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Data Processing

                 Data processing involves tasks to refine raw data for analysis or application, including cleaning, organising,
                 transforming, and summarising information. It ensures data accuracy, relevance, and accessibility for effective
                 decision-making and analysis. It is crucial across various sectors like business, science, and technology, facilitating
                 better utilisation of data assets. Data processing helps computers understand raw data. Use of computers to
                 perform different operations on data is included under data processing.


                 Data Interpretation

                 Data interpretation is the process of making sense of data by analysing it to uncover patterns, trends, and
                 insights. It involves examining the data to understand its meaning, implications, and significance, helping to
                 inform decision-making and draw conclusions. It is the process of making sense out of data that has been
                 processed. The interpretation of data helps us answer critical questions.


                 Process of Data Interpretation

                 The steps in the process of data interpretation are as follows:

                           Acquire            Process            Analyse            Interpret          Present


                 1.   Acquire: This initial step involves gathering raw data from diverse sources such as surveys, databases, or
                    sensors. It ensures that all relevant information is collected to provide a comprehensive dataset for analysis.

                 2.   Process: Once the data is collected, it undergoes cleaning and organisation to remove errors, inconsistencies,
                    or irrelevant information. This step ensures that the data is in a standardised format and ready for further
                    analysis.

                 3.   Analyse:  In  this  phase,  the  cleaned  and  organised  data  is  scrutinised  to  identify  patterns,  correlations,
                    or  trends.  Statistical  methods,  algorithms,  or  data  visualisation  techniques  may  be  employed  to  extract
                    meaningful insights from the data.
                 4.   Interpret: After analysing the data, the results are interpreted to derive actionable insights or conclusions.
                    This involves understanding the implications of the analysis findings in the context of the problem or question
                    at hand.

                 5.   Present: The final step involves presenting the interpreted findings in a clear and engaging manner. This
                    could include visualisations such as tables, graphs or charts, along with concise summaries, to effectively
                    communicate the insights derived from the data analysis.

                 These steps make sure that working with data is organised, complete, and useful, so that organisations can make
                 smart choices based on the data.

                         Methods of Data Interpretation


                 Data interpretation is the process of making sense out of a collection of data that has been processed. This
                 collection may be present in various forms like bar graphs, line charts and tabular forms and other similar forms.

                 There are two ways to interpret data-





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