Top 6 Challenges Faced By DATA SCIENTISTS

Whilst the role of a data scientist in our daily lives is considered to be among the prominent considering the ever increasing demand in their numbers. There are always two sides to a coin. Being a data scientist sure is rewarding, but it comes with its own pros and cons. The cons or challenges faced by a data scientist are as follows:

• Scarcity of Funds
• Scarcity of Tools for scaling
• Veracity
• Unavailability
• Access to right data
• Data Privacy / Security
• Lack of talent for the job
• Results / insights not utilized by the decision makers
• Inaccessibility of Data
• Privacy issues
• Data cleansing
• Lack of domain knowledge
• Dirty Data 
• Scarcity in clear questions
• Disbandment due to inability of organization to afford the team.
• Company issues
• Identification of issues / problem

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Some of the major Challenges from the ones listed continue to be the following:

• Identification of the problem

 It is better said than done. This is especially true in the case of data scientists, as they struggle to identify the problem whilst analyzing a real time issue. This is because not only do they have to make the data more readable for the masses, but also have to analyze the data. The problem can be identified by studying the situation well, and then coming up with solutions accordingly. Thus, finally obtaining the desired outcome / result.

• Results not utilized in decision making

These are also inclusive of challenges like company politics. There is a resulting failure in accommodation of the study findings by the decision makers. 

• Data Privacy

These types of challenges are centered in the vicinity of data itself. It is considered with the purity of the collected data, as well as the privacy and security issues revolving around it. 

• Scarcity of funds

This may occur due to the organization being small scale, with lack of funds to afford a team of data organization. This is concerned with the talent or expertise in the job, expertise in domain and external data sources are other factors for the lack of funds. This can also result in the disbandment of the team.

• Scarcity of tools to deploy / scale: 

This kind of challenges surfaces when there is a lack in the number of tools utilized for extraction of insights, and the deploying of models. 

• Lack of domain knowledge / expertise

Data scientists should possess knowledge of their respective domains. Failure of doing so, may result in a tight situation such as being unable to get across the results and needs. This is one of the largest challenges that data scientists have to face. Gaining expertise in their respective domains / subjects enables them to be more efficient in tackling issues at work front. 

What are some suitable jobs for beginners in the field of Data Science?

Job prospects for beginners (as in those who do not possess prerequisite expertise in the field) of Data Science are as follows:

• Reporting Analyst
• Business Analyst
• Reporting Analyst
• Junior Data Analyst
• Analyst associate
• Hadoop developer 
• Database developer
• ETL developer
• MIS developer
• Table developer

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No knowledge of domain in the field? No worries. Even people at a beginner level can aim to be a data scientist! Register for our data science course in hyderabad situated to transform your career into a better one. 

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