{"product_id":"prompt-engineering-for-data-science-learning-kit","title":"Prompt Engineering for Data Science - Learning Kit","description":"\u003cdiv class=\"dt-product-description-v7\"\u003e\n  \u003ch2 class=\"dt-heading-xl\"\u003ePrompt Engineering for Data Science Learning Kit\u003c\/h2\u003e\n\n  \u003cdiv class=\"dt-body-premium\"\u003e\n    Prompt Engineering for Data Science is designed to help data professionals use generative AI more effectively across real analytics and machine learning workflows. This training focuses on how structured prompting can support SQL generation, Python tasks, exploratory data analysis, model interpretation and more efficient technical problem-solving.\n    \u003cbr\u003e\u003cbr\u003e\n    Based on the provided course content, the Learning Kit supports professionals who want to integrate LLMs into existing Python, R and SQL workflows in a more practical and controlled way. It is a strong fit for data scientists, data analysts, ML engineers, quantitative researchers and BI professionals working with increasingly complex datasets and reporting demands.\n    \u003cbr\u003e\u003cbr\u003e\n    This product provides training only and does not include a certification exam voucher.\n  \u003c\/div\u003e\n\n  \u003cdiv class=\"dt-container-v7\"\u003e\n    \u003cdiv class=\"dt-grid-v7\"\u003e\n      \u003cdiv\u003e\n        \u003ch3 class=\"dt-heading-card\"\u003eWho is this training for\u003c\/h3\u003e\n        \u003cul class=\"dt-list-premium\"\u003e\n          \u003cli\u003eData scientists automating repetitive data wrangling and feature engineering tasks\u003c\/li\u003e\n          \u003cli\u003eData analysts using natural language to generate SQL and Python workflows\u003c\/li\u003e\n          \u003cli\u003eMachine learning engineers integrating LLM output into production and analysis pipelines\u003c\/li\u003e\n          \u003cli\u003eQuantitative researchers exploring synthetic data and AI-assisted reasoning\u003c\/li\u003e\n          \u003cli\u003eBI professionals translating technical findings into clear business summaries\u003c\/li\u003e\n        \u003c\/ul\u003e\n      \u003c\/div\u003e\n\n      \u003cdiv\u003e\n        \u003ch3 class=\"dt-heading-card\"\u003eReady for roles like\u003c\/h3\u003e\n        \u003cul class=\"dt-list-premium\"\u003e\n          \u003cli\u003eAI-Augmented Data Scientist\u003c\/li\u003e\n          \u003cli\u003eData Analytics Engineer\u003c\/li\u003e\n          \u003cli\u003eMachine Learning Specialist\u003c\/li\u003e\n          \u003cli\u003ePrompt Engineer\u003c\/li\u003e\n          \u003cli\u003eInsights Automation Lead\u003c\/li\u003e\n        \u003c\/ul\u003e\n      \u003c\/div\u003e\n    \u003c\/div\u003e\n  \u003c\/div\u003e\n\n  \u003ch3 class=\"dt-heading-section\"\u003eCourse Modules\u003c\/h3\u003e\n\n  \u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eFoundations of Technical Prompting \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n      Learn the structure of effective prompts for technical work, including zero-shot and few-shot prompting, persona setting and output constraints that fit data workflows.\n    \u003c\/div\u003e\n  \u003c\/details\u003e\n\n  \u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eAutomating EDA and Data Cleaning \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n      Build practical skills in using AI for anomaly detection, missing value handling, feature engineering ideas, exploratory analysis and data explanation for mixed audiences.\n    \u003c\/div\u003e\n  \u003c\/details\u003e\n\n  \u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003ePrompting for SQL and Code Generation \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n      Learn how prompt engineering can support text-to-SQL, Python generation, code debugging and more efficient technical problem-solving in analytics environments.\n    \u003c\/div\u003e\n  \u003c\/details\u003e\n\n  \u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003eAdvanced Reasoning Methods \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n      Explore advanced prompting methods such as chain-of-thought, least-to-most prompting and code-assisted reasoning for more complex data and statistical tasks.