Join data mining with brief introduction to data science

Data Science
Overview
A concise introduction to Data Science concepts and
fundamentals.

Introduction
Data Science is an interdisciplinary field that uses
scientific methods and algorithms to extract knowledge
from structured and unstructured data.
It includes data collection, cleaning, analysis,
interpretation, and visualization to support data-driven
decision making.

Definition and Scope
Data Science involves the extraction of insights from data
using statistics, machine learning, and programming.
It encompasses data gathering, cleaning, analysis, and
visualization processes to solve complex problems and
support decisions.

Key
Components
and Processes
The core data science process includes: defining goals, retrieving data, data
preparation and cleansing, exploratory data analysis, model building, and
presenting results.
Each step is critical for reliable and actionable insights.

Importance and
Applications
Data science drives improved decision-making, enhances
efficiency, personalization, competitive advantage, risk
management, and innovation across sectors.
Applications range from healthcare, finance, retail,
manufacturing, to energy and telecommunications, impacting
diverse industries globally.

Data Science
Techniques and
Tools
02

Data Collection and
Cleaning
Data can be sourced from files, web scraping, APIs,
databases, and big data platforms.
Cleaning corrects errors, handles missing values,
removes duplicates, and standardizes data to ensure
accuracy.

Statistical
Analysis and
Machine Learning
Statistical techniques and machine learning models are used to analyze data, find patterns,
and make predictions.
Model building is iterative and includes model selection, execution, diagnostics, and
evaluation for optimal results.

Popular Tools and
Technologies
Popular tools include Python and R programming languages,
machine learning frameworks like TensorFlow, and
visualization tools such as Tableau and Power BI.
Big data ecosystems use technologies like Hadoop, Spark,
and NoSQL databases for storage and processing.

Key
Components
and Processes
The data science process involves problem definition, data collection, cleaning and
integration, exploratory analysis, model building, evaluation, and deployment.
Effective execution of each step produces accurate and actionable insights for strategic
decision-making.

Importance and
Applications
Data science contributes to improved decision making, operational
efficiencies, customer personalization, competitive advantages, risk
management, and innovation in many sectors.
Applications include healthcare, finance, retail, manufacturing,
transportation, energy, telecommunications, public services,
education, sports, and e-commerce.

Definition and
Scope
Data Science is the interdisciplinary study that uses scientific methods, algorithms, and
systems to extract knowledge from both structured and unstructured data.
It involves gathering, processing, analyzing, and interpreting data to enable informed and
data-driven decisions.

Key Components and
Processes
Data science entails defining clear research goals, retrieving
and preparing data, exploratory data analysis, model building,
evaluation, presentation, and automation of results.
This cycle ensures continuous improvement and actionable
insights for business applications.

Data Collection and
Cleaning
Data is gathered from diverse sources such as files,
APIs, databases, and web scraping, including big
data platforms.
Cleaning involves error correction, handling missing
data, removing duplicates, and standardizing
formats to ensure data quality.

Statistical
Analysis and
Machine Learning
Techniques include regression, classification, clustering, and predictive modeling to find
patterns and forecast trends.
Model building is iterative and requires diagnostics and validation to optimize performance
and reliability.

Popular Tools and
Technologies
Common tools include Python, R, TensorFlow, Scikit-
learn, Tableau, and Power BI.
Big data ecosystems leverage Hadoop, Spark, NoSQL
databases for scalable storage, processing, and querying
of large datasets.

Importance and
Applications
Data science enables improved decision-making by providing actionable insights and forecasting trends.
It enhances operational efficiency by automating processes and optimizing resources.
Personalization drives better customer experiences while competitive advantages arise from market insights.
Risk management and innovation are further key benefits across industries like healthcare, finance, retail, manufacturing, and more.

Data Science
Techniques and Tools
03

Data Collection and
Cleaning
Data can be collected from files, web scraping, APIs, databases, and big
data platforms.
Data cleansing involves correcting errors, handling missing data,
removing duplicates, and standardizing formats to ensure data quality
and consistency.
Common issues include data entry errors, redundant whitespace,
capitalization mismatches, impossible values, and outliers.

Statistical
Analysis and
Machine Learning
Statistical and machine learning techniques such as regression, classification, clustering,
and predictive modeling uncover patterns and trends.
Model building is iterative, involving model and variable selection, execution, diagnostics,
and validation to achieve accurate and reliable results.

Popular Tools and
Technologies
Python and R are widely used programming languages in data
science.
Machine learning frameworks include TensorFlow, PyTorch,
and Scikit-learn.
For visualization, tools such as Tableau and Power BI are
common.
Big data ecosystems utilize Hadoop, Spark, and NoSQL
databases for data storage and processing.

Conclusion
s
Data science is a multidisciplinary field critical for extracting meaningful insights from vast and varied data.
Its techniques and tools span data collection, cleaning, analysis, modeling, and visualization to solve complex problems.
Applications across industries demonstrate its transformative impact on decision-making, efficiency, personalization, and innovation.
Ongoing advancements in tools and methodologies continue to expand data science’s capabilities and adoption.

CREDITS: This presentation template was created by Slidesgo, and
includes icons by Flaticon, and infographics & images by Freepik
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Join data mining with brief introduction to data science

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