistencies and anomalies is every data scientist’s nightmare. Dirty or invalid data leads to vague results. Data scientists work with terabytes to exabytes of data and when they have to spend most of the time just cleaning the data before starting the analysis.
Solution:
Data Scientists should create Data Governance tools for overall accuracy, consistency and formatting of data. Moreover, maintaining data quality should be the main aim. Business operations across the enterprise take advantage from good quality data. There should be some people employed in departments as data quality managers.
4. Lack of domain expert
Data scientists has to be good at high-end tools and techniques, is one of the crucial challenge. Data Scientists also need to have good domain knowledge and acquire subject matter expertise. The biggest task for data scientists is to apply domain knowledge to business solutions. Data scientists are a bridge between the top management and the IT department. Domain expertise is needed to convey the needs of the top management to IT Department and vice versa.
Solution:
Data scientists have to work on gaining scenario of business, understand the real problem and work on analyzing and modeling the effective solutions. Along the mastering statistical and technical tools, Data scientists also need to concentrate on the business requirements.
5. Data security issues
Now a days, data security is a biggest issue. Since data is acquired and retrieved through a lot of channels like, social media, etc. there is increased vulnerability of attack by hackers. Due to the confidentiality of data, Data scientists are facing problems in data extraction, utilization, developing algorithms. The procedure of taking consent from users is causing a major delay in time and expensiveness.
Solution:
For this issue, there are no shortcuts. One has to follow the pre-defined global data protection rules. There should be additional security checks and make use of cloud platforms for data storage. Organizations also actively need to take help of advanced solutions that involve Machine Learning to secure against cyber-crimes and fraudulent practices.
