Data Science — Machine Learning and Data Analysis using Python
Python is the programming language of choice for data science. It is freely available and houses all data science algorithms. There is numerous python packages developed for machine learning, data analytics, data visualization, statistical modelling and data analysis work.
“Information is the oil of the 21st century, and analytics is the combustion engine.”
— Peter Sondergaard, 2011
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Here analytics refers to the data science algorithm/machine learning algorithm/statistical algorithm/data analysis. The person who possesses expertise in analytics is a data scientist. According to Indeed, the average salary for a Data Scientist is $121,583. It’s nice to know that employment opportunities are abundant and growing as well. That number is only expected to increase, as demand for data scientists is expected to keep growing. In 2020, there are three times as many job postings in data science as job searches for data science, according to Quanthub. That means the demand for data scientists is vastly exceeding the supply.
Due to the global digital/technological revolution, businesses have been generating vast amounts of data and analysing this data is imperative to remain at the edge of competition. Technology has made significant advances in the last two decades and storing big data have become simpler and affordable, it is now time to analyse large volumes of stored data to derive actionable insights. This requires knowledge of programming language to process the data. This is achieved using python programming language and different data science, statistical modelling and machine learning packages available in python. The most popular and widely known packaged include Pandas, NumPy, and SciPy. Data scientists working on various machine learning tasks find that Python’s sci-kit-learn is a useful and valuable tool. Matplotlib, another one of Python’s packages, is also a perfect solution for data science projects that require graphics and other visuals.
In short, understanding Python is one of the valuable skills needed for a data science career. Python efficiently handles the large/big datasets though it could be not as fast as SAS. The data science model developed in Python can be directly implemented in the production environment which is not the case with other data analytics tools. This feature removes the dependency on the IT team makes python more appealing to learn. Python maintains a consistent coding structure in terms of programming keywords used in different data science, machine learning and data visualization functionalities.
There is a lot to write about data science using python programming, however, we have covered the overall idea and role of python in data science in a precise way.
