Mod 2 -Descriptive Analytics - Final ppt.pdf
- 1. Descriptive Analytics (Using MS Excel/Python/R)
- 2. What is Descriptive Analytics? Descriptive analytics uses statistical summaries and data visualization techniques to condense and describe historical data. It helps identify patterns, trends, and relationships within the data, clearly showing “what happened” and “what is currently happening.” Think of it as the foundation for further analysis – it sets the stage for understanding past performance and current trends. A common example of Descriptive Analytics are company reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders. It is relevant to note that in the Big Data world, the “simple nuggets of information” provided by Descriptive Analytics become prepared inputs for more advanced Predictive or Prescriptive Analytics that deliver real-time insights for business decision making.
- 3. Uses of Descriptive Analytics Descriptive Analytics helps to describe and present data in a format which can be easily understood by a wide variety of business readers. Descriptive Analytics rarely attempts to investigate or establish cause and effect relationships. As this form of analytics doesn’t usually probes beyond surface analysis, the validity of results is more easily implemented. Some common methods employed in Descriptive Analytics are observations, case studies, and surveys. Thus, collection and interpretation of large amount of data may be involved in this type of analytics. Descriptive analytics helps to identify important patterns and trends in large datasets. In comparison to all other methods of data analysis, descriptive is the most used one. The main task of descriptive analytics is to create metrics and key performance indicators for use in dashboards and business reports.
- 4. Data analytics can be divided into four key types: Descriptive Analytics: What happened? (Summarizes past and current data) • Diagnostic Analytics: Why did this happen? (Drills down to identify causes) • Predictive Analytics: What might happen in the future? (Uses trends to forecast future events) • Prescriptive Analytics: What should we do next? (Recommends actions based on predictions)
- 6. Advantages of Descriptive Analytics • Data-driven decision making: It provides well-informed decision-making based on facts rather than gut instincts by evaluating and simplifying data. • Presents data clearly: Descriptive analytics simplifies complex data, making it easy to understand through reports and visualizations like charts and graphs. • Convenient to Realize: Data that has been summarized and graphically represented is easier to clarify and evaluate for a larger audience. • Identifies Relevant Data Points: It offers straightforward metrics that give an accurate estimation of important data points. • Simple and cost-effective: Descriptive analytics is simple to use and just requires basic arithmetic knowledge for execution. • Efficient with tools: With the aid of tools like Python or MS Excel, which make things fast and easy.
- 7. Disadvantages of Descriptive Analytics • Inability of Cause Analysis: The main goal of descriptive analytics is to explain historical events. It doesn’t explore the root causes or reasons for the patterns that are seen. • Analysis Simplicity: The reach of descriptive analytics is restricted to basic analyses that look at the relationships between a small number of variables. • Doesn’t Explain Why: History offers lessons for future generations, by offering facts, but causes and predictions are not provided to the readers. • Inappropriate for Making Decisions in Real Time: Normally, descriptive analytics involves getting summary information at intervals intervals and this might not be the best option for decision- making when the time matter. In many situations, fast responsiveness is vital, therefore, sometimes only relying on the descriptive analytics might drag you behind. • Lack of ability to handle unstructured data: Structured and well-organized datasets are better suited for descriptive analytics. while analyzing semi-structured or unstructured data, such as text, photos, or multimedia, it could make challenging to offer insightful analysis.
- 8. Applications of Descriptive Analytics • Financial Performance Evaluation: For instance, in the past; descriptive analytics was often used to appraise and assess a specific firm’s previous performances. Lots of organizations can detect trends, patterns and possibilities for a change by tracking key performance indicators (KPI’s) at different periods of time. This awareness helps in the construction and building of business operations with all the required strategic planning. • Marketing and Analysis of Customer Behavior: However, Companies should analyze and understand the customers’ behavior. Firms need descriptive analytics to weight historical data on consumer interactions, purchasing patterns, and preferences. • Friction Analysis in Business Processes: Descriptive analytics is applied descriptive approaches in business learning and development, and to detect and reduce friction in business processes. All the blockades or impairing of efficiency restraining processes from moving will be called friction. Organizations can easily pinpoint the bottlenecks of their business processes by looking at historical data over workflow delays using of resources and process’s time. • Social Networking Analytics: In order to analyze user involvement, content performance, and audience demographics, descriptive analytics is used in social media. It assists businesses in customizing their social media plans according on past performance. • Crime and Fraud Detection: Pattern in previous crime data is investigated by law enforcement and security agencies in order to do descriptive analysis which is one of the types of analytics. It is applied by financial organizations to make discoveries of market fluctuations and anomalies that can prevent or can be used to fight them. • Crypto Market Analysis: Cryptocurrency markets are a great source of information for investors, as historical price data, market volumes aggregates, and market trends can be used to analyze the behavior of Bitcoin traders. These algorithms, mood patterns in the market, and possible factors may affect the price fluctuation of Bitcoin can all been fancy with the help of a descriptive analytics.
