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Descriptive Analytics > Lesson 2 > Flashcards

Lesson 2 Flashcards

1
Q

is a field of business intelligence with expertise in statistical analysis, waiting for history, and other data.

A

Descriptive Analytics

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2
Q

There are four main steps in descriptive analytics:

A
  1. Data Collection
  2. Data Preparation
  3. Exploratory Data Analysis
  4. Data visualization
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3
Q

The purpose of descriptive analytics

A

to turn data into insights. It is used to understand what happened in the past and why it happened.

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4
Q

Collecting data from various sources such as sales reports, customer surveys, social media, etc.

A

Data Collection

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5
Q

Cleaning and organizing the data so it can be analyzed.

A

Data Preparation

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6
Q

Analyzing the data to find trends, patterns, and relationships.

A

Exploratory Data Analysis

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6
Q

Creating graphs and charts to visualize the data and make it easy to understand.

A

Data Visualization

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7
Q

The most common techniques used in descriptive analytics are

A

statistical analysis
data visualization
predictive modeling.

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8
Q

Why descriptive analytics is important in data science?

A

This type of analytics is important in data science because it allows researchers to understand trends and patterns in data. It also helps researchers to develop hypotheses about how certain factors may influence the results of their research. Additionally, descriptive analytics can create visualizations of data that can help researchers/organizations communicate their findings to others.

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9
Q

a branch of data science that deals with data collection, organization, and analysis.

A

Descriptive Analytics

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10
Q

How does descriptive analytics work in data science?

A

Descriptive Analytics is a powerful tool that can summarize data and communicate information in an understandable way.

Descriptive statistics in data science can be used to identify relationships between variables and examine the differences between data groups.

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11
Q

There are several uses for the metrics generated by descriptive analytics, including:

A
  1. Reports
  2. Visualizations
  3. Dashboard
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12
Q

Descriptive analytics is used to provide the primary financial indicators found in a company’s financial statements. Descriptive analytics are often used in other typical reports to emphasize specific areas of business performance.

A

Reports

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13
Q

Metrics can be more effectively communicated to a larger audience by being displayed in charts and other graphic forms.

A

Visualizations

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14
Q

are a tool that executives, managers, and other staff members can use to monitor progress and organize their daily workload.

A

Dashboards

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15
Q

Five Steps Descriptive Data Science Involves:

A

Step 1: Define Business Metrics
Step 2: Identify Data Required
Step 3: Extract And Preprocess Data
Step 4: Data Analysis
Step 5: Present Data

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16
Q

These should represent the main organization’s objectives of each segment or the organization as a whole.

A

Define Business Metrics

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17
Q

Find the data you require to generate the desired stats. The data may be dispersed over numerous programmes and files at some businesses.

A

Identify Data Required

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18
Q

When data is gathered from several sources, extracting, integrating, and preprocessing it before analysis is a time-consuming but necessary step to ensure accuracy.

A

Extract and Preprocess Data

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19
Q

Businesses can utilize a variety of technologies, such as spreadsheets and business intelligence (BI) tools, to do descriptive analytics. In descriptive analytics, applying simple mathematical operations to one or more variables is a common step.

A

Data Analysis

20
Q

Data that is presented in visually appealing forms, such as pie charts, bar charts, and line graphs, is typically easier for stakeholders to understand. But some people, like financial professionals, would like information that is provided in the form of figures and tables.

A

Present Data

21
Q

Most Common Descriptive Analysis Methods for Descriptive Analysis Statistics

A
  1. Frequency Distribution
  2. Bar Charts
  3. Pie Charts
  4. Scatter Plot
  5. Histogram
22
Q

is a method that provides an overview of all the responses to a question.

A

Frequency Distribution

23
Q

is a visual representation that displays how responses vary on different dimensions.

A

Bar Chart

24
Q

displays how responses vary on different dimensions.

A

Pie Charts

25
Q

displays how two variables relate to each other.

A

Scatter Plot

26
Q

provides an overview of all the responses to a question, with each response grouped into bins according to some criterion such as age or income level.

A

Histogram

27
Q

There are four different types of descriptive analysis:

A
  1. Measures of Frequency
  2. Measures of Central Tendency
  3. Measures of Dispersion
  4. Measures of Position
28
Q

Understanding how frequently a specific event or response is likely to occur is crucial for descriptive analysis. The main goal of frequency measurements is to create something akin to a count or a percentage.

A

Measures of Frequency

29
Q

Measures: Count, Percent, Frequency

A

Measures of Frequency

30
Q

A single value that seeks to characterize a set of data by pinpointing the central position within that set of data is referred to as a

A

Measures of Central Tendency

31
Q

Measures: Mean, Median, and Mode

A

Measures of Central Tendency

32
Q

understanding how data is distributed across a range is crucial. Consider the average weight of a sample of two people to further explain this.

A

Measures of Dispersion

33
Q

Measures: Range, Variance, Standard Deviation

A
  1. Measures of Dispersion
34
Q

Identifying the position of a single value or its response in relation to others is another aspect of descriptive analysis.

A

Measures of Position

35
Q

Measures: Percentile Ranks, Quartile Ranks

A

Measures of Position

36
Q

Two Methods of Bivariate

A
  1. Contingency Table
  2. Scatter Plots
37
Q

In statistics, also known as a two-way frequency table

A

Contingency Table

38
Q

is a tabular representation with at least two rows and two columns that are used to present categorical data as frequency counts.

A

Contingency Table

39
Q

You can visualize the relationship between two or three different variables using a

A

Scatter Plots

40
Q

Advantages of Descriptive Analytics in Data Science

A
  • gives access to information that would otherwise be difficult to understand.
  • gives a precise estimation of how frequently important data points occur.
  • is cheap, and it only calls for rudimentary mathematical knowledge.
  • is easier to complete, particularly with the aid of programmes like Python or Microsoft Excel.
  • relies on information that businesses already have, so getting new information is not necessary.
  • compared to inferential statistics, it considers the entire population (rather than a data sampling).
41
Q

Drawbacks

A
  • Although you can summarize the data sets you have access to, they might not provide the full picture.
  • Descriptive analytics can’t be used to test a theory or figure out why data is presented in a certain way.
  • Descriptive analytics cannot be used to make future predictions.
  • Your results cannot be applied to a larger population as a whole.
  • Descriptive analytics provide no information regarding the method of data collection, so the data set may contain errors.
42
Q

Descriptive Analytics Use Cases

A
  1. Monitoring Social Media Activity
  2. Streaming and Online Shopping
  3. Learning Management Systems
42
Q

5 Examples of Descriptive Analytics

A
  1. Traffic and Engagement Reports
  2. Financial Statement Analysis
  3. Demand Trends
  4. Aggregated Survey Results
  5. Progress to Goals
43
Q

There are several types of financial statements

A

Balance Sheets, Income Statements, Cash Flow Statement and Shareholders’ Equity Statements.

44
Q

reads the statement from top to bottom, comparing each element to the elements above and below it. This helps determine relationships between variables.

A

Vertical analysis

45
Q

reads the statement from left to right and compares each item to itself in the previous period. This type of analysis determines changes over time.

A

Horizontal analysis

46
Q

Balance sheet analysis can be performed in three ways:

A

vertical, horizontal and ratio.

Descriptive Analytics (6 decks)

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