DAVLectuer3 Exploratory data analysis .pdf

1. Data Analysis
and
Visualization
CS 352)
Week 1- Introduction

2. Textbook
Data Analysis and Visualization Using
Python by Dr Ossama, 2018
Python for Data Analysis by Wes McKinney,
2022
Ultimate Python Libraries for Data Analysis
and Visualization by Abhinaba Banerjee,
2024
Python Data Science Handbook by Jake
VanderPlas , 2022

3. Summary of
course
objectives:
Understand and write programs to store
and manipulate data and measurements.
Implement the fundamental concepts of
interactive visualization of data.
Implement common data
transformations and statistical analysis
Demonstrate current machine learning
techniques for prediction and
knowledge discovery

4. Your intentions/expectations?
In what ways do you think this course could help your
professional development?
What topics are you most interested in?
What suggestions do you have for the instructors and the
course?

5. Introduction to Data Analysis
Overview of key
Python libraries
for data analysis
01
Libraries provide
built-in functions
and modules
02
Extensive range
of functionalities
available
03

6. Types of Python Data Analysis Libraries
Categorized into
three main groups:
Scientific
Computing Libraries
Data Visualization
Libraries
Machine Learning
Libraries

7. Scientific Computing Libraries
Essential for data manipulation
and analysis
Major libraries:
Pandas
NumPy
SciPy

8. Pandas - Data Manipulation
Provides data
structures and tools
for analysis
Fast access to
structured data
Key feature:
DataFrame (2D table
with rows & columns)
Supports easy
indexing functionality

9. NumPy - Array-
Based Computation
Uses arrays as primary
input/output
Supports matrix
operations
Enables fast array
processing with
minimal coding

10. SciPy - Advanced
Math Functions
Includes
functions for:
Optimization
Linear algebra
Signal
processing
Statistics
Also supports
data visualization

11. Data Visualization
Libraries
Essential for
communicating results
effectively
Major libraries:
Matplotlib
Seaborn

12. Matplotlib - Customizable Graphs
Most well-known
visualization
library
Used for
creating graphs
and plots
Highly
customizable
and versatile

13. Seaborn -
High-Level
Visualization Built on
Matplotlib
Simplifies
generation of:
Heat maps
Time series
plots
Violin plots

14. Machine Learning Libraries
Used for predictive modeling and
analytics
Key libraries:
Scikit-learn
Statsmodels

15. Scikit-learn - ML Algorithms
Contains tools for:
Regression
Classification
Clustering
Built on NumPy, SciPy, and
Matplotlib

16. Statsmodels - Statistical Analysis
Allows data exploration Supports:
Estimating statistical models
Performing statistical tests

17. Best Dataset Websites
Finding the Best Datasets for Data Science & Research

18. General-Purpose Datasets
Kaggle - Large
collection for ML & AI
Google Dataset
Search - Aggregated
datasets
UCI ML Repository -
Academic & research-
focused
Data.gov - U.S.
government open
data
Data World -
Community-driven
datasets

19. Big Data & Business Datasets
AWS Open Data
Registry - AI & Cloud
datasets
Google Cloud Public
Datasets - Cloud-
based analytics
Microsoft Azure
Open Datasets - AI &
Business Intelligence
FiveThirtyEight -
Political, sports, and
social data

20. Health & Science Datasets
WHO DATA - GLOBAL
HEALTH DATASETS
CDC OPEN DATA - PUBLIC
HEALTH AND DISEASE-
RELATED DATASETS
PHYSIONET - CLINICAL AND
PHYSIOLOGICAL HEALTH
DATA

21. Finance & Economics Datasets
WORLD BANK OPEN DATA -
ECONOMIC INDICATORS
IMF DATA -
MACROECONOMIC &
FINANCIAL STATISTICS
QUANDL - MARKET &
FINANCIAL DATA

22. Geospatial & Environmental Datasets
NASA Earth Data - Climate
& satellite imagery
OpenStreetMap - Free
geospatial data
USGS Earth Explorer -
Remote sensing data

23. Choosing the Right Dataset
SELECT BASED ON PROJECT
NEEDS
DATASET QUALITY, SIZE, AND
SOURCE CREDIBILITY
USE PLATFORMS LIKE KAGGLE,
GOOGLE DATASET SEARCH, AND
GOVERNMENT REPOSITORIES