- 9. What is Descriptive Statistics? Descriptive statistics is a branch of statistics focused on summarizing, organizing, and presenting data in a clear and understandable way. Its primary aim is to define and analyze the fundamental characteristics of a dataset without making sweeping generalizations or assumptions about the entire data set. Descriptive statistics is a type of statistical analysis that uses quantitative methods to summarize the features of a population sample. It is useful to present easy and exact summaries of the sample and observations using metrics such as mean, median, variance, graphs, and charts. Descriptive statistics is a subfield of statistics that deals with characterizing the features of known data. Descriptive statistics give summaries of either population or sample data. Aside from descriptive statistics, inferential statistics is another important discipline of statistics used to draw conclusions about population data. The main purpose of descriptive statistics is to provide a straightforward and concise overview of the data, enabling researchers or analysts to gain insights and understand patterns, trends, and distributions within the dataset. Descriptive statistics typically involve measures of central tendency (such as mean, median, mode), dispersion (such as range, variance, standard deviation), and distribution shape (including skewness and kurtosis). Additionally, graphical representations like charts, graphs, and tables are commonly used to visualize and interpret the data. Histograms, bar charts, pie charts, scatter plots, and box plots are some examples of widely used graphical techniques in descriptive statistics.
- 10. There are three types of descriptive statistics: Measures of Central Tendency Measures of Dispersion Measures of Frequency Distribution
- 14. Descriptive Statistics Applications • Business and Economics: Descriptive statistics are useful for analyzing sales data, market trends, and customer behaviour. They are used to generate averages, medians, and standard deviations in order to better evaluate product performance, pricing strategies, and financial metrics. • Healthcare: Descriptive statistics are used to analyze patient data such as demographics, medical histories, and treatment outcomes. They assist healthcare workers in determining illness prevalence, assessing treatment efficacy, and identifying risk factors. • Education: Descriptive statistics are useful in education since they summarize student performance on tests and examinations. They assist instructors in assessing instructional techniques, identifying areas for improvement, and monitoring student growth over time. • Market Research: Descriptive statistics are used to analyze customer preferences, product demand, and market trends. They enable businesses to make educated decisions about product development, advertising campaigns, and market segmentation. • Finance and investment: Descriptive statistics are used to analyze stock market data, portfolio performance, and risk management. They assist investors in determining investment possibilities, tracking asset values, and evaluating financial instruments.
- 15. Frequency Distribution in Statistics A frequency distribution is a representation, either in a graphical or tabular format, that displays the number of observations within a given interval. The frequency is how often a value occurs in an interval, while the distribution is the pattern of frequency of the variable. A frequency distribution is an overview of all values of some variable and the number of times they occur. It tells us how frequencies are distributed over the values. That is how many values lie between different intervals. They give us an idea about the range where most values fall and the ranges where values are scarce.
- 16. Frequency Distribution Graphs Graph Type Description Use Cases Histogram Represents the frequency of each interval of continuous data using bars of equal width. Continuous data distribution analysis. Bar Graph Represents the frequency of each interval using bars of equal width; can also represent discrete data. Comparing discrete data categories. Frequency Polygon Connects midpoints of class frequencies using lines, similar to a histogram but without bars. Comparing various datasets. Pie Chart Circular graph showing data as slices of a circle, indicating the proportional size of each slice relative to the whole dataset. Showing relative sizes of data portions.
