Differentiate AI, ML and DL - You will clearly distinguish AI, machine learning and deep learning, so you can judge when a learning model is justified and when a traditional programming approach is enough.
Write stronger prompts - You will learn to craft prompts with clear goals, context and precision. You will also practice role prompting, few-shot and zero-shot techniques for data analysis and LLM work.
Choose the right data - You will understand the difference between raw and processed data, as well as structured and unstructured sources, so you can select better input for analysis and modeling tasks.
Source data efficiently - You will explore practical data sources such as ERP, CRM, spreadsheets, APIs and IoT streams. This will help you design better collection processes with legal and ethical awareness.
Spot quality issues early - You will learn to identify missing values, measurement errors, inconsistencies and outdated records, allowing you to remove issues that weaken the reliability of analysis and models.
Prepare data for models - You will practice imputing missing values, removing duplicates, validating datasets and storing data securely in SQL, NoSQL and data lake environments for reliable downstream work.
Transform features wisely - You will learn when to use normalization, standardization, one-hot encoding and label encoding, so you can prepare variables properly for later modeling, comparison and evaluation.
Read patterns with EDA - You will use charts, histograms, heatmaps and correlation analysis to uncover trends, anomalies and outliers, helping you understand the dataset before any model is built.
Basic data handling - You should be comfortable reading tables, understanding rows and columns, and performing simple data operations in a spreadsheet or a similar tool before the training starts.
Basic computer skills - You should be able to use a computer, web browser and files efficiently, because during the training you will work with datasets, analytical tools and digital materials.
Analytical thinking - You should be able to draw simple conclusions from numbers and relationships, so you can more easily spot data issues, patterns and the logic of each preparation step.
Readiness for practice - You should be ready to complete hands-on exercises and analyze examples on your own, because the training is built around practical work from raw data to modeling.
