Data Analytics with Python – Great Combination
1. Python is a powerful programming language and nowadays it’s being used extensively in various technological segments. It has also gained huge popularity, especially in the field of data analytics. Python offers benefits, such as ease of learning, reading, understanding, collaboration, sharing and flexibility.
2. Python allows data analysts to work with different data types and formats, such as Comma Separated Values(CSV), Excel, JavaScript Object Notation(JSON), and Structured Query Language(SQL), Hyper Text Markup Language (HTML), Text (txt).
3. Python is used in various stages of the data analytics process, such as data cleaning, pre-processing, data visualization and reporting.
4. Python offers a wide range of libraries and tools that makes it easy to work with large sets of data
• NumPy: A library for working with single and multidimensional arrays and matrices, provides extensive numerical computations and mathematical functions.
• Pandas: – for data manipulation and analysis, providing powerful tools for data cleaning, pre-processing, and analysis.
• Matplotlib: A library for data visualization and for creating high-quality graphs and charts.
• Seaborn: A library for data visualization and for providing advanced statistical graphics for exploratory data analysis.
5. To get started with data analytics in Python, one needs to install Python and some of the important libraries and tools as mentioned above. There are a number of resources available online for learning Python for data analytics, online courses, tutorials, and books.