Essential Statistics for Beginners in Data Science

"Essential Statistics for Beginners in Data Science" provides a foundational understanding of key statistical concepts crucial for data analysis. It covers types of data, descriptive statistics, probability, and inferential statistics, empowering aspiring data scientists to make data-driven decisions. This guide emphasizes practical applications and visualization techniques, equipping beginners with the tools necessary to interpret and analyze data effectively.

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Essential Statistics for
Beginners in
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Introduction to Data science & Statistics
Definition of Data science:
Data science is an interdisciplinary field that uses scientific
methods, algorithms, and systems to extract knowledge and
insights from structured and unstructured data.
Definition of Statistics:
The science of collecting, analyzing, interpreting, and
presenting data.
Importance in Data Science:
Helps in making informed decisions based on data analysis.
Overview of Key Concepts: Descriptive vs. Inferential statistics.

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Types of Data
Quantitative Data:
Continuous (e.g., height, weight)
Discrete (e.g., number of students)
Qualitative Data:
Nominal (e.g., gender, color)
Ordinal (e.g., satisfaction ratings)

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Descriptive Statistics
Measures of Central Tendency:
Mean: Average value
Median: Middle value
Mode: Most frequent value
Measures of Dispersion:
Range: Difference between max and
min
Variance: Measure of variability
Standard Deviation: Average distance
from the mean

5. Importance of Visualization:
Enhances understanding and insights from data.
Common Types of Charts:
Histograms: Show frequency distribution1.
Box Plots: Summarize data using quartiles2.
Scatter Plots: Show relationships between two variables3.
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Data Visualization
Scatter plot
Histograms
Box Plots

6. Definition of Probability:
Measure of the likelihood that an event will
occur.
Importance in Data Science:
Essential for decision-making under
uncertainty.
Key Concepts:
Sample Space: Set of all possible outcomes
Events: Specific outcomes
Conditional Probability: Probability of an
event given another event
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Probability Basics
Sample
Space
Events
Conditional
Probability

7. Definition of Statistical Distributions:
Describes how values of a variable are spread or distributed.
Common Distributions:
Normal Distribution: Bell-shaped curve
Binomial Distribution: Distribution of successes in a series of trials
Poisson Distribution: Distribution of events in a fixed interval
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Distributions
Normal Distribution Binomial Distribution Poisson Distribution

8. Definition and Purpose:
Concluding a population based on sample
data.
Key Concepts:
Hypothesis Testing: Procedure for testing
assumptions
Confidence Intervals: Range of values likely
to contain the population parameter
p-values: Measure of evidence against a null
hypothesis
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Inferential Statistics

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Thank You

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