Big Data Concept. By Robinson A.

Traditional statistics  of Data  Traditional statistics is made up of  descriptive and inferential statistics. Let’s explore the difference between descriptive statistics and inferential statistics. Descriptive Statistics: Descriptive statistic s involves the summarization and presentation of data in a meaningful way. It provides insights into the characteristics of a dataset without making inferences beyond the data itself. Common measures used in descriptive statistics include: Measures of central tendency: Mean, median, and mode, which represent the central or typical value of a dataset. Measures of dispersion: Range, variance, and standard deviation, which quantify the spread or variability of data points. Measures of shape: Skewness and kurtosis, which describe the symmetry and peakedness of a distribution, respectively. Frequency distributions: Tables, histograms, and bar charts, which display the frequency or relative frequency of values within a dataset. Graphs:...

 Types of Data Visualisations Data visualizations come in various forms, each suited to different types of data and analytical goals. Visualizations are diverse and cater to different data types, patterns, and user preferences. Here are some common types of data visualizations:  Types of Data Visualisations Bar Chart:  This is use to displays different categorical of data with rectangular bars, where the length of each bar represents the value of a category. Line Chart:  It shows data points connected by straight lines, often used to visualize trends and changes over time. Scatter Plot:   I represents individual data points as dots on a two-dimensional graph, it useful for visualizing relationships and correlations between variables. Pie Chart:   It divides a circle into sectors to represent the proportion of different categories within a dataset, suitable for showing parts of a whole. Histogram:  It displays the distribution of numerical data by...

 Strategies for Limiting the Negative Effects of Big Data.    The Strategies for limiting the negative effects of big data involve a combination of technical, regulatory, and ethical approaches. It  requires a multi-faceted approach that addresses various ethical, legal, and social considerations. Here are some strategies: Strategies for Limiting the Negative Effects of Big Data   Data Governance and Regulation:   Implement robust data governance frameworks and regulations to ensure responsible data collection, storage, and usage. This includes compliance with data protection laws such as GDPR and CCPA, as well as industry-specific regulations. Privacy Protection:   Prioritize data privacy by anonymizing or pseudonymizing personal data, obtaining explicit consent for data collection and usage, and implementing strong security measures to safeguard sensitive information from unauthorized access or breaches. Transparency and Accountability:  P...

Application of Big data Techniques to a Problem. Big data techniques have been widely applied across various domains to address complex problems and extract valuable insights. Let's consider an application of big data techniques to a real-world problem: predicting customer churn for a subscription-based service. Problem Statement: A company provides a subscription-based service (e.g., a streaming platform, a software-as-a-service application, or a telecommunications provider) and wants to reduce customer churn by identifying users at risk of canceling their subscriptions. The company has access to a large dataset containing historical customer interactions, demographics, usage patterns, and subscription-related information. Below are some notable applications: Application of Big data Techniques to a Problem. Data Collection and Integration:   Gather data from various sources, including customer interactions, subscription history, demographic information, usage logs, and customer su...

  You are welcome to Robmags335 Blog On  this Blog, I will be discussing and sharing my Idea about BIG DATA  and the importance to individual and the society. In today’s digital age, the sheer volume of data being generated is staggering. From social media posts to online shopping transactions, every action we take generates data. This data, known as big data, holds immense potential for businesses and industries across the globe. Understanding the concept of big data is crucial to harness its power and unlock its countless benefits. Big data has become integral to our modern world, revolutionizing how we collect, manage, and analyze information. It has opened up new possibilities and opportunities across various industries, from healthcare to finance to marketing. But what exactly is big data, and how has it evolved over time? let explore as we progress