Revision as of 08:00, 6 July 2025

Data Analysis

Data analysis is the process of systematically applying statistical and logical techniques to describe, summarize, and compare data. It involves collecting, cleaning, and transforming data to extract valuable insights and inform decision-making. As an essential component of data science, data analysis serves a variety of fields, including economics, social science, business, and healthcare.

Introduction

Data analysis helps organizations derive meaningful conclusions from their data sets, addressing the complexities of raw data and converting them into actionable information. With the proliferation of digital technology and the exponential growth of data, data analysis has become a critical skill and function within many industries. Professionals engaged in data analysis must be proficient in statistics, mathematics, and computer science, as well as exhibit analytical thinking and problem-solving abilities.

History

Early Developments

The history of data analysis can be traced back to the origins of statistics in the 18th century. Pioneers like John Graunt and Pierre-Simon Laplace laid the foundation for statistical analysis through population studies and probability theory. In the 19th century, figures such as Florence Nightingale and Karl Pearson promoted statistical methods for public health and research, respectively.

The Rise of Computational Analysis

With the advent of computers in the mid-20th century, the ability to handle and analyze large data sets underwent a transformation. The development of programming languages, such as R and Python, along with databases like SQL, enabled the automation and efficiency of analytical processes. The shift towards quantitative analysis in various fields paved the way for the emergence of techniques like machine learning and big data analytics in the 21st century.

Data Analysis in the Digital Age

In recent years, the term "data-driven decision-making" has gained traction, indicating a growing recognition of the role data analysis plays in strategic planning. The rise of cloud computing, artificial intelligence, and advanced statistical methodologies has made data analysis more accessible than ever, leading to new opportunities and challenges for businesses and researchers.

Methodologies

Data analysis encompasses a variety of methodologies, which can be broadly classified into:

Descriptive Analysis

Descriptive analysis involves summarizing and interpreting data sets through statistics, charts, and graphs. Techniques include measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and data visualization methods. This type of analysis provides a clear view of trends and patterns within the data.

Inferential Analysis

Inferential analysis utilizes a sample from a population to make predictions and generalizations about that population. Key methods include hypothesis testing, confidence intervals, and regression analysis. Inferential statistics enable analysts to ascertain the significance of their findings and evaluate potential relationships among variables.

Predictive Analysis

Leaning heavily on machine learning algorithms, predictive analysis aims to forecast future outcomes based on historical data. Techniques such as decision trees, neural networks, and time series analysis are prevalent in this area. Predictive analytics is widely used in sectors such as finance for risk assessment and in marketing for consumer behavior analysis.

Prescriptive Analysis

Prescriptive analysis goes a step further by providing recommendations based on the outcomes of predictive analysis. This methodology employs optimization techniques and simulation models to guide decision-making processes. Businesses often use prescriptive analytics to enhance operational efficiency and resource allocation.

Exploratory Data Analysis (EDA)

Exploratory data analysis focuses on exploring data sets to uncover patterns, anomalies, and relationships without preconceived hypotheses. EDA employs visual methods such as scatter plots, box plots, and correlation matrices. It is an essential step in data analysis that facilitates cleaner modeling and processing.

Tools and Technologies

Data analysis relies on a range of tools and technologies which enhance the efficiency and effectiveness of the analysis process.

Programming Languages

R: A programming language and software environment specifically designed for statistical computing and graphics. R is widely used among statisticians and data scientists, offering numerous packages for data manipulation and analysis.
Python: Known for its versatility, Python includes libraries such as Pandas and NumPy that specialize in data analysis and manipulation. Python is increasingly favored due to its readability and extensive community support.
SQL: A domain-specific language used for managing and querying relational databases. SQL facilitates data extraction and basic analytical functions within databases.

Data Visualization Tools

Tableau: A powerful data visualization tool that enables users to create interactive and shareable dashboards. Tableau is popular among business intelligence professionals for its user-friendly interface.
Power BI: Developed by Microsoft, Power BI is a business analytics solution that provides interactive visualizations and real-time dashboards.
Matplotlib and Seaborn: Libraries within Python used for data visualization, allowing users to create static, animated, and interactive visualizations in Python.

