Have you ever been in a situation whereby you know all the commands and formulas in a subject but you find it difficult to solve a particular task on that subject? This was me when I first started learning data science a few months ago. It became a real challenge for me. I was unable to solve any project I received, even with all of the materials I knew, until I was able to apply these 5 steps to any data science project I encountered.
It only takes 5 steps to solve any data science project, no matter how small, large, or real-life it is.
When working on a data science project, this is the first step you must take. Depending on the project, you should ask yourself questions such as
- What type of data do I need?
- Where can I find this information?
- How do I keep this information safe and accessible?
- Please keep in mind that the data is in its raw form at this point.
You might work in a company that already has this data stored in a database. Step 1 is not required at this point. The next step is to prepare this data, which brings us to step 2.
This stage involves cleaning data.
How do we clean data?
- By locating and addressing missing values
- Removing duplicate values
- Ensuring that all the values in a particular column must have the same data type The values in a specific column must be homogeneous. For example, consider a column that stores the height in centimeters of different people. Every value in that column must be in centimeters, not millimeters or inches.
We can proceed to the next step now that your data is clean and organized.
This is referred to as the reasoning stage. This is the stage at which the various data manipulation techniques you learned are applied depending on the type of problem you want to solve.
An example can be plotting the mean values for a particular column(s) in a Dataframe.
Using the cleaned and manipulated data, create interactive dashboards or plots to see how the data changes over time. This stage requires less programming knowledge.
Finally, we employ machine learning tools to conduct experiments and make predictions based on the visualized data. For example, building a system that predicts how much traffic will be on a specific road in the next hour.
Thank you for getting this far. I hope this information is useful to you. Remember to follow for more related content.