It is not a surprise that data science projects are time-consuming with a lot of iterations. Iterations can be quite frustrating and affect the work in more than one way. Hence, it requires data scientists to be smart and proactive. With simple hacks, it is possible to avoid unhealthy iterations and focus on increasing productivity.

Focus on the bigger picture (the bigger problems)

Data scientists should focus on the problems that affect the organisation on a larger scale. Look for bigger problems and find stable solutions. Do not let the smaller problems overshadow the bigger ones.

Data cleaning

Any data scientist would agree that data cleaning is a tedious process. However, it is important to have a highly processed and neatly structured data as it yields better results than a noisy one. The cleaning process should be done with a simple regular expression rather than a complex tool.

Domain knowledge

When building a model with given data, a data scientist should be aware of where the data is from. It helps in getting better insights and provides an in-depth analysis of the data.

Always open to more learning

Data science is huge and there’s no limit on things you can learn each day. Data scientists should keep themselves updated with new tech being developed every day to solve problems efficiently.

Data Science is humongous and needs continuous learning to sustain. Be updated with simple tips and tricks to increase productivity and solve complex problems with simple solutions.