Big Data Technologies

With the growth of big data, new technologies have emerged:

Apache Hadoop: An open-source framework that enables distributed storage and processing of large data sets across clusters of computers.
Apache Spark: A unified analytics engine that supports various data processing tasks, known for its speed and ease of use.
NoSQL databases: Such as MongoDB and Cassandra, which allow organizations to handle large volumes of unstructured or semi-structured data efficiently.

Real-world Applications

Data analysis has profound implications across various sectors:

Business and Marketing

In business, data analysis informs marketing strategies, customer segmentation, and sales forecasting. Firms leverage customer data to understand buying behaviors and preferences, leading to targeted marketing campaigns. Predictive analytics has become pivotal for pricing strategies and inventory management, enhancing profitability and efficiency.

Healthcare

In healthcare, data analysis significantly influences patient care and operational management. Analysts utilize health data to identify treatment outcomes, reduce hospital readmissions, and optimize resource allocation. Additionally, during public health crises, such as the COVID-19 pandemic, data analysis has played an essential role in tracking infection rates and modeling the spread of disease.

Finance

The finance sector employs data analysis for credit scoring, fraud detection, and investment decision-making. Financial analysts use predictive models to assess risks and market trends. Furthermore, algorithmic trading relies heavily on data analysis techniques to execute trades at optimal times based on market conditions.

Sports

In sports, data analysis is utilized for player performance evaluations, game strategy development, and injury prevention. Organizations apply statistical analysis to optimize training regimens and improve team dynamics. The use of advanced data analytics, including player tracking and machine learning, has transformed talent scouting and game strategies.

Criticism and Controversies

While data analysis is a powerful tool, it has faced criticism across several dimensions:

Misinterpretation of Data

One of the primary concerns regarding data analysis is the potential for misinterpretation. Data presented without context can lead to misleading conclusions, particularly when statistical methods are incorrectly applied. This risk is exacerbated by a lack of understanding of statistical principles among decision-makers.

Data Privacy Concerns

The nature of data collection raises significant privacy issues, particularly with the rise of personal data analytics. The ethical dimensions of collecting, storing, and analyzing personal information have prompted discussions around data protection laws and regulations such as GDPR. The challenge remains to balance data analytics and privacy rights.

Algorithmic Bias

Data analysis and machine learning algorithms can inadvertently perpetuate biases present in training data, leading to discriminatory outcomes. This phenomenon has sparked discussions regarding the ethical responsibility of data scientists and the need for transparency in algorithmic decision-making processes.

Influence and Impact

The influence of data analysis extends beyond individual organizations; it impacts entire industries and societies.

Decision-Making

Data analysis has revolutionized decision-making processes, moving from intuition-based approaches to data-driven strategies. The accessibility of analytical tools has democratized data analysis, empowering a wider range of professionals to leverage data insights in their decision-making.

Economic Growth

Industries leveraging data analysis often experience increased efficiency and innovation, contributing to economic growth. Improved business performance through data-driven insights results in increased competitiveness and the creation of new job roles within analytics and data science.

Societal Change

Data analysis plays a critical role in shaping social policies and addressing societal issues by providing insights into pressing matters such as inequality, poverty, and public health. The ability to harness data for social good has the potential to inform policy decisions and foster positive social change.

