7 Tips for Data Science Project Management

Tips to help you plan and execute your data science projects efficiently and successfully.

7 Tips for Data Science Project Management
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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 render it suitable for further analysis.


4. Decide Which Model is Suitable


You need to decide the model that is suitable for the project. Are you just interested in descriptive data science such as data visualization or in using your data for predictive analysis? For predictive analysis, you may use linear regression (for continuous target variable) or classification (for discrete target variable). If the data does not have a target variable, you may use clustering algorithms for pattern recognition modeling.


5. Build, Evaluate, and Test the Model


For machine learning models such as linear regression, classification, or clustering, you have to build, test, and evaluate your model. This will involve partitioning your data into training and testing sets. Then you need to determine the types of evaluation metrics suitable such as mean square error, R2 score, mean absolute error, overall accuracy, sensitivity, specificity, confusion matrix, cross validation score, etc.


6. Decide If You Need a Team


Are you working on the project on your own or with collaborators? Large scale projects may require a team. If working with a team, make sure you assign roles to team members based on their experience and expertise. Make sure there is effective communication between members in the team, as this will help improve productivity.


7. Write a Project Report to Summarize Your Findings


Once the project is complete, write a project report to summarize the outputs from your analysis. It is important to summarize your results in a way that is not too technical. 




In summary, we have discussed important tips to keep in mind when managing a data science project. Careful preparation, planning, and execution will help you to complete your data science projects in an efficient and timely manner.
Benjamin O. Tayo is a Physicist, Data Science Educator, and Writer, as well as the Owner of DataScienceHub. Previously, Benjamin was teaching Engineering and Physics at U. of Central Oklahoma, Grand Canyon U., and Pittsburgh State U.