Programming languages like Python are used at every step in the data science process. For example, a data science project workflow might look something like this:
Step 1: Understanding Your Dataset
Firstly you need to know and understand what type of form does your data take. You can derive insights by performing some functions and looking for a particular type of data in every row as well as column. However, this can consume a lot of time and effort to complete this type of computational task. Hence, you can use the libraries of Python like Pandas and Numpy which can quickly perform the job by using parallel processing. Furthermore, by using the pandas library, you can clean and sort your data into a table that’s ready for further analysis.
Step 2: Data Extraction
Step 3: Data Visualisation
Now that you have extracted the right data, you’ll need to visualise or have a graphical representation of the data. It can be difficult to derive insights when you see so many numbers on the screen. The best way to do this by visualising these data in the forms of graphs, pie charts, and other formats. To perform this function the libraries of Python Seaborn and Matplotlib are ideal options.
Step 4: Building Predictive Models
Step 5: Presenting Your Findings
You see, Python can be efficiently used at almost every step along the way!