References

@@ Line 1: / Line 1: @@
-== Data Analysis ==
+= Data Analysis =
-Data analysis is the process of systematically applying statistical and logical techniques to describe, summarize, and compare data. This field is crucial in transforming raw data into meaningful information, enabling organizations and individuals to make informed decisions. As the volume of data generated continues to grow exponentially, the importance of effective data analysis has become increasingly evident across various domains including business, healthcare, science, and social sciences.
+Data analysis is the process of systematically applying statistical and logical techniques to describe, summarize, and compare data. It involves collecting, cleaning, and transforming data to extract valuable insights and inform decision-making. As an essential component of data science, data analysis serves a variety of fields, including economics, social science, business, and healthcare.
-=== Introduction ===
+== Introduction ==
-In an era characterized by information overload, data analysis has emerged as a key discipline in harnessing the power of large datasets. It encompasses a range of methods and tools aimed at extracting insights, patterns, or trends from both structured and unstructured data. The ultimate goal of data analysis is to derive actionable information that can lead to improved decision-making processes. As industries continue to evolve, data analysis is pivotal in driving strategic planning and operational efficiency.
+Data analysis helps organizations derive meaningful conclusions from their data sets, addressing the complexities of raw data and converting them into actionable information. With the proliferation of digital technology and the exponential growth of data, data analysis has become a critical skill and function within many industries. Professionals engaged in data analysis must be proficient in statistics, mathematics, and computer science, as well as exhibit analytical thinking and problem-solving abilities.
-=== History ===
+== History ==
-The origins of data analysis can be traced back to the development of statistics in the 18th century. Early practitioners used rudimentary methods to infer population characteristics from survey data. With advancements in mathematics and statistical theory, the field began to mature in the 19th century with contributors like Karl Pearson, who introduced significant developments in correlation and regression analysis.
+=== Early Developments ===
-The post-World War II era marked a significant turning point with the increased use of computers, which considerably enhanced the speed and scale at which data could be processed. The introduction of software packages in the 1960s and 1970s, such as SPSS and SAS, revolutionized data analysis by making complex statistical methods more accessible to researchers and analysts.
+The history of data analysis can be traced back to the origins of statistics in the 18th century. Pioneers like John Graunt and Pierre-Simon Laplace laid the foundation for statistical analysis through population studies and probability theory. In the 19th century, figures such as Florence Nightingale and Karl Pearson promoted statistical methods for public health and research, respectively.
-In the 21st century, the advent of big data technologies and practices, such as data mining, machine learning, and artificial intelligence, has transformed the landscape of data analysis. The burgeoning availability of large datasets and powerful computational tools has allowed for more sophisticated analyses and predictive modeling.
+=== The Rise of Computational Analysis ===
-=== Types of Data Analysis ===
+With the advent of computers in the mid-20th century, the ability to handle and analyze large data sets underwent a transformation. The development of programming languages, such as R and Python, along with databases like SQL, enabled the automation and efficiency of analytical processes. The shift towards quantitative analysis in various fields paved the way for the emergence of techniques like machine learning and big data analytics in the 21st century.
-Data analysis can be primarily categorized into four types, each serving different purposes:
+=== Data Analysis in the Digital Age ===
-. '''Descriptive Analysis''' - This type of analysis aims to summarize and describe the main features of a dataset. Common techniques include means, medians, modes, frequencies, and standard deviations. Descriptive statistics provide a clear summary that can inform stakeholders about trends and patterns present in the data.
+In recent years, the term "data-driven decision-making" has gained traction, indicating a growing recognition of the role data analysis plays in strategic planning. The rise of cloud computing, artificial intelligence, and advanced statistical methodologies has made data analysis more accessible than ever, leading to new opportunities and challenges for businesses and researchers.
-. '''Inferential Analysis''' - Inferential analysis involves making predictions or generalizations about a population based on a sample. Techniques such as hypothesis testing, confidence intervals, and regression analysis fall under this category. This type of analysis allows analysts to draw conclusions and make forecasts about future data points.
+== Methodologies ==
-. '''Predictive Analysis''' - Predictive analysis focuses on using historical data to predict future outcomes. It employs statistical modeling and machine learning techniques to identify patterns and correlations within data. Predictive models are extensively used in various fields, including finance for credit scoring, marketing for customer behavior prediction, and healthcare for disease outcome forecasting.
