Use of Python in Data Science

Introduction to Python:

“Python” was invented by Guido van Rossum in the year 1991 and Python is considered to be one of the most popular programming languages. Python has wide applications in Web Development, Software Development, Mathematics, System Scripting and many more. Python works well on various platforms such as Mac, Linux, Raspberry Pi, Windows etc. The developers can write the programs with fewer lines in Python with the help of Python syntax. Remembering the syntax of Python is very easy as it is similar to the English language respectively.

Data Science:

Data Science is the branch of the science which enables the organizations to understand the most difficult data in easier way and further to make the appropriate decisions by extracting the most useful and relevant data from multiple data sources. The essential steps involved in Data Science are: Understanding the business problem statement, Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preparing a model, Training and Testing the Data, Data Visualization and Deployment of a model. Data Science can be widely used to predict the future by finding the relevant patterns and analysis of the data.

Python (The Best fit for Data Science):

In sense of quantitative and analytical computing, Python is very easy to use. Now days, Python is also used in the different fields like Oil and Gas, Signal Processing, Finance and many more. In order to increase the Google’s internal infrastructure and to build applications like YouTube, Netflix, etc, Python is widely used. For Data Manipulation purposes the Python libraries are used which makes very easy to learn even for the beginners. Time spent for debugging the codes are minimized in Python. Time required to code is less in Python as compared to other programming languages that enable the software developers to work more on the algorithms.

The use of Python in Data Science has 4 major steps which are as follows;

  • First Stage: At the initial stage, we should know the type of data. Our database can have “n” number of rows and “n” number of columns and hence forth we should know what has to be done further.
  • Second Stage: This stage deals with the appropriate data collection. The data may be sometimes readily available or sometimes we need to scrape the data from the web pages respectively.
  • Third Stage: This stage deals with the data visualization. Different types of plots such as Bar Plot, Scatter Plot, Histogram, Pie Chart, Heatmap, Density Plot, Box Plot etc can be used for data visualization purposes.
  • Fourth Stage: In this stage we can perform mathematics such as Probability, Calculus, and Matrix Functions etc. To perform the above mathematics a machine learning library named “Scikit – Learn” can be used.

Future of Python in Data Science:

As in future there will be advancement in Machine Learning, Deep Learning and Artificial Intelligence we will be likely to see the advancements in existing Python libraries. Today the top companies like IBM, NASA, Pixar, Netflix, J.P Morgan, Facebook, Spotify etc make use of Python in large scale. The best features such as simplicity, readability, support, community and popularity makes Python to stand apart from other programming languages. According to the reports of “Towards Data Science”, there has been a tremendous improvement in the “Tensor Flow” libraries. Professionals such as Machine Learning Developers and Machine Learning Scientists prefer Python to build different types of applications and various tools like Natural Language Processing and Sentiment (Emotion) Analysis.

Applications of Data Science:

  • Netflix: Python is widely used by Netflix in order to train the machine learning models in its personalized infrastructure. The task such as Movie Recommendation can be implemented with the help of various Python libraries such as Tensor flow, PyTorch, Keras, XGBoost, LightGBM (with Pandas, Numpy, Matplotlib, Scipy, Sklearn). The network devices in Netflix are managed with the help of Python based applications. Netflix widely provides users their personalized (favorite) movies and serial recommendations. Python is used by Netflix for auto – remediation, risk classification and security automation. The ongoing project in Netflix is the “Security Monkey”.
  • Uber: To estimate the fares, the Uber uses a combination of both input data and output data. If there are variations in fares, then the amount is calculated with the help of “Surge Pricing Algorithm”. The fares are calculated by considering the street traffic data, GPS data and the algorithm based on journey time. To find out the passengers pick up point, big data plays a pivotal role.
  • YouTube: It is a free website which is used to watch online videos. YouTube is widely used to upload the videos. It uses Artificial Intelligence, Machine Learning and Data Science in order to increase its popularity. YouTube uses Python to view a video, to control the templates, video administration etc. The features such as scalability, flexibility and dynamic nature have been added by Python to YouTube.

Top 10 real world applications of Python in Data Science includes Web Development, Game Development, Machine Learning and Artificial Intelligence, Desktop GUI, Web Scrapping Applications, Audio and Video Applications and many more.

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