Challenges of Being a Data Scientist

1. Challenges of Being a Data Scientist Data scientist is one of the highest paying jobs. Though data scientist is not a new subject, the demand for data scientists is the result of the accumulation of huge data which can be useful to solve real company problems and make decisions. Companies are in dire need of data scientists who can analyse and make sense out of all the data. With thisdata science certification, you can start your career and earn high. However, the job comes with a lot of challenges and struggles you should know before you venture into one so that you can be prepared better. 1. Lack of knowledge in the domain As a fresher, you will face a lot of challenges - you will have the mathematical and statistical skills and techniques to use with the data but you may struggle to apply the right domain knowledge to get accurate results. An experienced person knows what works and what doesn’t. So if you’re just starting out as a data scientist, improve your skills and expand your domain knowledge by taking up datasets and applying your knowledge to solve the problem. This way, you will get used to different kinds of data across many domains and get an idea of variables that are used in them. 2. Data science is a complex subject for a common man. To explain what data can do for a company itself is an arduous task. A data scientist has to be skilled in communication to educate the benefits of data in a simple and efficient language that can be understood by everyone. Even though there is an importance in data analytics and data science technologies, the end users need to know accumulating and analysing the right data can benefit them.Data scientists have been accustomed to asking questions so that data can do much more beyond counting and visualising numbers. Explaining to people about what data can do for them

2. 3. Often, the job of a data scientist is not fully understood by everyone other than the data scientists. A person skilled in data science may be skilled in only one domain like analytics or data engineering but companies do not know that. They misunderstand that a data scientist does it all. So they expect too much from them, giving them the task of getting the data, building a model, building algorithms, machine learning, analysing and cleaning the data. It is too much of a task for any data scientist. The task should be divided between the analytics team, data engineering team, visualization team, model building team and such. Misunderstanding about roles and responsibilities 4. Every business should have the right metric which goes well with its objectives. The metric or parameters used to evaluate the performance of a predictive model. As a lot is expected from a data scientist, there needs to be an understanding of using the right metric and KPIs to analyse the data accurately and provide accurate results. Understanding the right metric and KPI

3. They can build a right model with accurate results but if the right metric isn’t used, it won’t help with the business’s objective at all. Every company has different parameters or metrics to identify their performance and they need to define one clearly before starting any Data Science work. The metrics and the KPIs should be identified and provided to the Data Scientist to enable them to work accordingly. 5. The most common problem any data scientist has is the availability of the right data. Since there is a lot of data produced, a data scientist has to work with them and make sense out to it to solve problems. But many times, they face the problem of finding the right data to work with. Having irrelevant data can lead to building the wrong model, and ultimately, wrong conclusions which will be useless in solving the business’s problems. Quality data is better than quantity data. A Data Scientist’s role involves understanding the question asked and answering the question by analyzing the data using the right tools and techniques. To find the right data, the requirements of a company need to be clear. Getting the right data to work with 6. Many times, a data scientist might be creating the right model but they may not necessarily be solving problems. It takes a professional with good problem-solving skills to create accurate models that solve the company’s problems in real time. Creating models without solving it 7. This an arduous challenge as there is no such algorithm which works best on a dataset. If there is a linear relationship between the feature and the target variables, one generally chooses the linear models such as Linear Regression, Logistic Regression while for non-linear relationship the tree based models like Decision Tree, Random Forest, Gradient Boosting, etc, works better. You can try different models on a dataset and evaluate based on the metric given. One which minimizes the mean squared error or has a greater ROC curve is eventually considered to be the go-to mode. The combination of different algorithms together may provide better results. Choosing the right algorithms 8. Data security

4. Data security is becoming increasingly important due to cybercrime. The many kinds of data sources which are interconnected has made it susceptible to attacks from the hackers. Thus Data Scientists are struggling to get consent to use the data because of the lack of certainty and the vulnerability that surrounds it. Following the global data protection is one way to make sure that the data is safe. Implement cloud platforms or additional security checks to safeguard the data. Additionally, Machine Learning could be also used to protect against cybercrimes or frauds. This best data science course offers machine learning and deep learning. As a data scientist, do you face any other challenges? Let us know in the comments. Contact Us Learn Digital Academy Contact No: +916366370046 Email: info@learndigital.co Website: https://learndigital.co/