Data Analysis: Difference between revisions

Latest revision as of 09:47, 6 July 2025

Data Analysis is the process of systematically applying statistical and logical techniques to describe, summarize, and compare data. It involves the use of various tools and methods to interpret raw data in order to derive meaningful information that can guide decision-making. Data analysis plays a crucial role in various fields such as business, science, social science, and healthcare, facilitating the understanding of interests and behaviors based on collected information.

Historical Background

Data analysis can be traced back to the early days of statistics and informatics. The development of data analysis as a formal discipline began in the 18th century with pioneers such as John Graunt and Pierre-Simon Laplace, who laid the groundwork for statistical methods. These early efforts were primarily focused on demographic data and probability, which are still fundamental to modern data analysis.

Evolution through Technologies

The advancement of technology has played a significant role in the evolution of data analysis. The invention of the personal computer in the late 20th century democratized access to data analysis tools, enabling more individuals and organizations to analyze data. Software such as Excel and statistical packages such as SPSS and SAS revolutionized the way analysts worked with data, allowing them to perform complex analyses more efficiently.

The Rise of Big Data

The emergence of the internet and the proliferation of data generation have ushered in the era of big data. This phenomenon has transformed data analysis from traditional methods into complex processes involving large volumes of diverse data sets. With the advent of big data, newer technologies such as Hadoop and Spark have been developed to manage and analyze this massive inflow of data efficiently.

Types of Data Analysis

There are various types of data analysis, each with its own methodologies and applications. Understanding the distinctions between these types is crucial for selecting the appropriate approach for a given context.

Descriptive Analysis

Descriptive analysis refers to the techniques used to summarize and describe the main features of a dataset. It often involves the calculation of basic statistical measures such as the mean, median, and mode. Graphical representations like bar charts, histograms, and pie charts are also common in descriptive analysis, serving to provide a visual summary of the data.

Inferential Analysis

Inferential analysis goes beyond mere description by making inferences or generalizations about a population based on sample data. It involves hypothesis testing, estimation of population parameters, and confidence intervals. In this approach, analysts apply statistical tests such as t-tests, chi-squared tests, and ANOVA to evaluate the relationships and differences among variables.

Predictive Analysis

Predictive analysis involves using historical data and statistical algorithms to forecast future outcomes. Techniques such as regression analysis, time series analysis, and machine learning models fall under predictive analysis. This type of analysis is widely used in business contexts, such as sales forecasting, risk assessment, and customer behavior analysis.

Prescriptive Analysis

Prescriptive analysis is the most advanced form, focusing not only on predicting future outcomes but also recommending actions to achieve desired results. It often relies on optimization algorithms and simulation techniques. Prescriptive analysis is utilized in various fields, including operations research, healthcare, and supply chain management, to enhance decision-making processes.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or reasons behind certain trends or outcomes. It typically involves comparing current data with historical data to identify patterns and correlations. These analyses often utilize techniques such as root cause analysis, which seeks to pinpoint the factors leading to a specific outcome, enabling businesses to address issues proactively.

Tools and Techniques

The tools and techniques used in data analysis have evolved dramatically over time, driven by technological advancements and increased data complexity.

Statistical Tools

Statistical tools are foundational to data analysis. These include software applications such as R and Python, which are widely used for their extensive libraries and frameworks that support data manipulation and statistical modeling. Additionally, proprietary software like Minitab and Stata also provide robust statistical analysis capabilities.

Data Visualization Tools

Effective visualization is critical in data analysis, as it translates complex data findings into understandable formats. Tools such as Tableau, Power BI, and Google Data Studio are popular for creating interactive visualizations and dashboards. These tools enable analysts to convey insights through eye-catching graphics, making it easier for stakeholders to grasp key findings.

Machine Learning and AI

The incorporation of machine learning and artificial intelligence into data analysis represents a significant leap forward. Various machine learning algorithms, such as decision trees, neural networks, and clustering techniques, are used to find patterns and make predictions based on historical data. This integration allows analysts to handle larger datasets and uncover insights that would be difficult to detect through traditional methods.

Implementation and Applications

Data analysis finds application across a wide array of industries, providing valuable insights that drive progress and innovation.

Business Intelligence

In the context of business, data analysis is instrumental in the practice of business intelligence (BI). Companies leverage data analysis to assess performance, identify market trends, and understand customer preferences. The insights derived from data analysis are vital in guiding strategic decisions, optimizing marketing campaigns, and improving customer relationships.

Healthcare Analytics

Healthcare analytics employs data analysis to enhance patient outcomes and streamline healthcare services. Analysts examine patient data, treatment effectiveness, and operational metrics to identify areas for improvement. Predictive analytics, for example, is immensely beneficial in predicting patient admissions and disease outbreaks, allowing healthcare providers to allocate resources effectively.

