<prompt> Ke mefuta efe e tloaelehileng ea mesebetsi ea ho qhekella data?nan  <response>: Instruction in English: What are the common types of data manipulation tasks? . Response in English: Data manipulation refers to the process by which data is cleaned, transformed, and rearranged to improve its quality and usefulness. It is usually performed with the help of specialized software and involves several common tasks, including:1. Data cleaning: Involves checking the dataset for errors, inconsistencies or inaccuracies, and correcting or removing any such issues that are identified. This could include removing duplicates, filling missing values, and fixing typographical errors.2. Sorting: Arranging the data in a specific order based on certain criteria. For example, sorting a list of customer names in alphabetical order.3. Filtering: Involves selecting a subset of data based on certain criteria. For example, filtering a dataset to only include customers from a specific geographic region.4. Merging: Combining data from multiple sources into a single dataset. This could include merging data from different databases, spreadsheets or files.5. Aggregation: Summarizing or grouping data based on certain criteria to produce summary statistics, such as calculating the total, average, or count.6. Transformation: Changing the format or structure of the data to make it easier to work with or to improve its analysis. For example, converting data to a common unit of measurement, or transforming categorical data into numerical data.7. Transposition: Flipping the rows and columns of a dataset. This is useful when working with datasets that have more columns than rows or vice versa.Overall, data manipulation is a vital step in any data analysis process, as it enables analysts and researchers to extract meaningful insights from their data. . Response in Sesotho: Ho qhekella ha data ho bolela mokhoa oo data e hloekisoang, ho fetoloa, le ho hlophisoa bocha ho ntlafatsa boleng le molemo oa eona. Hangata e etsoa ka thuso ea software e khethehileng me e kenyelletsa mesebetsi e maloa e tloaelehileng, e kenyeletsang: 1. Ho hloekisa boitsebiso: Ho kenyelletsa ho hlahloba dataset bakeng sa liphoso, ho se lumellane kapa ho se nepahale, le ho lokisa kapa ho tlosa lintlha leha e le life tse joalo tse khetholloang. Sena se ka kenyelletsa ho tlosa likopi, ho tlatsa lintlha tse sieo, le ho lokisa liphoso tsa mongolo. 2. Ho hlopha: Ho hlophisa lintlha ka tatellano e itseng ho latela litekanyetso tse itseng. Mohlala, ho hlopha lethathamo la mabitso a bareki ka tatellano ea alfabeta. 3. Ho sefa: Ho kenyelletsa ho khetha karoloana ea data ho latela maemo a itseng. Mohlala, ho sefa dataset ho kenyelletsa bareki ba sebaka se itseng feela. 4. Ho kopanya: Ho kopanya lintlha tse tsoang mehloling e mengata ho ea ho dataset e le ngoe. Sena se ka kenyelletsa ho kopanya data ho tsoa ho li-database tse fapaneng, li-spreadsheet kapa lifaele. 5. Aggregation: Ho akaretsa kapa ho hlophisa lintlha ho latela litekanyetso tse itseng ho hlahisa lipalo-palo tse akaretsang, tse kang ho bala kakaretso, karolelano, kapa palo. 6. Phetoho: Ho fetola sebopeho kapa sebopeho sa data ho etsa hore ho be bonolo ho sebetsa kapa ho ntlafatsa tlhahlobo ea eona. Ka mohlala, ho fetolela data ho tekanyo e tloaelehileng, kapa ho fetola lintlha tsa sehlopha ho ba lintlha tsa linomoro. 7. Phetoho: Ho phethola mela le likholomo tsa dataset. Sena se bohlokoa ha o sebetsa le li-dataset tse nang le mela e mengata ho feta mela kapa ka tsela e fapaneng. Ka kakaretso, ho qhekella ha data ke mohato oa bohlokoa tsebetsong efe kapa efe ea tlhahlobo ea data, kaha e thusa bahlahlobisisi le bafuputsi ho fumana lintlha tsa bohlokoa ho data ea bona. 