Data analytics involves evaluating and comprehending raw data to discover meaningful insights, patterns, and trends.

The process of data analysis involves the following steps:

Data Collection:

• Gathering data from numerous sources, such as databases, files, sensors, or online platforms.

Data Cleaning and Preprocessing:

• It involves removing inconsistencies, errors, or outliers from the collected data and converting it into a suitable format for analysis.

Data Exploration and Visualisation:

• Exploring the data through statistical summaries, visualizations, and descriptive analysis to gain meaningful insights and identifying key relationships and patterns

Data Modelling and Analysis:

• It involves applying various statistics-focused machine learning techniques to the data to develop predictive or descriptive models and perform an in-depth analysis.

Data Interpretation and Insights:

• Understanding the result of the analysis to derive meaningful insights and recommended actions.

Data Visualization and Reporting:

• It involves presenting discoveries and insights using visualizations, reports, or dashboards to communicate to stakeholders effectively.

Various domains and industries, such as business, finance, healthcare, and marketing, use data analysis and its variety of tools, techniques, and technologies, such as statistical software, programming languages (like Python, R, etc.), data visualization tools, and machine learning algorithms, to extract useful information from large and complex datasets, for decision-making and problem-solving purposes.

The ultimate goal of data analysis is to facilitate evidence-based decision-making, which will lead to a positive outcome based on data-driven insights.