Master Data Analyst Course in Bangalore with ProITBridge's Expert Course

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EXPLORATORY DATA ANALYSIS UNVEILING INSIGHTS IN
YOUR DATA
Data Analyst Course in Bangalore
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About Us
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. Expert
Faculty Our team of experienced and dedicated
instructors comprises industry experts 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.
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About our Courses
Data Science Course
Full Data Science and Ai program
Project Program
Embark on a learning journey designed by
experts, incorporating the latest advancements in
data science
OUR Courses
Learn from seasoned professionals and
industry experts who bring a wealth of
knowledge and real-world experience to the
classroom.
Placement Support
Mechine Learning Progrem
Artificial intelligence Progrem
Career Guidance
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Definition of Exploratory Data Analysis
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.
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Steps Involved in EDA
Univariate Analysis
Data Collection
Multivariate Analysis
Bivariate Analysis
Handling Missing Data
Feature Engineering
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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.

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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.

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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.

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