Performing data analytics using Python involves the following general steps:
Data collection: Collecting data from various sources and storing it in a structured format, such as a CSV file, Excel spreadsheet, or a database.
Data cleaning and preprocessing: This involves cleaning the data by removing unwanted data, filling in missing values, removing duplicates, and transforming data into a consistent format.
Data exploration and visualization: This step involves exploring and visualizing the data to identify trends, patterns, and relationships. You can use libraries such as Pandas, Matplotlib, and Seaborn to analyze and visualize the data.
Data modeling and analysis: This step involves building a statistical or machine learning model to analyze the data and generate insights. Python has several popular libraries for machine learning, including Scikit-Learn and TensorFlow.
Interpretation and reporting: Finally, the insights generated by the model need to be interpreted and reported to stakeholders in a way that is easy to understand and actionable.
To perform data analytics in Python, you will need to have a good understanding of Python programming, data analysis libraries such as Pandas and Numpy, and machine learning libraries such as Scikit-Learn and TensorFlow. There are several resources available online, including courses, tutorials, and books, that can help you learn these skills and apply them to real-world data analytics problems.