+Data analysis encompasses a variety of methodologies, which can be broadly classified into:
-. '''Prescriptive Analysis''' - This analysis goes a step further by recommending actions based on data analysis. It uses algorithms and mathematical models to advise on potential solutions to complex problems. Prescriptive analysis is particularly useful in optimization problems where the best course of action needs to be determined.
+=== Descriptive Analysis ===
-=== Tools and Techniques ===
+Descriptive analysis involves summarizing and interpreting data sets through statistics, charts, and graphs. Techniques include measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and data visualization methods. This type of analysis provides a clear view of trends and patterns within the data.
-Data analysis employs a variety of tools and techniques designed to facilitate analysis and visualization of data. The choice of tools often depends on the complexity of the analysis, the size of the dataset, and the specific requirements of the analysis.
+=== Inferential Analysis ===
-. '''Statistical Software''' - Traditional statistical analysis is often conducted using software such as R, Python (with libraries such as Pandas, NumPy, and SciPy), SAS, and SPSS. These tools allow analysts to carry out complex calculations and data manipulation with ease.
+Inferential analysis utilizes a sample from a population to make predictions and generalizations about that population. Key methods include hypothesis testing, confidence intervals, and regression analysis. Inferential statistics enable analysts to ascertain the significance of their findings and evaluate potential relationships among variables.
-. '''Data Visualization Tools''' - Effective communication of data insights often necessitates the use of visualization tools. Software such as Tableau, QlikView, and Microsoft Power BI helps in creating interactive dashboards and visual representations of data, which can enhance understanding and facilitate decision-making.
+=== Predictive Analysis ===
-. '''Big Data Technologies''' - With the rise of big data, technologies such as Apache Hadoop, Apache Spark, and NoSQL databases have become integral in handling large volumes of unstructured data. These tools allow analysts to perform data processing and analysis on a scale previously unattainable.
+Leaning heavily on machine learning algorithms, predictive analysis aims to forecast future outcomes based on historical data. Techniques such as decision trees, neural networks, and time series analysis are prevalent in this area. Predictive analytics is widely used in sectors such as finance for risk assessment and in marketing for consumer behavior analysis.
-. '''Machine Learning Libraries''' - Machine learning has revolutionized data analysis by adding predictive capabilities. Libraries like TensorFlow, Scikit-learn, and Keras are utilized to build models that learn from data and improve over time, leading to more accurate predictions and deeper insights.
+=== Prescriptive Analysis ===
-=== Usage and Implementation ===
+Prescriptive analysis goes a step further by providing recommendations based on the outcomes of predictive analysis. This methodology employs optimization techniques and simulation models to guide decision-making processes. Businesses often use prescriptive analytics to enhance operational efficiency and resource allocation.
-Data analysis is broadly employed across many sectors, each leveraging data to meet unique operational or strategic objectives. The implementation of data analysis can be structured into several key steps:
+=== Exploratory Data Analysis (EDA) ===
-. '''Data Collection''' - The first step in data analysis involves gathering raw data from various sources such as surveys, databases, sensors, and transactions. Ensuring data quality and relevance is crucial at this stage.
+Exploratory data analysis focuses on exploring data sets to uncover patterns, anomalies, and relationships without preconceived hypotheses. EDA employs visual methods such as scatter plots, box plots, and correlation matrices. It is an essential step in data analysis that facilitates cleaner modeling and processing.
-. '''Data Cleaning''' - Raw data often contains inaccuracies, missing values, or inconsistencies. Data cleaning involves identifying and correcting these issues to ensure that the dataset is reliable for analysis.
+== Tools and Technologies ==
-. '''Data Exploration''' - Exploratory data analysis (EDA) is conducted to understand the underlying patterns and relationships within the data. Visualization techniques and summary statistics are commonly employed to uncover insights that guide further analysis.
+Data analysis relies on a range of tools and technologies which enhance the efficiency and effectiveness of the analysis process.
-. '''Statistical Analysis and Modeling''' - Analysts apply various statistical methods to examine the data and develop models that can elucidate relationships or predict future outcomes. The choice of methods depends on the nature of the data and the analysis objectives.
+=== Programming Languages ===
+* '''R''': A programming language and software environment specifically designed for statistical computing and graphics. R is widely used among statisticians and data scientists, offering numerous packages for data manipulation and analysis.
