Data Science & Analytics · PPL

Pandas & NumPy for Data Analysis Certification

A practical program designed to develop essential Pandas and NumPy skills for data manipulation, cleaning, transformation, analysis, and efficient numerical computing.

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

This course is accredited by PPL

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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Introduction to Pandas & NumPy

  • Data analysis fundamentals
  • Python data ecosystem
  • Pandas overview
  • NumPy overview
  • Analytical workflows
  • Common applications

Module 2: NumPy Array Fundamentals

  • NumPy arrays
  • Array creation
  • Array dimensions
  • Shapes and sizes
  • Data types
  • Array properties

Module 3: NumPy Indexing & Slicing

  • Array indexing
  • Array slicing
  • Boolean indexing
  • Fancy indexing
  • Multi-dimensional arrays
  • Data selection

Module 4: NumPy Array Operations

  • Arithmetic operations
  • Element-wise operations
  • Broadcasting
  • Universal functions
  • Comparison operations
  • Logical operations

Module 5: NumPy Aggregation & Statistics

  • Sum and product
  • Mean and median
  • Minimum and maximum
  • Variance
  • Standard deviation
  • Percentiles

Module 6: Reshaping & Combining Arrays

  • Reshaping
  • Flattening
  • Transposing
  • Concatenation
  • Stacking
  • Splitting arrays

Module 7: Vectorization & Broadcasting

  • Vectorized computation
  • Broadcasting rules
  • Removing unnecessary loops
  • Array-based calculations
  • Performance benefits
  • Efficient numerical processing

Module 8: Introduction to Pandas

  • Series
  • DataFrames
  • Indexes
  • Creating DataFrames
  • Data inspection
  • Pandas data types

Module 9: Importing & Exporting Data

  • CSV files
  • Excel files
  • JSON data
  • SQL data concepts
  • Reading datasets
  • Exporting results

Module 10: Selecting & Filtering Data

  • Column selection
  • Row selection
  • loc
  • iloc
  • Boolean filtering
  • Conditional selection

Module 11: Data Cleaning with Pandas

  • Missing values
  • Duplicate records
  • Incorrect values
  • Data type conversion
  • String cleaning
  • Data validation

Module 12: Handling Missing Data

  • Detecting missing values
  • Dropping missing values
  • Filling missing values
  • Forward filling
  • Backward filling
  • Imputation concepts

Module 13: Sorting & Ranking Data

  • Sorting values
  • Sorting indexes
  • Multi-column sorting
  • Ranking
  • Top and bottom values
  • Ordered analysis

Module 14: Grouping & Aggregation

  • GroupBy
  • Aggregate functions
  • Multiple aggregations
  • Transform operations
  • Group filtering
  • Summary analysis

Module 15: Combining DataFrames

  • Merging
  • Joining
  • Concatenating
  • Join types
  • Merge keys
  • Relationship validation

Module 16: Reshaping Data

  • Pivot tables
  • Pivoting
  • Melting
  • Stacking
  • Unstacking
  • Wide and long formats

Module 17: Working with Text Data

  • String methods
  • Text cleaning
  • Pattern matching
  • String extraction
  • String replacement
  • Regular expression concepts

Module 18: Date & Time Analysis

  • DateTime conversion
  • Date components
  • Time differences
  • Date filtering
  • Resampling
  • Time-based analysis

Module 19: Exploratory Data Analysis

  • Descriptive statistics
  • Distribution analysis
  • Correlation
  • Outlier identification
  • Pattern discovery
  • Analytical summaries

Module 20: Data Visualization with Pandas

  • Plotting fundamentals
  • Line charts
  • Bar charts
  • Histograms
  • Scatter plots
  • Analytical visualization

Module 21: Performance Optimization

  • Efficient data types
  • Vectorized operations
  • Memory usage
  • Query optimization
  • Large dataset handling
  • Pandas best practices

Module 22: Practical Data Analysis Project

  • Dataset import
  • Data cleaning
  • Transformation
  • Aggregation
  • Exploratory analysis
  • 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 roles of Pandas and NumPy in Python-based data analysis.

02

Create, manipulate, reshape, and analyze NumPy arrays efficiently.

03

Use Pandas Series and DataFrames for structured data analysis.

04

Clean missing, duplicate, inconsistent, and incorrectly formatted data.

05

Filter, group, aggregate, merge, and reshape complex datasets.

06

Perform statistical and exploratory analysis using Pandas and NumPy.

07

Work effectively with textual, numerical, and time-based datasets.

08

Optimize data processing workflows using vectorized and efficient operations.

— Your learning path

Where this fits in your Data Science & Analytics journey

Click any stage to open its detail page. You are at Pandas & NumPy for Data Analysis Certification.

Start Build Advanced Mastery
— Questions answered

Frequently Asked Questions

What are Pandas and NumPy?
Pandas and NumPy are Python libraries widely used for structured data manipulation, numerical computing, data preparation, and analytical workflows.
Who should attend this course?
The course is suitable for data analysts, aspiring data scientists, BI professionals, analytics professionals, Python developers, and working professionals who regularly work with data.
Do I need previous Python experience?
Basic Python knowledge is recommended because the course focuses on using Python libraries for practical data manipulation and analysis.
Which topics are covered?
The course covers NumPy arrays, vectorization, Pandas DataFrames, filtering, cleaning, missing data, grouping, merging, reshaping, time-series manipulation, exploratory analysis, and optimization.
What practical skills will I develop?
You will develop skills in numerical computing, data cleaning, transformation, filtering, aggregation, dataset integration, exploratory analysis, visualization, and efficient data processing.
— 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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