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Data Management for Wits: About Us

These are general advice , data varies hugely between types of research and projects . Please make an appointment for a consultation if you need more specific advice

Data Science


Do your have any problems with your results or Data??

Are you having trouble in interpreting or analysing your  data??

If so, Let our Data Guru's help You!!!!



Service Offered

                                            Data Cleansing
  •  Data Cleansing is the process of detecting and correcting(or removing) corrupt or inaccurate records  from a record set, or database.
  •  And refers to identifying incomplete, incorrect parts of the data and then replacing, modifying, or deleting the dirty  data.
  • After cleansing, a data set  should be consistent with other similar data sets in the system.
  • The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.
  • The validation may be strict (such as rejecting any address that does not have a valid postal code) or  fuzzy (such as correcting records that partially match existing, known records).

                                     Data Analysis

  • Data analysis is a process of inspecting,cleansing,transforming and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.Or is the process of bringing order, structure and meaning to the mass of collected data.
  • It is described as messy, ambiguous and time-consuming, but also as a creative and fascinating process.

  • Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.

Data analysis has two  methods which are  Qualitative and Quantitative methods of data analysis.According to  Kreuger and Neuman,qualitative and quantitative analyzes are similar in four ways.Boths involves the following: 

  • Inference - the use of reasoning to reach a conclusion based on evidence.
  •  Striving to avoid errors, false conclusions and misleading inferences.
  •  A public method or process - revealing their study design in some way.
  • Comparison as a central process – identification of patterns or aspects that are similar or different.

 Data Management"Research "

  • Data Management is an administrative process that includes acquiring, validating, storing, protecting, and processing required data to ensure the accessibility, reliability, and timeliness of the data for its users.
  • RDM services refer to the storage, access and preservation of data produced in particular investigations or research projects.

  • These services support the full data lifecycle including data management planning, digital curation and metadata creation and conversion.

  • The aim of the RDM services is to ensure research integrity and enable the use of existing data for future research endeavors.

  • Data management solutions make processing, validation, and other essential functions simpler and less time-intensive.

                                              Benefits of Data Management  click here.

         Data Interpretation
  • Data interpretation is the process of making sense of numerical data that has been collected, analyzed, and presented.Or it also refers to the process of critiquing and determining the significance of important information, such as survey results, experimental findings, observations or narrative reports.
  • Interpreting data is an important critical thinking skill that helps you comprehend text books, graphs and tables.
  • Researchers use a similar but more meticulous process to gather, analyze and interpret data.
  • Experimental scientists base their interpretations largely on objective data and statistical calculations.
  • Social scientists interpret the results of written reports that are rich in descriptive detail but may be devoid of mathematical.
  • Metadata  is  a set of data that describes and gives information about other data.
  • Or is data [information] that provides information about other data.
  • Three distinct types of metadata exist: descriptive metadata, structural metadata, and administrative metadata.
  1. Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
  2. Structural metadata is metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters.
  3. Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it.
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What is meant by Data

  • Data is measured,collected and reported and analyzed, whereupon it can be  visualized using graphs, images or other analysis tools.Or data is a Set of  values of Qualitative or Quantitative variables.
  • Raw data is a collection of numbers or characters before it has been "cleaned" and corrected by researchers.

Where by : Set  is a collection of distinct objects, considered as an object in its own  right. Eg, the numbers 2, 4 and 6  are distinct object when considered separately,but when they are considered collectively they form a single set of size three, {2,4,6}.

                 : Qualitative Data are properties that are observed and can generally not be measured with a numerical result. 

                :Quantitative Data is a classification that describes the nature of information within the values assigned to variables.

                :Variable is a logical  set of attributes.


University of the




 3th Floor, East Campus,

Data Services Librarian

Contact US @

Nina Lewin



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