+* '''Python''': Known for its versatility, Python includes libraries such as Pandas and NumPy that specialize in data analysis and manipulation. Python is increasingly favored due to its readability and extensive community support.
+* '''SQL''': A domain-specific language used for managing and querying relational databases. SQL facilitates data extraction and basic analytical functions within databases.
-. '''Interpretation of Results''' - Once the analysis is complete, stakeholders must interpret the results. This often involves translating complex statistical findings into actionable insights that inform decision-making.
+=== Data Visualization Tools ===
+* '''Tableau''': A powerful data visualization tool that enables users to create interactive and shareable dashboards. Tableau is popular among business intelligence professionals for its user-friendly interface.
+* '''Power BI''': Developed by Microsoft, Power BI is a business analytics solution that provides interactive visualizations and real-time dashboards.
+* '''Matplotlib and Seaborn''': Libraries within Python used for data visualization, allowing users to create static, animated, and interactive visualizations in Python.
-. '''Reporting and Presentation''' - Communicating findings effectively is essential. Data analysts prepare comprehensive reports and visual presentations to relay insights to decision-makers in a manner that is clear and comprehensible.
+=== Big Data Technologies ===
-=== Real-world Examples ===
+With the growth of big data, new technologies have emerged:
+* '''Apache Hadoop''': An open-source framework that enables distributed storage and processing of large data sets across clusters of computers.
+* '''Apache Spark''': A unified analytics engine that supports various data processing tasks, known for its speed and ease of use.
+* '''NoSQL databases''': Such as MongoDB and Cassandra, which allow organizations to handle large volumes of unstructured or semi-structured data efficiently.
-The application of data analysis is ubiquitous, spanning various industries:
+== Real-world Applications ==
-. '''Healthcare''' - Data analysis plays a critical role in healthcare, from predicting patient outcomes to optimizing resource allocation. For instance, hospitals use predictive models to better manage patient flow and reduce wait times.
+Data analysis has profound implications across various sectors:
-. '''Finance''' - Financial institutions employ data analysis for risk management, fraud detection, and assessing creditworthiness. By analyzing transactional data, banks can identify suspicious activities and mitigate risks.
+=== Business and Marketing ===
-. '''Retail''' - Retailers analyze customer data to enhance marketing strategies and optimize inventory. Through customer segmentation and behavior analysis, businesses can tailor promotions and product offerings to maximize sales.
+In business, data analysis informs marketing strategies, customer segmentation, and sales forecasting. Firms leverage customer data to understand buying behaviors and preferences, leading to targeted marketing campaigns. Predictive analytics has become pivotal for pricing strategies and inventory management, enhancing profitability and efficiency.
-. '''Marketing''' - Targeted advertising campaigns rely heavily on data analysis to identify potential customers. Marketers analyze consumer behavior and preferences, allowing for personalized marketing efforts that increase conversion rates.
+=== Healthcare ===
-. '''Sports''' - Sports teams utilize data analysis to enhance player performance and game strategy. Through performance metrics and game statistics, analysts provide insights that inform coaching decisions and player acquisitions.
+In healthcare, data analysis significantly influences patient care and operational management. Analysts utilize health data to identify treatment outcomes, reduce hospital readmissions, and optimize resource allocation. Additionally, during public health crises, such as the COVID-19 pandemic, data analysis has played an essential role in tracking infection rates and modeling the spread of disease.
-=== Criticism and Controversies ===
+=== Finance ===
-While data analysis has vast applications, it is not without its criticisms and controversies:
+The finance sector employs data analysis for credit scoring, fraud detection, and investment decision-making. Financial analysts use predictive models to assess risks and market trends. Furthermore, algorithmic trading relies heavily on data analysis techniques to execute trades at optimal times based on market conditions.
-. '''Data Privacy''' - The collection and analysis of personal data raise significant concerns about privacy and security. Many individuals are wary of how their data is used, leading to calls for stricter regulations on data collection practices.
+=== Sports ===
-. '''Algorithmic Bias''' - There is a growing awareness of biases embedded in data analysis processes and algorithms. If the data used for analysis is biased, the insights and predictions generated could perpetuate systemic inequalities, particularly in sensitive areas such as criminal justice and hiring practices.
+In sports, data analysis is utilized for player performance evaluations, game strategy development, and injury prevention. Organizations apply statistical analysis to optimize training regimens and improve team dynamics. The use of advanced data analytics, including player tracking and machine learning, has transformed talent scouting and game strategies.
-. '''Misinterpretation of Data''' - Inadequate or misleading data interpretations can lead to poor decision-making. Without proper context or methodological transparency, data analyses can be misused or lead to erroneous conclusions.