Social Science Research

In the realm of social sciences, data analysis is crucial for understanding societal trends and behaviors. Researchers employ various analytical techniques to study phenomena such as voting patterns, economic models, and demographic changes. The analysis of survey data, observational data, and experiments facilitates evidence-based conclusions regarding social issues.

Sports Analytics

Sports analytics has emerged as a vital element in modern sports management and performance evaluation. Teams and organizations use data analysis to assess player performance, devise game strategies, and enhance fan engagement. Techniques such as player tracking and performance metrics enable teams to make data-driven decisions that impact their competitive edge.

Marketing Analytics

Marketing analytics involves the use of data analysis to optimize marketing strategies and assess campaign effectiveness. By analyzing customer data, web traffic, social media engagement, and sales figures, analysts can measure the return on investment (ROI) of marketing activities and tailor their approaches to better resonate with target audiences.

Challenges and Limitations

Despite its vast potential, data analysis faces several challenges and limitations that can hinder its effectiveness.

Data Quality Issues

Data quality is paramount in data analysis; poor-quality data can lead to erroneous conclusions. Issues such as missing values, inaccurate records, and inconsistent formats can compromise the integrity of the analysis. Organizations must implement strict data governance practices to ensure data accuracy, completeness, and consistency before analysis.

Complexity of Analysis

The complexity of modern data analysis techniques can also pose challenges. More advanced data analysis methodologies, such as machine learning and predictive modeling, require specialized knowledge and skills that may not be available in all organizations. As a result, companies may find it challenging to implement and interpret complex analyses without adequate expertise.

Overfitting and Underfitting

In predictive modeling, issues of overfitting and underfitting can arise. Overfitting occurs when a model is excessively complex, capturing noise instead of the underlying data trend, while underfitting happens when a model is too simplistic to capture the data patterns efficiently. Both issues can lead to poor predictive performance.

Ethical Considerations

The ethical dimensions of data analysis also warrant careful consideration. Concerns regarding data privacy, consent, and the potential for discrimination based on data-driven decisions must be addressed. Organizations must adhere to ethical guidelines and legal frameworks when conducting data analysis to protect individuals' rights and promote transparency.

Future Trends

The future of data analysis is poised for exciting developments driven by advancements in technology and shifts in data usage patterns.

Integration of AI and Automation

As artificial intelligence and automation technologies continue to evolve, data analysis will increasingly incorporate these capabilities to enhance analytical accuracy and efficiency. Automation in data cleaning, processing, and visualization will enable analysts to focus on interpretation and decision-making rather than repetitive tasks.

Enhanced Real-time Analytics

The demand for real-time analytics is growing as organizations seek timely insights for rapid decision-making. The ability to analyze data in real-time enables businesses to respond quickly to market changes, customer needs, and emerging opportunities. Technologies such as stream processing are set to play a pivotal role in facilitating real-time data analysis.

Greater Focus on Data Literacy

As data becomes an integral part of business strategy, the emphasis on data literacy is anticipated to increase. Organizations will invest in training programs to enhance employees' abilities to interpret and analyze data. A workforce skilled in data literacy will be better equipped to make informed decisions based on evidence.

Ethical Frameworks and Governance

The need for ethical frameworks and data governance will become increasingly important as data analysis continues to expand and influence diverse areas. Organizations are expected to establish clearer policies concerning data collection, usage, and sharing to ensure ethical practices and compliance with regulations.

