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Week - 1 |
EN:Data Description: What is data and why should we be data literate? |
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Week - 2 |
EN: Data sources: Open data, company data, web data, research data, public records |
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Week - 3 |
EN: Data types: Structured vs. Unstructured data, Numerical vs. Categorical data |
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Week - 4 |
EN: Data collection and ethics |
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Week - 5 |
EN: Data storage: Introduction to R, tidyverse package exploration |
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Week - 6 |
EN: EN: EN: Data cleaning, identifying and understanding variables |
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Week - 7 |
EN: Calculating descriptive statistics, making sense of and interpreting relationships. |
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Week - 8 |
EN: Bar charts (stacked and side-by-side), line and area charts, radar charts. The “Data-to-Ink Ratio” principle in visualization. |
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Week - 9 |
EN: Data visualization: Histograms, Density curves, Box plots, violin plots, scatter plots, heatmaps; legends |
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Week - 10 |
EN: Data Storytelling: How to interpret a graph? Techniques for effectively presenting findings. |
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Week - 11 |
Introduction to Data Science: What is data science, what does a data scientist do, roles and tools in data science |
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Week - 12 |
Introduction to the World of Data Science and Machine Learning: Fundamental Concepts in Machine Learning: Supervised and Unsupervised Learning, Model Performance |
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Week - 13 |
EN: Introduction to Machine Learning: Supervised learning, Logic of Regression and Classification, Linear regression application, KNN (K-Nearest Neighbors) algorithm application
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Week - 14 |
EN: Unsupervised Learning:Logic of Clustering,
Generative AI (GenAI) and the relationship of large language models with data |
Eskisehir Technical University Info Package