It is one of the most in-demand and much sought after career. Despite its soaring popularity, the role of a data scientist is fast changing. But how much ever the complicated the job turns into, some of the primary responsibilities that they are required to do continue to remain the same and these are the areas that are eating into the time of a data scientist.
Find out what tasks take up maximum time for data scientists.
60 percent: Cleaning and organising Data
According to a study, which surveyed 16,000 data professionals across the world, the challenge of dirty data is the biggest roadblock for a data scientist. Often data scientists spend a considerable time formatting, cleaning, and sometimes sampling the data, which will consume a majority of their time.
Hence, a data scientist, the need for you to ensure that you have access to clean and structured data can save you a lot of time and will help you get done with the work quickly.
19 percent: Collecting data
One of the major challenges that Data Science professionals face is finding the relevant data sets to work with. Many a time organisation’s data lakes are nothing but a dumping ground with relevant and irrelevant data sets. As a Quora users point out, the trouble just doesn’t stop there. data scientists then have to contact different departments get access to the data that they need and more often ends up waiting for weeks together.
However, once they receive the data, considerable time is wasted by exploring and understanding the data, “For example, they might not know what a set of fields in a table is referring to at first glance, or data may be in a format that can’t be easily understood or analyzed. There is usually little to no metadata to help, and they may need to seek advice from the data’s owners to make sense of it,” the user points out.
9 percent: Modelling/machine learning
Once the first two use cases have been sorted, a data scientist is then left with the task of suggesting machine learning and predictive modelling as per business requirements.
It is said that one of the hardest parts of being a data scientist is not exactly developing a problem, rather it is about defining a given problem and finding means to measure the solution. This is even more pertinent when the clients do not have a clear idea of what they want. So if your models do not deliver the outcomes in correlation with the business requirement, then you are left with the daunting task of explaining discrepancies and understanding what went wrong and where.
“Often, analysts are given vague goals by the business. “Help me improve my bottom line by 15%” or “Identify the biggest problems our customers are facing” are not precise enough problem statements for the analysts. Enough time needs to be spent on understanding the exact business problem and then converting this business problem into an analytics problem that can be solved with data,” Gaurav Vohra co-founder & CEO of Jigsaw Academy notes.
5 percent: Other
Since Data Science is a mix of business use-cases, mathematics, statistics, programming and communication skills, data scientists are not singularly tasked with data handling alone. As another Quora user sums up, a data scientist is also required to perform a number of other tasks which include:
- Undirected research and frame open-ended industry questions
- Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports
- Recommend cost-effective changes to existing procedures and strategies
4 percent: Refining algorithms
This process might take months before to make the necessary changes and this can be achieved through a number of ways, often leaving the data scientist with perplexing questions choosing the right way to do so.
3 percent: Building training sets
Data Sets are the essential component or the building blocks upon which the data scientist builds his project. At times, the data scientist will have to perform scaling, decomposition, aggregation transformations on the data before they can train their models.
