Data Analytics is a sub-field of Data Science.

• Analytics focuses on looking backward (or at the present) to answer "What happened?" and "Why did it happen?" using statistics and data visualization. It is often more focused on business intelligence (BI) and reporting.

• Data Science is a broader field that often looks forward, asking "What will happen?" or "How can we make X happen?" It uses more advanced techniques like machine learning and predictive modeling to build products and forecasts.

The typical process involves five key steps (sometimes called the Analytics Lifecycle):

1. Define: Identify the problem or question you need to answer.

2. Collect: Gather the relevant data from various sources.

3. Clean & Process: Handle missing values, correct errors, and transform the data into a usable format.

4. Analyze: Apply statistical techniques and tools (like Excel, SQL, or Python) to find patterns and insights.

5. Visualize & Report: Communicate your findings clearly using charts, graphs, and dashboards to support business decisions.

• The three most commonly used and important languages are:

  1. SQL (Structured Query Language): Essential for retrieving and managing data stored in databases.

  2. Python: Highly versatile, used for data manipulation, statistical analysis, and machine learning.

  3. R: Primarily used for statistical computing and graphics.

• Other key tools include Microsoft Excel and data visualization tools like Tableau or Power BI.

• Big Data refers to extremely large, complex data sets that traditional data processing application software is inadequate to deal with. It is characterized by the Three Vs: Volume (sheer quantity of data), Velocity (speed at which data is created and collected), and Variety (different forms of data, e.g., structured, unstructured).

• Data Analytics provides the techniques and tools necessary to extract value and insights from these massive Big Data sets.