<prompt> Thathamisa liphapang pakeng tsa rasaense oa data le mohlahlobi oa data.nan  <response>: Instruction in English: List the differences between a data scientist and a data analyst. . Response in English: While both data scientists and data analysts work with data, there are several key differences between these two roles:1. Education and Skills: Data scientists often have a Ph.D. or Masters degree in a quantitative field such as mathematics, statistics or computer science. They have a strong foundation in machine learning, statistical modeling, and programming. Data analysts, on the other hand, typically have a Bachelors degree in a related field, with strong skills in data manipulation, data visualization, and data interpretation.2. Responsibilities: Data scientists are responsible for designing and implementing models to mine and analyze large datasets, making predictions and extracting insights from the data. Data analysts, on the other hand, work with structured data to generate reports, visualizations, and dashboards to help stakeholders make informed decisions.3. Scope: Data scientists work with a broad scope of tasks - from setting up data pipelines to building machine learning models and conducting experiments. Data analysts focus on a narrower range of tasks, such as cleaning and organizing data, performing statistical analysis, and creating reports.4. Toolset: Data scientists often use tools such as R, Python, and SQL, while data analysts may use tools such as Excel, Tableau, and SQL.In summary, data scientists possess a wider range of skills and responsibilities, with a focus on machine learning and predictive analytics, while data analysts have a more specific focus on generating insights and visualizations from structured data. . Response in Sesotho: Le hoja ka bobeli bo-rasaense ba data le bahlahlobi ba data ba sebetsa ka boitsebiso, ho na le liphapang tse maloa tsa bohlokoa pakeng tsa likarolo tsena tse peli: 1. Thuto le Tsebo: Bo-rasaense ba data hangata ba na le Ph.D. kapa lengolo la Master lefapheng la lipalo tse kang lipalo, lipalo kapa saense ea khomphutha. Ba na le motheo o tiileng oa ho ithuta ka mochini, ho etsa mohlala oa lipalo, le ho etsa mananeo. Bahlahlobisisi ba lintlha, ka lehlakoreng le leng, hangata ba na le lengolo la Bachelor lefapheng le amanang, le nang le tsebo e matla ea ho laola lintlha, pono ea data, le tlhaloso ea lintlha. 2. Boikarabello: Bo-ramahlale ba data ba na le boikarabello ba ho rala le ho kenya tsebetsong mehlala ea ho rafa le ho sekaseka li-dataset tse kholo, ho etsa likhakanyo le ho ntša lintlha ho tsoa ho data. Bahlahlobisisi ba lintlha, ka lehlakoreng le leng, ba sebetsa ka lintlha tse hlophisitsoeng ho hlahisa litlaleho, lipono, le li-dashboard ho thusa ba amehang ho etsa liqeto tse nepahetseng. 3. Bophahamo: Bo-ramahlale ba data ba sebetsa ka boholo bo pharaletseng ba mesebetsi - ho tloha ho theha lipeipi tsa data ho isa ho ho haha mefuta ea ho ithuta ka mochini le ho etsa liteko. Bahlahlobi ba lintlha ba shebana le mefuta e mengata e fokolang ea mesebetsi, joalo ka ho hloekisa le ho hlophisa lintlha, ho etsa tlhahlobo ea lipalo, le ho etsa litlaleho. 4. Toolset: Bo-rasaense ba data ba atisa ho sebelisa lisebelisoa tse kang R, Python, le SQL, ha bahlahlobi ba lintlha ba ka sebelisa lisebelisoa tse kang Excel, Tableau, le SQL. Ka bokhutšoanyane, bo-ramahlale ba data ba na le litsebo le boikarabello bo pharaletseng haholoanyane, ba tsepamisitse maikutlo ho ithuteng ka mochini le ho hlahloba lintho esale pele, athe bahlahlobisisi ba data ba tsepamisitse maikutlo haholo ho hlahiseng lintlha le lipono ho tsoa ho data e hlophisitsoeng. 