AI and Data analytics are gaining a lot of traction in 2024. They both are very important in today’s business landscape. Learn more about AI and data analytics tools, uses, importance, and more here!
AI and Predictive Analytics: Predictive analytics involves using methods from data mining, statistics, machine learning, mathematical modelling, and artificial intelligence to foresee future events. It relies on past data to make predictions, and we often apply predictive analytics in our everyday lives without much consideration. For instance, forecasting the sales of a product (like flowers) …
AI and Predictive Analytics: Definition, Examples, Tools, Function, and More! Read More »
AI For Data Analysis: As business data becomes more complicated, there’s a requirement for more sophisticated approaches to understand it.
Data analyst is set to provide new opportunities at the beginning of 2024. Read here to learn the learning path for a roadmap to becoming a data analyst.
Difference Between Data Analysis and Data Analytics: There’s a lot of confusion about the difference between data analysis and data analytics. Even though they sound similar, they have different definitions. Because of the word similarity, some folks think they mean the same thing and use them interchangeably. Technically, this is not accurate; there’s a clear …
Difference Between Data Analysis and Data Analytics: Primary Differences Read More »
Analysis of Quantitative data enables you to transform raw data points, typically organised in spreadsheets, into actionable insights. Refer to the article to know more!
Analysis of Survey Data: As a researcher, marketer, or student, have you ever struggled to make sense of all the responses from a survey you administered? You’re not alone – understanding large amounts of survey data can be an overwhelming task. Data, data everywhere – but are you making sense of it all? However, raw …
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Data analysis techniques in research help you turn raw data into clear, meaningful insights that support your research objectives. These techniques include qualitative analysis, quantitative methods, statistical tools, and software that simplify complex datasets. Researchers often use methods like descriptive statistics, regression analysis, correlation analysis, and data visualization to interpret findings accurately. Understanding the right data analysis tools in research—such as Excel, SPSS, Python, R, and SQL—ensures reliable and bias-free results. This guide explains types of data analysis, methods, tools, and real examples to help you choose the best technique for your study.
Big data analytics is the process of identifying trends, patterns, and correlations in vast amounts of raw data in order to make data-informed decisions. Keep reading to know more!
Data analytics and big data analysis are two big terms used in this data-driven world. Let us gather some knowledge about them in detail here.
