The document discusses data science as a field focused on analyzing data for insights, utilizing various tools and techniques for decision-making in areas like business and healthcare. It covers programming languages used in data science, machine learning methods, data visualization tools, big data handling, data cleaning techniques, and statistical methods. Additionally, it highlights cloud computing resources such as AWS, Google Cloud, and Microsoft Azure that support data analysis.

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Data Science
Tools and Techniques

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What is Data Science
Data science is the field of analyzing data
to find insights.
It involves using tools, techniques, and
algorithms.
Data science helps in decision-making for
businesses, healthcare, and more.

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Programming Languages in Data Science
Python: Popular for data analysis and
machine learning.
R: Great for statistics and
visualizations.
SQL: Used for managing and querying
databases.

4. Tableau: Easy-to-use tool for interactive
dashboards.
Power BI: Microsoft’s tool for visualizing
business data.
Matplotlib (Python): For creating graphs
and charts.
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Data Visualization Tools

5. Supervised Learning: Trains models using
labeled data.
Unsupervised Learning: Finds patterns in
unlabeled data.
Deep Learning: Uses neural networks for
complex data like images.
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Machine Learning Techniques

6. Hadoop: Handles large datasets across
multiple computers.
Apache Spark: Faster processing for
large-scale data.
NoSQL Databases: For unstructured or
large-scale data.
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Big Data Tools

7. Handling Missing Data: Replace or remove
missing values.
Removing Duplicates: Ensures data
accuracy.
Outlier Detection: Identifying extreme
values that might affect analysis.
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Data Cleaning Techniques

8. Descriptive Statistics: Summarizes data (mean,
median, mode).
Regression Analysis: Predicts relationships
between variables.
Hypothesis Testing: Tests if data supports a
certain theory.
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Statistical Techniques in Data Science

9. AWS (Amazon Web Services): Cloud tools
for data storage and processing.
Google Cloud Platform: Machine learning
tools and data storage.
Microsoft Azure: Provides scalable cloud
resources for data analysis.
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Cloud Computing in Data Science

10. These tools help us extract insights and make
informed decisions.
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