\n    \u003c\/div\u003e\n  \u003c\/details\u003e\n\n  \u003cdetails class=\"dt-acc-item-v7\"\u003e\n    \u003csummary\u003ePrompts in the ML Lifecycle \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n    \u003cdiv class=\"dt-acc-content\"\u003e\n      Understand how LLMs can support model documentation, explanation, synthetic data use cases and validation workflows throughout modern machine learning processes.\n    \u003c\/div\u003e\n  \u003c\/details\u003e\n\n  \u003ch3 class=\"dt-heading-section\"\u003eCareer Opportunities\u003c\/h3\u003e\n\n  \u003cdiv class=\"dt-body-premium\"\u003e\n    This Learning Kit supports professionals who want to combine strong data skills with modern AI workflow capabilities. It is especially useful for teams and individuals who want to reduce repetitive work, speed up analysis and improve communication around technical data findings.\n    \u003cbr\u003e\u003cbr\u003e\n    The training is also relevant for organizations that want to build more efficient analytics capability across data science, BI and machine learning teams.\n    \u003cbr\u003e\u003cbr\u003e\n    For organizations looking to train multiple employees, visit \u003ca href=\"https:\/\/www.divitrain.com\/nl-nl\/pages\/for-teams\"\u003eteam training options\u003c\/a\u003e.\n    \u003cbr\u003e\u003cbr\u003e\n    You can also explore more AI-focused options in the \u003ca href=\"https:\/\/www.divitrain.com\/collections\/artificial-intelligence-ai\"\u003eArtificial Intelligence training collection\u003c\/a\u003e.\n  \u003c\/div\u003e\n\n  \u003ch3 class=\"dt-heading-section\"\u003eFrequently Asked Questions\u003c\/h3\u003e\n\n  \u003cdiv class=\"dt-faq-accordion-v7\"\u003e\n    \u003cdetails class=\"dt-acc-item-v7\"\u003e\n      \u003csummary\u003eIs this just a course on how to use ChatGPT \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n      \u003cdiv class=\"dt-acc-content\"\u003e\n        No. The course focuses on prompt engineering logic for data science tasks and is relevant to broader LLM-based workflows, not just one tool.\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails class=\"dt-acc-item-v7\"\u003e\n      \u003csummary\u003eWill I learn how prompting helps with SQL and Python \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n      \u003cdiv class=\"dt-acc-content\"\u003e\n        Yes. Based on the provided content, the training covers text-to-SQL, Python support, debugging and prompt-based technical output generation.\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails class=\"dt-acc-item-v7\"\u003e\n      \u003csummary\u003eDo I need to be a senior programmer to take this course \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n      \u003cdiv class=\"dt-acc-content\"\u003e\n        No. A basic understanding of Python or SQL is recommended, but the course is designed to help data professionals accelerate their technical workflows with AI.\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails class=\"dt-acc-item-v7\"\u003e\n      \u003csummary\u003eDoes this course cover advanced prompting methods \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n      \u003cdiv class=\"dt-acc-content\"\u003e\n        Yes. Based on the provided content, the course includes advanced methods such as few-shot prompting, chain-of-thought and least-to-most prompting.\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails class=\"dt-acc-item-v7\"\u003e\n      \u003csummary\u003eHow does this course address privacy and security \u003cspan class=\"dt-acc-toggle\"\u003e+\u003c\/span\u003e\u003c\/summary\u003e\n      \u003cdiv class=\"dt-acc-content\"\u003e\n        The training includes guidance on anonymized prompting and responsible AI use so learners can work more safely with sensitive or proprietary data.\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n  \u003c\/div\u003e\n\u003c\/div\u003e","brand":"DiviTrain.com","offers":[{"title":"Default Title","offer_id":57051585839429,"sku":null,"price":287.2,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0280\/0350\/0118\/files\/prompt-engineering-for-data-science-learning-kit-divitrain.png?v=1771857026","url":"https:\/\/www.divitrain.com\/en-eu\/products\/prompt-engineering-for-data-science-learning-kit","provider":"DiviTrain.com","version":"1.0","type":"link"}