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Data Science Crash Course

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In a data science crash course, you can expect to learn: 1. The basics of programming languages such as Python or R, including data structures, control structures, and functions. 2. Techniques for data cleaning and preprocessing, including handling missing data, data normalization, and feature scaling. 3. Statistical concepts and techniques for exploratory data analysis, including hypothesis testing, correlation analysis, and regression analysis. 4. Machine learning algorithms, such as decision trees, logistic regression, and neural networks, and techniques for model selection and evaluation. 5. Data visualization techniques using tools such as matplotlib or ggplot2 to create clear and informative charts and graphs that effectively communicate insights from data.

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DATA SCIENCE CRASH COURSE.
Introduction to Data Scienc
Data science is an interdis
processes, algorithms, and
and unstructured data. It c
science, and domain-specifi
way that can inform decisio

Data science involves a ran
data mining, data visualiza
large and complex datasets,
relationships and to build
algorithms and data-driven
organizational performance.

Data science has numerous a
healthcare, finance, market
the way organizations opera
responsive to customer need

TOOLS USED IN DATA SCIENCES

, regression, classification,

4. Natural Language Process
text data.

5. Deep Learning: Use Tenso
networks for image and text
tasks.

6. Data Wrangling: Use tool
wrangling.

7. Web Scraping: Use tools
websites and APIs.

These are just a few exampl
its versatility and large c
choice for data scientists
APIs can be an essential to
expanding the scope of thei
predictions.

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Uploaded on
April 28, 2023
Number of pages
6
Written in
2022/2023
Type
Class notes
Professor(s)
Zunair ahmad
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