+== Criticism and Controversies ==
-. '''Overreliance on Data''' - Organizations may be tempted to base decisions solely on data analysis, neglecting qualitative factors and human judgment. Effective decision-making should incorporate both quantitative data and qualitative insights.
+While data analysis is a powerful tool, it has faced criticism across several dimensions:
-=== Influence and Impact ===
+=== Misinterpretation of Data ===
-Data analysis has fundamentally transformed the way organizations operate and make decisions. Its impact extends beyond individual businesses to societal structures, influencing how governments, organizations, and communities respond to challenges. The increased capability to analyze vast amounts of data has led to:
+One of the primary concerns regarding data analysis is the potential for misinterpretation. Data presented without context can lead to misleading conclusions, particularly when statistical methods are incorrectly applied. This risk is exacerbated by a lack of understanding of statistical principles among decision-makers.
-. '''Enhanced Decision-Making''' - Organizations that leverage data analysis are often better equipped to make informed decisions, thereby improving efficiency and effectiveness in their operations.
+=== Data Privacy Concerns ===
-. '''Innovation''' - The insights gained from data analysis can drive innovation by identifying new market opportunities or optimizing existing processes. Companies that invest in data analysis are often at the forefront of technological advancements.
+The nature of data collection raises significant privacy issues, particularly with the rise of personal data analytics. The ethical dimensions of collecting, storing, and analyzing personal information have prompted discussions around data protection laws and regulations such as GDPR. The challenge remains to balance data analytics and privacy rights.
-. '''Policy Formulation''' - Governments utilize data analysis to craft policies based on empirical evidence, ensuring that decisions are rooted in real-world conditions and trends.
+=== Algorithmic Bias ===
-. '''Social Change''' - Data analysis has become a tool for social advocacy, allowing activists to highlight inequity and drive change by presenting data-supported arguments to promote social justice.
+Data analysis and machine learning algorithms can inadvertently perpetuate biases present in training data, leading to discriminatory outcomes. This phenomenon has sparked discussions regarding the ethical responsibility of data scientists and the need for transparency in algorithmic decision-making processes.
-=== See Also ===
+== Influence and Impact ==
-* [[Statistical Analysis]]
-* [[Machine Learning]]
+The influence of data analysis extends beyond individual organizations; it impacts entire industries and societies.
+=== Decision-Making ===
+Data analysis has revolutionized decision-making processes, moving from intuition-based approaches to data-driven strategies. The accessibility of analytical tools has democratized data analysis, empowering a wider range of professionals to leverage data insights in their decision-making.
+=== Economic Growth ===
+Industries leveraging data analysis often experience increased efficiency and innovation, contributing to economic growth. Improved business performance through data-driven insights results in increased competitiveness and the creation of new job roles within analytics and data science.
+=== Societal Change ===
+Data analysis plays a critical role in shaping social policies and addressing societal issues by providing insights into pressing matters such as inequality, poverty, and public health. The ability to harness data for social good has the potential to inform policy decisions and foster positive social change.
+== See Also ==
 * [[Big Data]]
-* [[Data Mining]]
+* [[Data Science]]
 * [[Business Intelligence]]
-* [[Predictive Modeling]]
+* [[Statistics]]
+* [[Machine Learning]]
-=== References ===
+== References ==
-* [https://www.sas.com/en_us/insights/analytics/what-is-data-mining.html SAS - What is Data Mining?]
+* [https://www.statista.com/statistics/464478/number-of-data-analysts-worldwide/ Statista: Number of Data Analysts Worldwide]
-* [https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/data-analysis/ Statistics How To - Data Analysis Defined]
+* [https://www.cio.com/article/3577737/big-data-analytics-what-it-is-and-how-to-use-it.html CIO: Big Data Analytics - What It Is and How to Use It]
-* [https://www.ibm.com/cloud/learn/data-analysis IBM - Data analysis and its applications]
+* [https://www.analyticsvidhya.com/blog/2021/02/data-analysis-101-a-comprehensive-guide/ Analytics Vidhya: Data Analysis 101 - A Comprehensive Guide]
-* [https://www.tableau.com/learn/articles/data-visualization-what-is Tableau - What is Data Visualization?]
+* [https://towardsdatascience.com/data-analysis-in-python-the-basics-199b827e1fd3 Towards Data Science: Data Analysis in Python - The Basics]
-* [https://www.datanami.com/2020/09/23/the-role-of-data-analysis-in-the-modern-business-environment/ Datanami - The Role of Data Analysis in the Modern Business Environment]
+* [https://www.forbes.com/sites/bernardmarr/2020/01/30/what-is-data-analysis/?sh=39be8cbb6a29 Forbes: What is Data Analysis?]
-* [https://www.forbes.com/sites/bernardmarr/2020/10/05/the-importance-of-data-analysis-in-business/?sh=59f6b2b63776 Forbes - The Importance of Data Analysis in Business]
-[[Category:Data science]]
 [[Category:Data analysis]]
 [[Category:Statistics]]
+[[Category:Data science]]