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1. EXPLORATORY
DATA ANALYSIS
UNVEILING
INSIGHTS IN
YOUR DATA
#Data Analyst Course in Bangalore

2. we take pride in being the leading destination for
aspiring data scientists and professionals
seeking to elevate their skills in the dynamic
world of data science.
About Us
Expert Faculty: Our team of experienced and
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and academics, ensuring that you learn from the
best minds in the field.
Comprehensive Curriculum: Our carefully
crafted curriculum is designed to cover the
latest tools, techniques, and technologies in data
science, giving you a holistic understanding of
the subject.

3. Embark on a learning journey designed by
experts, incorporating the latest
advancements in data science
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4. It employs various statistical and graphical
techniques to understand the underlying
patterns, relationships, and trends within the
data. EDA often precedes formal statistical
modeling and is a crucial step in the data
analysis process, helping analysts and
researchers make informed decisions about
the data and the phenomena they represent.
Definition of Exploratory Data Analysis

5. Steps Involved in
EDA
Univariate Analysis
Feature Engineering
Multivariate Analysis
Data Collection
Handling Missing
Data
Bivariate Analysis

6. Best Practices in EDA
Best practices in Exploratory Data Analysis
(EDA) are essential for ensuring that the
insights drawn from the data are accurate,
reliable, and actionable. Here are some key
best practices in EDA
Understand the Data Domain:
Familiarize yourself with the domain from
which the data comes. Understanding the
context can guide your analysis and help you
ask relevant questions about the data.

7. Why EDA Matters
Exploratory Data Analysis (EDA) matters
significantly for several reasons in the realm of
data science and analytics
Understanding the Data: EDA helps analysts
grasp the fundamental characteristics of the
dataset. By examining the data's structure,
range, distribution, and patterns, analysts can
make sense of the information they are
working with.

8. Key EDA Techniques
Exploratory Data Analysis (EDA) involves a
variety of techniques to uncover patterns,
relationships, and insights within a dataset.
Descriptive Statistics: Descriptive statistics like
mean, median, standard deviation, and
quartiles provide a summary of numerical
data.

9. Frequently Asked
Questions (FAQ)
What is Data
Science?
What is Machine
Learning?
What is the
difference between
Data Science and
Data Analytics?
Data scientists collect data
from diverse sources,
including databases,
sensors, social media, and
more.
In machine learning, a
computer system is trained
using large amounts of
data, allowing it to identify
patterns, correlations, and
trends within the data
Data Science involves a
broader spectrum, including
data analytics, and focuses
on complex problem-
solving using various
techniques

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