Python for Data Science

Data science is becoming increasingly important in today’s interconnected, data-driven world. Data is being generated at an increasing pace by consumers, sensors, or scientific experiments that emit data points every day. In finance, business, administration and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill. Python increasingly becoming the programming language of choice when performing data analytics due to its flexibility and shallow learning curve.

NumPy is an open-source extension to Python that adds support for multidimensional arrays of large sizes. This support allows the desired acquisition, storage, and complex manipulation of data mentioned previously. NumPy alone is a great tool to solve many numerical computations. This library contains algorithms and mathematical tools to manipulate NumPy objects, with very definite scientific and engineering objectives. The combination of Python, NumPy has been the environment of choice of many applied mathematicians for years.

Pandas is another open-source Python library that eases the task of performing data acquisition, data cleaning/pre-processing and data transformation from various data sources such as CSV, Excel, etc. Simple data exploration and calculation of common statistics can be performed easily in Pandas.

At the end of the course, participants should be familiar with using Python together with popular Python data science libraries such as NumPy, Pandas, matplotlib and seaborn for performing various data analysis and visualisation tasks. The course will be presented in a hands-on, workshop manner, where students will get a chance to write data analysis Python scripts.