Beginners Guide To Learning Python for Data Analysis.

Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of extracting information to support decision making. Python is a great language for doing data analysis because it has a great set of libraries, it is free and open source, it is also very readable and has a simple syntax. These features make it a great choice especially for beginners with no technical background. It is however very important to learn the basics of python as a beginner and understand data structures as they will come in handy when writing codes.

The Data Analysis process involves the following:

• Data Collection/Extraction

• Data Processing

• Data Cleaning

• Data Analysis

• Communication of extracted information

As mentioned earlier, Python has a great set of libraries that make analysis easier. Let's discuss a few of these libraries to gain insight on their importance for each phase of the data analysis process:

• Numpy: Numerical python( Numpy) is the numerical library used for calculations.

• Pandas: This is used for data manipulation and analysis.

• Matplotlib: This is used for data visualization.

• Seaborn-This is mostly used for the visualisation of statistical models. It is a library for making statistical graphics in Python

• Scikit-Learn: This is an open source library for the Python. It features various classification, regression and clustering algorithms.

Now that we understand these libraries and their functions, the focus should be on practicing them. Start importing data sets and begin manipulating them using these libraries.