References

@@ Line 1: / Line 1: @@
-= Data Analysis =
+'''Data Analysis''' is the process of systematically applying statistical and logical techniques to describe, summarize, and compare data. It involves the use of various tools and methods to interpret raw data in order to derive meaningful information that can guide decision-making. Data analysis plays a crucial role in various fields such as business, science, social science, and healthcare, facilitating the understanding of interests and behaviors based on collected information.
-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.
+== Historical Background ==
-== Introduction ==
+Data analysis can be traced back to the early days of statistics and informatics. The development of data analysis as a formal discipline began in the 18th century with pioneers such as John Graunt and Pierre-Simon Laplace, who laid the groundwork for statistical methods. These early efforts were primarily focused on demographic data and probability, which are still fundamental to modern data analysis.
-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.
+=== Evolution through Technologies ===
-== History ==
+The advancement of technology has played a significant role in the evolution of data analysis. The invention of the personal computer in the late 20th century democratized access to data analysis tools, enabling more individuals and organizations to analyze data. Software such as Excel and statistical packages such as SPSS and SAS revolutionized the way analysts worked with data, allowing them to perform complex analyses more efficiently.
-=== Early Developments ===
+=== The Rise of Big Data ===
-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 emergence of the internet and the proliferation of data generation have ushered in the era of big data. This phenomenon has transformed data analysis from traditional methods into complex processes involving large volumes of diverse data sets. With the advent of big data, newer technologies such as Hadoop and Spark have been developed to manage and analyze this massive inflow of data efficiently.
-=== 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.
+There are various types of data analysis, each with its own methodologies and applications. Understanding the distinctions between these types is crucial for selecting the appropriate approach for a given context.
-=== 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.
+Descriptive analysis refers to the techniques used to summarize and describe the main features of a dataset. It often involves the calculation of basic statistical measures such as the mean, median, and mode. Graphical representations like bar charts, histograms, and pie charts are also common in descriptive analysis, serving to provide a visual summary of 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.
+Inferential analysis goes beyond mere description by making inferences or generalizations about a population based on sample data. It involves hypothesis testing, estimation of population parameters, and confidence intervals. In this approach, analysts apply statistical tests such as t-tests, chi-squared tests, and ANOVA to evaluate the relationships and differences 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.
+Predictive analysis involves using historical data and statistical algorithms to forecast future outcomes. Techniques such as regression analysis, time series analysis, and machine learning models fall under predictive analysis. This type of analysis is widely used in business contexts, such as sales forecasting, risk assessment, and customer 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.
+Prescriptive analysis is the most advanced form, focusing not only on predicting future outcomes but also recommending actions to achieve desired results. It often relies on optimization algorithms and simulation techniques. Prescriptive analysis is utilized in various fields, including operations research, healthcare, and supply chain management, to enhance decision-making processes.
-=== Exploratory Data Analysis (EDA) ===
+=== Diagnostic Analysis ===
-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.
+Diagnostic analysis aims to understand the causes or reasons behind certain trends or outcomes. It typically involves comparing current data with historical data to identify patterns and correlations. These analyses often utilize techniques such as root cause analysis, which seeks to pinpoint the factors leading to a specific outcome, enabling businesses to address issues proactively.
-== Tools and Technologies ==
+== Tools and Techniques ==
-Data analysis relies on a range of tools and technologies which enhance the efficiency and effectiveness of the analysis process.
+The tools and techniques used in data analysis have evolved dramatically over time, driven by technological advancements and increased data complexity.
-=== Programming Languages ===
+=== Statistical Tools ===
-* '''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.
+Statistical tools are foundational to data analysis. These include software applications such as R and Python, which are widely used for their extensive libraries and frameworks that support data manipulation and statistical modeling. Additionally, proprietary software like Minitab and Stata also provide robust statistical analysis capabilities.
-* '''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 ===
+Effective visualization is critical in data analysis, as it translates complex data findings into understandable formats. Tools such as Tableau, Power BI, and Google Data Studio are popular for creating interactive visualizations and dashboards. These tools enable analysts to convey insights through eye-catching graphics, making it easier for stakeholders to grasp key findings.
+=== Machine Learning and AI ===
+The incorporation of machine learning and artificial intelligence into data analysis represents a significant leap forward. Various machine learning algorithms, such as decision trees, neural networks, and clustering techniques, are used to find patterns and make predictions based on historical data. This integration allows analysts to handle larger datasets and uncover insights that would be difficult to detect through traditional methods.
+== Implementation and Applications ==
+Data analysis finds application across a wide array of industries, providing valuable insights that drive progress and innovation.
+=== Business Intelligence ===
-With the growth of big data, new technologies have emerged:
+In the context of business, data analysis is instrumental in the practice of business intelligence (BI). Companies leverage data analysis to assess performance, identify market trends, and understand customer preferences. The insights derived from data analysis are vital in guiding strategic decisions, optimizing marketing campaigns, and improving customer relationships.
-* '''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 ==
+=== Healthcare Analytics ===
-Data analysis has profound implications across various sectors:
+Healthcare analytics employs data analysis to enhance patient outcomes and streamline healthcare services. Analysts examine patient data, treatment effectiveness, and operational metrics to identify areas for improvement. Predictive analytics, for example, is immensely beneficial in predicting patient admissions and disease outbreaks, allowing healthcare providers to allocate resources effectively.
-=== Business and Marketing ===
+=== Social Science Research ===
-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.
+In the realm of social sciences, data analysis is crucial for understanding societal trends and behaviors. Researchers employ various analytical techniques to study phenomena such as voting patterns, economic models, and demographic changes. The analysis of survey data, observational data, and experiments facilitates evidence-based conclusions regarding social issues.
-=== Healthcare ===
+=== Sports Analytics ===
-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.
+Sports analytics has emerged as a vital element in modern sports management and performance evaluation. Teams and organizations use data analysis to assess player performance, devise game strategies, and enhance fan engagement. Techniques such as player tracking and performance metrics enable teams to make data-driven decisions that impact their competitive edge.
-=== Finance ===
+=== Marketing Analytics ===
-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.
+Marketing analytics involves the use of data analysis to optimize marketing strategies and assess campaign effectiveness. By analyzing customer data, web traffic, social media engagement, and sales figures, analysts can measure the return on investment (ROI) of marketing activities and tailor their approaches to better resonate with target audiences.
-=== Sports ===
+== Challenges and Limitations ==
-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.
+Despite its vast potential, data analysis faces several challenges and limitations that can hinder its effectiveness.
-== Criticism and Controversies ==
+=== Data Quality Issues ===
-While data analysis is a powerful tool, it has faced criticism across several dimensions:
+Data quality is paramount in data analysis; poor-quality data can lead to erroneous conclusions. Issues such as missing values, inaccurate records, and inconsistent formats can compromise the integrity of the analysis. Organizations must implement strict data governance practices to ensure data accuracy, completeness, and consistency before analysis.
-=== Misinterpretation of Data ===
+=== Complexity of Analysis ===
-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.
+The complexity of modern data analysis techniques can also pose challenges. More advanced data analysis methodologies, such as machine learning and predictive modeling, require specialized knowledge and skills that may not be available in all organizations. As a result, companies may find it challenging to implement and interpret complex analyses without adequate expertise.
-=== Data Privacy Concerns ===
+=== Overfitting and Underfitting ===
-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.
+In predictive modeling, issues of overfitting and underfitting can arise. Overfitting occurs when a model is excessively complex, capturing noise instead of the underlying data trend, while underfitting happens when a model is too simplistic to capture the data patterns efficiently. Both issues can lead to poor predictive performance.
-=== Algorithmic Bias ===
+=== Ethical Considerations ===
-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.
+The ethical dimensions of data analysis also warrant careful consideration. Concerns regarding data privacy, consent, and the potential for discrimination based on data-driven decisions must be addressed. Organizations must adhere to ethical guidelines and legal frameworks when conducting data analysis to protect individuals' rights and promote transparency.
-== Influence and Impact ==
+== Future Trends ==
-The influence of data analysis extends beyond individual organizations; it impacts entire industries and societies.
+The future of data analysis is poised for exciting developments driven by advancements in technology and shifts in data usage patterns.
-=== Decision-Making ===
+=== Integration of AI and Automation ===
-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.
+As artificial intelligence and automation technologies continue to evolve, data analysis will increasingly incorporate these capabilities to enhance analytical accuracy and efficiency. Automation in data cleaning, processing, and visualization will enable analysts to focus on interpretation and decision-making rather than repetitive tasks.
-=== Economic Growth ===
+=== Enhanced Real-time Analytics ===
-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.
+The demand for real-time analytics is growing as organizations seek timely insights for rapid decision-making. The ability to analyze data in real-time enables businesses to respond quickly to market changes, customer needs, and emerging opportunities. Technologies such as stream processing are set to play a pivotal role in facilitating real-time data analysis.
-=== Societal Change ===
+=== Greater Focus on Data Literacy ===
-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.
+As data becomes an integral part of business strategy, the emphasis on data literacy is anticipated to increase. Organizations will invest in training programs to enhance employees' abilities to interpret and analyze data. A workforce skilled in data literacy will be better equipped to make informed decisions based on evidence.
+=== Ethical Frameworks and Governance ===
+The need for ethical frameworks and data governance will become increasingly important as data analysis continues to expand and influence diverse areas. Organizations are expected to establish clearer policies concerning data collection, usage, and sharing to ensure ethical practices and compliance with regulations.
 == See Also ==
 * [[Big Data]]
-* [[Data Science]]
+* [[Statistics]]
+* [[Data Mining]]
 * [[Business Intelligence]]
-* [[Statistics]]
 * [[Machine Learning]]
+* [[Predictive Analytics]]
 == References ==
-* [https://www.statista.com/statistics/464478/number-of-data-analysts-worldwide/ Statista: Number of Data Analysts Worldwide]
+* [https://www.r-project.org/ R Project for Statistical Computing]
-* [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.sas.com/en_us/home.html SAS - Analytics Software]
-* [https://www.analyticsvidhya.com/blog/2021/02/data-analysis-101-a-comprehensive-guide/ Analytics Vidhya: Data Analysis 101 - A Comprehensive Guide]
+* [https://www.ibm.com/analytics BI and Analytics Solutions from IBM]
-* [https://towardsdatascience.com/data-analysis-in-python-the-basics-199b827e1fd3 Towards Data Science: Data Analysis in Python - The Basics]
+* [https://www.tableau.com/ Tableau - Data Visualization Software]
-* [https://www.forbes.com/sites/bernardmarr/2020/01/30/what-is-data-analysis/?sh=39be8cbb6a29 Forbes: What is Data Analysis?]
+* [https://www.microsoft.com/en-us/microsoft-365/business/solutions/power-bi-data-visualization Power BI - Business Analytics]
 [[Category:Data analysis]]
-[[Category:Statistics]]
+[[Category:Statistical analysis]]
 [[Category:Data science]]