- 21. Step 1: Enter the Data Step 2: Select the Data Step 3: Choose Chart type to generate Bar Diagram
- 22. Step 1: Enter the Data Step 2: Select the Data Step 3: Choose Chart type to generate Histogram
- 23. Measures of location • Measures of location are used to quantify where an observation stands in relation to the rest of the distribution. They describe the central tendency of the data. Common measures of location include: • Quartiles • Percentiles • Mean • Median • Mode
- 24. Measures of location The common measures of location are quartiles and percentiles. Previously, we learned that the median is a number that measures the “center” of the data. But the median can also be thought of as a measure of location because the median is the “middle value” of a set of data. The median is a number that separates ordered data into halves. Half of the values in the data are the same number or smaller than the median and half of the values in the data are the same number or larger.
- 25. Some common measures of location are: • Mean: the sum of the data points divided by the number of data points. • Median: the middle value that divides the data into two equal halves. • Mode: the most frequent value in the data. • Quartiles: the values that divide the data into four equal parts. • Percentiles: the values that divide the data into 100 equal parts. • Minimum: the smallest value in the data. • Maximum: the largest value in the data. • Midrange: the average of the minimum and maximum values. • Five number summary: a set of five numbers that includes the minimum, maximum, median, and quartiles.
- 26. Dispersion in Statistics Dispersion in statistics is a way to describe how spread out or scattered the data is around an average value. It helps to understand if the data points are close together or far apart. Dispersion shows the variability or consistency in a set of data. There are different measures of dispersion like range, variance, and standard deviation. Measures of Dispersion measure the scattering of the data. It tells us how the values are distributed in the data set. In statistics, we define the measure of dispersion as various parameters that are used to define the various attributes of the data. These measures of dispersion capture variation between different values of the data.
- 27. Measures of Dispersion are used to represent the scattering of data. These are the numbers that show the various aspects of the data spread across various parameters. Measures of location are numbers that describe the position of a data point in a distribution.
- 28. Types of Measures of Dispersion Measures of dispersion can be classified into the following two types : • Absolute Measure of Dispersion • Relative Measure of Dispersion These measures of dispersion can be further divided into various categories. They have various parameters and these parameters have the same unit.
- 30. Absolute Measure of Dispersion The measures of dispersion that are measured and expressed in the units of data themselves are called Absolute Measure of Dispersion. For example – Meters, Dollars, Kg, etc. Some absolute measures of dispersion are: Range: It is defined as the difference between the largest and the smallest value in the distribution. Mean Deviation: It is the arithmetic mean of the difference between the values and their mean. Standard Deviation: It is the square root of the arithmetic average of the square of the deviations measured from the mean. Variance: It is defined as the average of the square deviation from the mean of the given data set. Quartile Deviation: It is defined as half of the difference between the third quartile and the first quartile in a given data set. Interquartile Range: The difference between upper(Q3 ) and lower(Q1) quartile is called Interter quartile Range. Its formula is given as Q3 – Q1.
- 31. Relative Measure of Dispersion Coefficient of Range: It is defined as the ratio of the difference between the highest and lowest value in a data set to the sum of the highest and lowest value. Coefficient of Variation: It is defined as the ratio of the standard deviation to the mean of the data set. We use percentages to express the coefficient of variation. Coefficient of Mean Deviation: It is defined as the ratio of the mean deviation to the value of the central point of the data set. Coefficient of Quartile Deviation: It is defined as the ratio of the difference between the third quartile and the first quartile to the sum of the third and first quartiles.
- 32. Association Association is concerned with how each variable is related to the other variable (s). In this case, the first measure that we will consider is the covariance between two variables j and k. Population covariance is a measure of the association between pairs of variables in a population.
- 33. Types of Association Strong positive association The association can be strong (very little scatter compared to the movement in the trend) or weak (lots of scatter around the trend). An association is called positive if y tends to get bigger when x gets bigger and negative if y tends to get smaller as x gets bigger. Example: Running on a treadmill for a longer period of time will help you burn more calories. As your hair grows longer, you will need more shampoo. A strong negative correlation in practice means an inverse relationship with a correlation coefficient of -0.4 and greater. By greater, the closer a correlation coefficient is to 1.00 or -1.00 the stronger the correlation. What this means is for every increase in unit of variable X, 0.4 units of Y decrease. Example of negatively correlating variables: The more it rains, the less you can water the garden. The more you cook at home, the less you might eat out. The lower the temperature, the more clothes you may wear
- 38. Data Visualization Data visualization is the use of visual representations to display information. This definition might sound modern, but data visualization has been around for centuries. One of the earliest and most obvious examples is maps, which developed out of the need to graphically display geographic data. Since then data visualization has continued to develop to meet the needs of today’s users. There are multiple ways to visualize data (including charts, graphs, and infographics), and technology is constantly evolving to present information in more eye-catching and useful ways. Examples of this include making visualizations interactive and allowing the end user to filter and display different metrics. Regardless of these updates, the aim remains the same: to present key insights and make it easier to engage with and understand data.
