Thakshashila: /* Common Techniques */

2025-06-05T04:25:27Z

Common Techniques

← Older revision		Revision as of 04:25, 5 June 2025
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	* A/B testing		* A/B testing
	* Natural Language Processing (NLP)		* Natural Language Processing (NLP)
	* Deep Learning (see [[Deep Learning]])		* [[Deep Learning]] (see [[Deep Learning]])

	== Applications ==		== Applications ==

Thakshashila: /* Data Modeling */

2025-06-05T04:24:14Z

Data Modeling

← Older revision		Revision as of 04:24, 5 June 2025
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	=== Data Modeling ===		=== Data Modeling ===
	Applying algorithms, particularly from [[Machine Learning]], to make predictions, classifications, or decisions.		Applying algorithms, particularly from [[What is Machine Learning\|Machine Learning]], to make predictions, classifications, or decisions.

	=== Interpretation and Communication ===		=== Interpretation and Communication ===

Thakshashila: Created page with "= Data Science = '''Data Science''' is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It integrates techniques from statistics, computer science, and domain-specific knowledge to turn raw data into actionable intelligence. == Overview == Data Science combines aspects of data analysis, machine learning, data engineering, and software development to address complex..."

2025-06-05T04:21:43Z

Created page with "= Data Science = '''Data Science''' is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It integrates techniques from statistics, computer science, and domain-specific knowledge to turn raw data into actionable intelligence. == Overview == Data Science combines aspects of data analysis, machine learning, data engineering, and software development to address complex..."

New page

= Data Science =

'''Data Science''' is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It integrates techniques from statistics, computer science, and domain-specific knowledge to turn raw data into actionable intelligence.

== Overview ==
Data Science combines aspects of data analysis, machine learning, data engineering, and software development to address complex problems. It is widely used in business, healthcare, government, and scientific research to make data-driven decisions.

== History ==
The term "Data Science" gained popularity in the early 2000s, although the use of data in analysis and decision-making has a much longer history. It evolved from traditional statistics and data analysis into a broader discipline with the advent of big data and increased computational power.

== Key Components ==

=== Data Collection ===
Gathering raw data from various sources, including databases, APIs, sensors, web scraping, and user logs.

=== Data Cleaning and Preprocessing ===
Preparing data by handling missing values, removing duplicates, correcting errors, and formatting for analysis.

=== Exploratory Data Analysis (EDA) ===
Using statistical summaries and visualization techniques to understand patterns, trends, and anomalies in the data.

=== Data Modeling ===
Applying algorithms, particularly from [[Machine Learning]], to make predictions, classifications, or decisions.

=== Interpretation and Communication ===
Conveying insights through reports, dashboards, and visualizations to stakeholders.

== Tools and Technologies ==
Data Scientists use a variety of tools, including:

* '''Programming Languages''': Python, R, SQL
* '''Libraries/Frameworks''': Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch
* '''Visualization Tools''': Matplotlib, Seaborn, Tableau, Power BI
* '''Big Data Technologies''': Hadoop, Spark
* '''Databases''': MySQL, PostgreSQL, MongoDB

== Common Techniques ==

* Descriptive statistics
* Predictive modeling
* Classification and regression
* Clustering
* Dimensionality reduction
* A/B testing
* Natural Language Processing (NLP)
* Deep Learning (see [[Deep Learning]])

== Applications ==

* Business intelligence and analytics
* Fraud detection
* Recommendation systems
* Healthcare diagnostics
* Market research and customer segmentation
* Scientific research and simulations

== Relationship to Other Fields ==

* [[Artificial Intelligence]]: Data Science provides the data and analysis used to train AI systems.
* [[Machine Learning]]: A subset of AI and a major part of Data Science used to make predictions and uncover patterns.
* [[Big Data]]: Refers to the large and complex data sets analyzed using Data Science methods.
* [[Statistics]]: Provides the theoretical foundation for Data Science techniques.

== Role of a Data Scientist ==
A Data Scientist is responsible for:

* Understanding the business problem
* Designing data-driven solutions
* Collecting and cleaning data
* Building models and validating results
* Communicating findings effectively

== Challenges ==

* Data privacy and ethical considerations
* Data quality and consistency
* Model bias and fairness
* Communicating complex results to non-technical audiences

== See Also ==
* [[Machine Learning]]
* [[Deep Learning]]
* [[Artificial Intelligence]]
* [[Big Data]]
* [[Statistics]]
* [[Data Mining]]
* [[Data Visualization]]

== References ==
<references />

[[Category:Data Science]]
[[Category:Machine Learning]]
[[Category:Artificial Intelligence]]
[[Category:Computer Science]]

Data Science - Revision history

Thakshashila: /* Common Techniques */

Thakshashila: /* Data Modeling */