7 Tips for Data Science Project Management
Tips to help you plan and execute your data science projects efficiently and successfully
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Project management is an important aspect of data science. Good project management skills will help improve your efficiency and productivity. This article will discuss some tips for managing a data science project.
1. Ask the Right Questions
Asking the right questions is one of the most important steps for a data science project. You need to determine what insights you are trying to obtain from your data. In some cases, you need to ask the right questions even before the data collection process.
2. Gather the Data
Do you have the data available for analysis? If the data is already available, then you may proceed to the next step. If data is not available, you may need to figure out how to collect the data, for example using surveys, or purchase already existing data. If you have to collect your own data, some points to keep in mind include: the quantity of data you need, time needed to collect the data, and the cost of data collection. You need to also make sure the data is representative of the population. Irrespective of where your data is coming from, make sure data collected is of good quality, because bad data produces low quality and unreliable predictive models.
3. Clean and Process Your Data
Any data collected will have imperfections such as the presence of missing data or data may be entered on questionnaires in the wrong format. Raw data will have to be cleaned and preprocessed to…