- 40. Importance of Data Visualization Data visualization helps to ensure data insights aren’t lost in delivery; most of us can’t process big blocks of statistics, our brains aren’t built like that. Anyone who has looked at a long list of numbers will understand the disconnect this can cause. Graphical representation solves this pain point by making statistics and data easier to absorb. Data visualization is not only about creating simple and attractive visuals. It can be used to create insights by identifying patterns and trends that would otherwise be difficult to spot. Displaying a set of data on a scatter plot, for example, might reveal connections between outliers that previously went unnoticed when the statistics were in a table.
- 41. Types of Data Visualization There are instances when you may need to display one key figure, for example, the number of customers, or the number of returned items. A KPI visualization is best suited to this purpose because it shows one big number. However, this number will mean nothing on its own; you have to, at the very least, provide a date range and compare it with another metric to give it some context. To show comparisons between categories, for example, the number of sales each staff member has made in the last month, it is best to use a bar chart or column chart. A stacked bar chart gives you the option to add another category, so as well as showing how many sales each staff member has made, you might also include the product type they sold by adding color and a key.
- 42. When comparing parts to the whole it is best to use a pie chart, donut chart, or treemap. An example of part-to-whole comparison is the number of people who answered ‘yes’ or ‘no’ to a specific question. Generally speaking, it is a bad idea to use a pie chart or donut chart for more than three categories because it becomes difficult for users to accurately absorb the data. With more categories, it is better to use a treemap. To show changes over time the most effective options are line charts, area charts, or column charts. You might, for instance, choose one of these to display month-by-month revenue. If you want to add an additional category (such as product type) you can use a line chart with multiple lines or a stacked area chart. But it's best to tread carefully with these because they can become confusing if not properly executed. To show the details of many items it is best to use a table. Some people avoid using tables because they seem too basic, but when you have many items (such as a lot of customer details) a table can be the right choice. Amid the myriad of visualization options available, tables can be quite striking when combined with other types of charts and graphs on a dashboard.
- 44. Advantages of Data Visualization • Easy to spot trends. Visualization allows users to see patterns in the data they might otherwise have missed. • Simple sharing of information. It is far easier to share data with charts, graphs, and infographics. • Makes data accessible to non-technical users. With visualization, you no longer need to be a mathematician to understand the data insights. • Easy to remember. Charts and graphs are not only easier to digest; they also tend to stay in the memory more easily than lists of numbers and statistics. • Increase revenue. When all the decision-makers have the information at their fingertips, it empowers management to make quick and accurate decisions.
- 45. Disadvantages of Data Visualization • Information still needs to be accurate. Great visualizations i.e, don’t make up for bad data. If best practices are not followed then visualizations can fall into the trap of becoming style over substance. • Data visualization is an investment. Companies that want to effectively organize and visualize their data, or provide this ability to their customers, will either need a lot of involvement from analytics engineers (if they have the resources)), or an integrated analytics solution. Neither of these options comes without its costs, and pricing can vary depending on requirements. This then raises the question of whether to build the analytics solution in-house or buy off the shelf. • Correlation does not equal causation. Visualizations often show the correlation between two or more metrics, so users often assume causation. But just because there is a correlation it doesn’t necessarily mean that one is caused by the other. There may be several other factors at play that aren’t included in the visualization. • Users can still misinterpret the information. While visualization makes it easier for users to absorb data, it is still open to misinterpretation. For example, users might focus on the wrong thing when viewing it. This once again highlights the importance of using the right visualization type for the data displayed and the desired outcome. • Confusing visualizations. Visualizations are supposed to simplify data, but if done badly they can make matters even more complicated. Perhaps the wrong chart type has been chosen, or there is too much information
- 46. Tools Need for Data Visualization 1. Chart generators or plugins: These tend to be used by developers and data engineers because the software requires a more advanced level of expertise. The plugins have many visualization types to choose from and there may even be a data-processing API that allows you to create actionable insights from your data. These tools usually have the capability to categorize and analyze basic data, and so can be used as the foundation of a company’s BI platform. 2. Visualization reporting software: This is most often used by report developers and BI engineers. The software creates business and data analysis reports, which can then be turned into visualizations using a selection of built-in charts. 3. A fully integrated BI and analytics solution: As the name suggests, this is the most complete solution. A good BI platform will allow you to easily explore data on your own, and create interactive dashboards and charts via a user-friendly no-code UI. The top solutions offer plug-and-play integrations, no-code tools, and flexible embedding options (such as React, Iframes, and Web Components) that allow you to seamlessly embed visualizations and dashboards into your product in a way that matches the brand.
