Data Science — definition?
Interdisciplinary field extracting knowledge from data.
Data collection methods?
Surveys, web scraping, sensors, handling missing data, removing duplicates, transformation.
Data cleaning — purpose?
Ensure data quality for accurate analysis.
Exploratory Data Analysis — role?
Understand data patterns, relationships, and outliers.
Techniques of EDA?
Summary stats, visualization, correlation analysis.
Statistical inference — purpose?
Draw conclusions about populations from samples.
Hypothesis testing — role?
Evaluate assumptions using sample data.
Model validation — methods?
Cross-validation, train/test split.
Performance metrics?
Accuracy, precision, recall, F1 score.
Overfitting — meaning?
Model captures noise, poor generalization.
Data visualization — purpose?
Communicate data insights visually.
Common visualization tools?
Tableau, Matplotlib, Seaborn.
Big Data — definition?
Handling large datasets beyond traditional tools.
Hadoop — function?
Distributed storage and processing using HDFS and MapReduce.
Spark — advantage?
Fast, in-memory distributed data processing.
Supervised learning — example?
Linear regression, decision trees.
Answer 8 questions on Introduction to Data Science Fundamentals.
1. How do statistical inference and machine learning algorithms differ in their primary objectives within data science?
2. What is the primary function of data cleaning in the data collection process?
Go over the full study guide for Introduction to Data Science Fundamentals.
Read the study guide →Cybersécurité
Intelligence Artificielle
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