How is Python Used for Data Science?

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

The next hurdle is extracting the necessary data. As data is not always perfectly readily available to us and you may need to scrape data from the web. At this stage, the libraries of Python Scrapy and BeautifulSoup can help to extract data from the internet.

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

After learning about the data through your exploration, the next step is machine learning which is a highly complex computational technique. It involves mathematics tools like probability, calculus and matrix functions of over columns and rows. All of this can become super easy and efficient using the machine learning library Scikit-Learn of Python to build a predictive model that forecasts future outcomes for your company based on the data you pulled.

Step 5: Presenting Your Findings

Finally, you’ve spent hours or even days and weeks to derive an optimum solution. It’s time for you to arrange your final analysis and your model results into an appropriate format for communicating with your coworkers!

You see, Python can be efficiently used at almost every step along the way!