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Class notes Datascience Data Science
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These are my basics notes for classes 1 to 4.
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Data Science
Flashcards
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Flashcards
10 Flashcards
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Some examples from this set of practice questions
1.
What is data science?
Answer: Data science is an interdisciplinary field that uses various techniques, algorithms, and processes to extract insights and knowledge from data. It combines skills from statistics, computer science, domain knowledge, and data engineering to analyze and interpret data.
2.
What is the difference between structured and unstructured data?
Answer: Structured data is organized and easily searchable, typically residing in relational databases. Unstructured data lacks a predefined structure and can include text, images, videos, and more.
3.
What is data preprocessing in data science?
Answer: Data preprocessing involves cleaning and transforming raw data into a format suitable for analysis. It includes tasks like handling missing values, outlier detection, and feature scaling.
4.
What is the primary goal of data visualization in data science?
Answer: To present data in a clear and understandable manner
5.
What type of data is categorical data?
Answer: Data with distinct categories or labels
Content preview
DATA SCIENCEData science is a multidisciplinary field that involves using various
techniques, algorithms, processes, and systems to extract valuable insights and
knowledge from data. It combines elements of statistics, computer science,
mathematics, and domain expertise to analyze and ieterpret complex datasets
and make data-driven decisions.
Here are some key components and aspects of data science :
1.DATA COLLECTION :
Data scientists collect data from various sources.
Which can include structured data and unstructured data. Data can be
gathered from sensors, Web scraping, surveys, and more.
2.DATA CLEANING AND PREPROCESSING :
Raw data often contains error, missing values, and
inconsistencies. Data scientists clean and preprocess the data to ensure it is
accurate and ready for analysis. This may involve data imputation,
normalization, and transformation
3.EXPLORATORY DATA ANALYSIS (EDA) :
EDA involves visualizing and summarizing data to
gain a better understanding of its characteristics. Data scientists use various
tools and techniques to identify patterns, trends, outliers and potential
relationships within the data.
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