<prompt> Likarolo tse tharo tsa potefolio ea mahlale a data ke life?nan  <response>: Instruction in English: What are the three sections of a data science portfolio? . Response in English: A data science portfolio typically consists of three main sections:1. Projects: This section outlines the data science projects that the individual has worked on. These projects should demonstrate a strong understanding of data analysis, modeling, and interpretation, as well as expertise with the tools and techniques used in the field. Each project should be well-documented, with a clear description of the problem, the approach, the results, and any insights gleaned from the work.2. Skills and Tools: This section highlights the individuals proficiency with the technical skills and tools required for data science work. This can include programming languages like Python or R, frameworks such as TensorFlow or scikit-learn, and experience with data visualization tools like Tableau or ggplot2. Its important to showcase not only knowledge of these tools, but also the ability to use them in real-world projects.3. Education and Experience: This section provides an overview of the individuals education, training, and work experience in the field of data science. This can include formal education in a relevant field, participation in training programs, online courses, or workshops, and any prior work experience in data analysis or modeling. This section helps provide context for the individuals abilities and expertise in data science. . Response in Sesotho: Potefolio ea saense ea data hangata e na le likarolo tse tharo tse kholo: 1. Merero: Karolo ena e hlalosa merero ea saense ea data eo motho a sebelitseng ho eona. Merero ena e lokela ho bontsa kutloisiso e matla ea tlhahlobo ea data, ho etsa mohlala, le ho hlalosa, hammoho le boiphihlelo ba lisebelisoa le mahlale a sebelisoang tšimong. Morero o mong le o mong o lokela ho ngoloa hantle, o nang le tlhaloso e hlakileng ea bothata, mokhoa oa ho atamela, liphello, le lintlha leha e le life tse fumanoeng mosebetsing. 2. Tsebo le Lisebelisoa: Karolo ena e totobatsa bokhoni ba motho ka boqhetseke le lisebelisoa tse hlokahalang bakeng sa mosebetsi oa mahlale a data. Sena se ka kenyelletsa lipuo tsa mananeo joalo ka Python kapa R, meralo e kang TensorFlow kapa scikit-learn, le boiphihlelo ka lisebelisoa tsa pono ea data joalo ka Tableau kapa ggplot2. Ho bohlokoa ho bontsa eseng feela tsebo ea lisebelisoa tsena, empa hape le bokhoni ba ho li sebelisa mererong ea nnete ea lefatše. 3. Thuto le Boiphihlelo: Karolo ena e fana ka kakaretso ea thuto ea motho ka mong, koetliso le phihlelo ea mosebetsi lefapheng la saense ea data. Sena se ka kenyelletsa thuto ea semmuso lebaleng le amehang, ho nka karolo mananeong a koetliso, lithupelo tsa inthaneteng, kapa lithupelo, le boiphihlelo bofe kapa bofe ba pele ba mosebetsi oa tlhahlobo ea data kapa ho etsa mohlala. Karolo ena e thusa ho fana ka moelelo oa bokhoni ba motho le boitsebelo ba mahlale a data. 