The service that i provide is to analyze the data provided by the user, extracting it from any sources, cleaning said data and filter it to be as clear as possible, and visualizing said data for more clearer vision for the patterns, abnormalities, and statistical analysis that may arise from said data.
Data extraction has a wide array of approaches, sometimes i can simply extract data from a csv file, and some other times i can create a certain dataset to appeal to the client's needs and standards, if the client didnt provide such data - in such approach the cost of this service will sky rocket since dataset creation is a really huge task. Data forms the backbone of any data analytics you do. Regarding data, there are many things to go wrong – be it the construction, arrangement, formatting, spellings, duplication, extra spaces, and so on. To perform the data analytics properly I would need to use various data cleaning techniques so that the data that i am working with data is ready for analysis. I tend to firstly analyze the data for any discrepancies and/or quality improvements by dropping unnecessary columns, merging them, removing abnormal records, fixing the dataset if any column is structurally inaccurate, or by fixing certain values in the dataset itself - to make further analysis much more easier and accurate. After that i tidy the data up as much as possible to visualize it more easily and more accurately. Tidy data is a standard way of mapping the meaning of a dataset to its structure. A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types.
After all said and done, i collect this cleaned data and visualize it for further insights and statistical analysis that can be further looked at.