Data Science Fundamentals and Practices.pptx

Data Science Fundamentals: Data
Collection, Cleaning, and
Visualization
An Introduction to Key Concepts

Agenda
• - Overview of Topics
• - Data Collection
• - Data Cleaning
• - Data Visualization
• - Practical Exercises
• - Q&A

Agenda (Continued)
• - In-depth exploration of each topic
• - Hands-on exercises to solidify learning
• - Opportunity to ask questions at the end

Introduction to Data Science
• - Data Science is the interdisciplinary field that
uses scientific methods, processes, algorithms,
and systems to extract knowledge and insights
from structured and unstructured data.
• - It involves data collection, cleaning, analysis,
and visualization.

Importance of Data Collection
• - Data Collection is the foundation of Data
Science.
• - Without accurate and relevant data, all
subsequent analyses and visualizations are
meaningless.

Importance of Data Cleaning and
Visualization
• - Data Cleaning ensures the data's quality and
consistency, making it ready for analysis.
• - Data Visualization transforms data into a
visual context, such as a graph or map, to
make data easier to understand.

Data Collection Overview
• - Data Collection is the process of gathering
and measuring information on variables of
interest.
• - It is a critical step in data science, setting the
stage for data analysis.

Types of Data: Structured vs.
Unstructured
• - Structured Data: Organized in a fixed format
(e.g., databases, spreadsheets).
• - Unstructured Data: Not organized in a
predefined manner (e.g., text files, images).

Types of Data: Qualitative vs.
Quantitative
• - Qualitative Data: Descriptive and conceptual
(e.g., interviews, surveys).
• - Quantitative Data: Numeric and can be
measured (e.g., statistics, counts).

Sources of Data: Databases
• - Centralized collections of structured data,
easily queryable using SQL.

Sources of Data: APIs
• - Application Programming Interfaces (APIs)
allow for automated data retrieval from online
services.

Sources of Data: Web Scraping and
Sensors
• - Web Scraping: Extracting data from websites
using automated scripts.
• - Sensors and IoT: Collecting data from
physical devices like temperature sensors,
smart devices.

Tools and Techniques for Data
Collection: Python Libraries
• - requests: For making HTTP requests to fetch
data from the web.
• - BeautifulSoup: For parsing HTML and XML
documents.
• - pandas: For data manipulation and analysis.

Using APIs for Data Collection
• - APIs provide a way to access large amounts
of data in a structured and efficient manner.
• - Example: Fetching weather data from an API.

Brief Demo/Example of Data
Collection
• - Demonstrate a simple API call or web
scraping example using Python.

Why Data Cleaning is Essential
• - Ensures data quality, making it ready for
analysis.
• - Increases accuracy, consistency, and
reliability of the data.

Overview of Common Data Issues
• - Missing Data: Missing values in the dataset.
• - Duplicates: Repeated entries in the dataset.
• - Inconsistencies: Irregular data formats or
misaligned data.

Importance of Data Cleaning
• - Poor quality data can lead to incorrect
conclusions.
• - Cleaning helps in transforming raw data into
a usable format.

Data Cleaning Techniques
Introduction
• - Introduction to techniques such as handling
missing values, removing duplicates, and
correcting inconsistencies.

Handling Missing Values
• - Methods: Imputation, Removal, or
Substitution.

Removing Duplicates
• - Identifying and eliminating duplicate records
to maintain data integrity.

Correcting Inconsistencies
• - Standardizing data formats and correcting
any inconsistencies in data entry.

Standardizing Data Formats
• - Ensuring all data follows a consistent format,
e.g., date formats, string cases.

Hands-On Data Cleaning Practical
Example
• - Open a sample dataset in Excel.
• - Identify issues such as missing values,
duplicates, and inconsistent formats.

Step-by-Step Walk-Through
• - Step 1: Handling missing data.
• - Step 2: Removing duplicates.
• - Step 3: Standardizing formats.

Cleaning Data in Excel
• - Practical demo or screenshots showing how
to clean data in Excel.

Final Cleaned Dataset
• - Compare before and after cleaning.
• - Highlight the improvements and ready-to-
analyze data.

Introduction to Data Visualization
• - Helps in understanding complex data.
• - Makes patterns and trends more apparent.

Benefits of Data Visualization
• - Easier communication of insights.
• - Supports data-driven decision-making.

Visualization Overview
• - Visualization is key to conveying findings in
an understandable way.

The Need for Effective
Visualizations
• - Poor visualizations can mislead; effective
ones clarify and inform.

Types of Data Visualizations: Bar
Charts and Histograms
• - Bar Charts: Used for comparing categories.
• - Histograms: Used for showing distributions
of data.

Types of Data Visualizations: Pie
Charts and Scatter Plots
• - Pie Charts: Represent parts of a whole.
• - Scatter Plots: Show relationships between
two variables.

Tools for Data Visualization:
Excel/Google Sheets
• - Built-in charting tools for quick visualizations.

Python Libraries for Visualization
• - matplotlib: Basic plotting library.
• - seaborn: Statistical data visualization.
• - plotly: Interactive visualizations.

Step-by-Step Guide to Creating
Visualizations
• - Excel/Google Sheets: Simple chart creation.
• - Python: Example code for creating a bar
chart or scatter plot.

Using Python for Visualization
• - Code examples showing how to create
different visualizations.

Visualization of a Sample Dataset
• - Example: Create a bar chart from a dataset.
• - Walkthrough of the process and
interpretation of the results.

Practical Exercise: Instructions
• - Collect a small dataset.
• - Clean the data using techniques covered.
• - Create at least two visualizations.

Time Allocation
• - Allocate 30 minutes for the exercise.
• - Encourage presenting findings after the
exercise.

Q&A
• - Open the floor for any questions.
• - Clarify any doubts related to the lecture
content.

Summary: Recap of Key Concepts
• - Data Collection: Fundamental to acquiring
relevant data for analysis.
• - Data Cleaning: Ensures data quality and
consistency for reliable analysis.
• - Data Visualization: Critical for interpreting
and communicating data insights.

Summary: Data Collection
• - Importance of collecting accurate and
relevant data.

Summary: Data Cleaning
• - The role of data cleaning in ensuring data
integrity.

Summary: Data Visualization
• - Effective visualizations enhance
understanding of data.

Closing Slide
• - Thank you for your participation and
attention.

Data Science Fundamentals and Practices.pptx

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