- 47. Tabular Versus Visual Display of Data An initial decision that has to be made about your data is whether it should be displayed in a table or a graph. Though there are no hard rules, there are general guidelines you can use to make this determination. Questions to ask yourself: • Are the independent and dependent variables qualitative or quantitative? • What is the total number of data points to be shown? • Is there more than one independent variable? • Are you trying to represent the statistical distribution of the data? • How important is it to be able to see individual values? • How important is it to understand the overall trend? With these questions in mind, here are some examples:
- 48. Table: Impact failure threshold of 1018 cold rolled steel Temperature (deg C) Mean Impact Energy (joules) 20 70.4 100 77.3 With only two values in Table 2, it does not make much sense to provide a graph since the data can be easily interpreted from the table data. The display of the exact values for each data point in Figure 1 reinforces the lack of the necessity of a graph. Two data points can also be successfully described in the main text without a table. They should be included in a table only if required by the instructor.
- 49. Tools and software for data visualization Data visualization tools range from no-code business intelligence tools like Power BI and Tableau to online visualization platforms like DataWrapper and Google Charts. There are also specific libraries in popular programming languages for data science, such as Python and R. Data Visualization Tools refer to all forms of software designed to visualize data. Different tools can contain varying features but, at their most basic, data visualization tools will provide you with the capabilities to input datasets and visually manipulate them. Helping you showcase raw data in a visually digestible graphical format, data visualization tools can ensure you produce customizable bar, pie, Gantt, column, area, doughnut charts, and more.
- 50. When you need to handle datasets that contain up to millions of data points, you will need a program that will help you explore, source, trim, implement and provide insights for the data you work with. A data visualization tool will enable you to automate these processes, so you can interpret information immediately, whether that is needed for your annual reports, sales and marketing materials, identifying trends and disruptions in your audience's product consumption, investor slide decks, or something else. After you have collected and studied the trends, outliers, and patterns in data you gathered through the data visualization tools, you can make necessary adjustments in business strategy and propel your team closer to better results.
- 51. Data visualization: Creating charts 1. To create data visualization graphs and charts, you can follow these steps: Prepare your data. 2. Select the data that you want to include in your chart or graph. 3. Choose your chart type. 4. Customize your chart. 5. Save and share your chart. When creating data visualizations, it is important to keep it simple, add white space, use purposeful design principles, focus on three elements, and make it easy to compare data.
- 52. Google Charts Best Data Visualization Tool for Creating Simple Line Charts and Complex Hierarchical Trees.
- 53. Google Chart The powerful and free data visualization tool Google Charts is specifically designed for creating interactive charts that communicate data and points of emphasis clearly. The charts are embeddable online, and you can select the most fitting ones from a rich interactive gallery and configure them according to your taste. Supporting the HTML5 and SVG outputs, Google Charts work in browsers without the use of additional plugins, extracting the data from Google Spreadsheets and Google Fusion Tables, Salesforce, and other SQL databases. Visualize data through pictographs, pie charts, histograms, maps, scatter charts, column and bar charts, area charts, treemaps, timelines, gauges, and many more.
- 54. Data Visualization Dashboard A dashboard is a collection of visuals grouped in certain data points to achieve a set of goals. For example, a graph that shows the growing dynamics of leads is a visual. The dashboard would consist of the mentioned graph and other visuals to display a full picture of goal completion: • a pie chart showing the percentage of leads per channel • scorecards showing the number of leads and their quality • a Geo map showing the number of lead by country • and other graphic indicators
- 56. Thank You