Data Science & Analytics · PPL

Exploratory Data Analysis Certification

A practical program designed to develop skills in exploring, cleaning, visualizing, and interpreting datasets to discover patterns, relationships, anomalies, and actionable insights.

  • ⏱3 DaysDuration
  • ◈PPLAccredited
  • ◐3 LanguagesArabic · English · Hindi
  • ◆₹6,499.00 Per delegate

This course is accredited by PPL

This is for all ppl accredited courses
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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Introduction to Exploratory Data Analysis

  • EDA fundamentals
  • Purpose of exploration
  • EDA workflow
  • Types of analysis
  • Business applications
  • Data-driven insights

Module 2: Understanding Data

  • Structured data
  • Numerical data
  • Categorical data
  • Ordinal data
  • Time-series data
  • Data characteristics

Module 3: Python for EDA

  • Python fundamentals
  • Data structures
  • Functions
  • Analytical workflows
  • Jupyter Notebook
  • Python libraries

Module 4: NumPy for Data Exploration

  • NumPy arrays
  • Array operations
  • Indexing and slicing
  • Aggregations
  • Statistical operations
  • Numerical analysis

Module 5: Pandas for EDA

  • Series and DataFrames
  • Importing datasets
  • Selecting data
  • Filtering
  • Sorting
  • Data inspection

Module 6: Data Profiling

  • Dataset dimensions
  • Data types
  • Summary information
  • Unique values
  • Frequency distributions
  • Initial quality assessment

Module 7: Data Cleaning for EDA

  • Missing values
  • Duplicate records
  • Incorrect values
  • Data type conversion
  • Data inconsistencies
  • Cleaning strategies

Module 8: Descriptive Statistics

  • Mean
  • Median
  • Mode
  • Variance
  • Standard deviation
  • Percentiles

Module 9: Univariate Analysis

  • Variable distributions
  • Frequency analysis
  • Histograms
  • Box plots
  • Summary measures
  • Distribution interpretation

Module 10: Bivariate Analysis

  • Variable relationships
  • Numerical relationships
  • Categorical comparisons
  • Cross-tabulation
  • Scatter plots
  • Relationship interpretation

Module 11: Multivariate Analysis

  • Multiple-variable relationships
  • Group comparisons
  • Interaction patterns
  • Multidimensional analysis
  • Visual exploration
  • Insight identification

Module 12: Distribution Analysis

  • Normal distribution
  • Skewness
  • Kurtosis
  • Distribution shapes
  • Density analysis
  • Transformation considerations

Module 13: Outlier Detection

  • Outlier concepts
  • Box plot method
  • IQR method
  • Z-score concepts
  • Business-rule detection
  • Outlier treatment

Module 14: Correlation Analysis

  • Correlation concepts
  • Pearson correlation
  • Spearman correlation
  • Correlation matrices
  • Heatmap concepts
  • Correlation interpretation

Module 15: Data Visualization with Matplotlib

  • Plotting fundamentals
  • Bar charts
  • Line charts
  • Scatter plots
  • Histograms
  • Visualization customization

Module 16: Advanced EDA Visualizations

  • Box plots
  • Violin plot concepts
  • Heatmaps
  • Pairwise analysis
  • Distribution comparisons
  • Multivariate visualization

Module 17: Categorical Data Analysis

  • Category frequencies
  • Proportions
  • Cross-tabulations
  • Group comparisons
  • Category relationships
  • Visual analysis

Module 18: Time-Based Exploratory Analysis

  • Date-time preparation
  • Time trends
  • Seasonal patterns
  • Period comparisons
  • Moving statistics
  • Temporal visualization

Module 19: Feature Relationship Analysis

  • Feature-target relationships
  • Feature interactions
  • Redundant variables
  • Informative features
  • Association patterns
  • Feature insights

Module 20: Automated & AI-Assisted EDA

  • Automated profiling
  • Data quality summaries
  • Visualization suggestions
  • AI-assisted pattern discovery
  • Insight summarization
  • Responsible AI usage

Module 21: Communicating EDA Insights

  • Insight selection
  • Data storytelling
  • Visualization interpretation
  • Business context
  • Analytical reporting
  • Recommendations

Module 22: Practical EDA Project

  • Dataset profiling
  • Data cleaning
  • Statistical exploration
  • Visualization
  • Pattern identification
  • Insight presentation
— 01.2 · Is it right for you?

Who it's for & what's included

Pick a delivery method to see exactly who it suits and everything you receive.

Who it's for

Classroom

Best for learners who want face-to-face tuition and to network with peers in person.

What's included

Everything you get

  • ✓ Live instructor on-site
  • ✓ Printed workbook & materials
  • ✓ Group exercises & case studies
Who it's for

Online Instructor-Led

Best for learners who want a live instructor and a fixed schedule, without the travel.

What's included

Everything you get

  • ✓ Live instructor via video call
  • ✓ Digital workbook & resources
  • ✓ Session recordings
Who it's for

Self-Paced

Best for self-motivated learners who need maximum flexibility around work and life.

What's included

Everything you get

  • ✓ On-demand video lessons
  • ✓ Interactive quizzes
  • ✓ 24/7 access on any device
— What you will master

Course Objectives

01

Understand the purpose and workflow of exploratory data analysis.

02

Profile and assess datasets before advanced analytical modelling.

03

Clean and prepare datasets for reliable exploratory analysis.

04

Apply descriptive statistics to summarize and understand data.

05

Perform univariate, bivariate, and multivariate analysis.

06

Identify distributions, correlations, patterns, and potential outliers.

07

Create effective visualizations to communicate analytical findings.

08

Translate exploratory findings into meaningful business insights.

— Questions answered

Frequently Asked Questions

What is Exploratory Data Analysis?
Exploratory Data Analysis is the process of examining and visualizing datasets to understand their structure, distributions, patterns, relationships, anomalies, and potential insights.
Who should attend this course?
The course is suitable for data analysts, data scientists, BI professionals, analytics professionals, data engineers, and working professionals who analyze datasets.
Do I need previous Python experience?
Basic Python knowledge is helpful because the course uses Python, Pandas, NumPy, and visualization libraries for practical exploratory analysis.
Which tools and techniques are covered?
The course covers Python, Pandas, NumPy, Matplotlib, descriptive statistics, data profiling, distribution analysis, correlation, outlier detection, and analytical visualization.
What practical skills will I develop?
You will develop skills in data profiling, cleaning, statistical exploration, visualization, correlation analysis, outlier detection, pattern discovery, and communicating data-driven insights.
— Trusted by learners

What our delegates say

★★★★★

"The structure, the practice exams, the instructor — all top tier. Passed first try."

AS
Aarti SharmaSenior Project Manager · TCS
★★★★★

"Best training I have attended. The content is exactly what modern projects need."

JD
James DonovanProgramme Director · Capgemini
★★★★★

"24/7 support actually means 24/7 — got help on my mock exam at 2am. Worth every dollar."

MO
Maya OkaforPMO Lead · Standard Bank

★ 4.8 / 5 from 12,000+ verified learner reviews on Trustpilot & Google.

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