Lesson 2 Flashcards
is a field of business intelligence with expertise in statistical analysis, waiting for history, and other data.
Descriptive Analytics
There are four main steps in descriptive analytics:
- Data Collection
- Data Preparation
- Exploratory Data Analysis
- Data visualization
The purpose of descriptive analytics
to turn data into insights. It is used to understand what happened in the past and why it happened.
Collecting data from various sources such as sales reports, customer surveys, social media, etc.
Data Collection
Cleaning and organizing the data so it can be analyzed.
Data Preparation
Analyzing the data to find trends, patterns, and relationships.
Exploratory Data Analysis
Creating graphs and charts to visualize the data and make it easy to understand.
Data Visualization
The most common techniques used in descriptive analytics are
statistical analysis
data visualization
predictive modeling.
Why descriptive analytics is important in data science?
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.
a branch of data science that deals with data collection, organization, and analysis.
Descriptive Analytics
How does descriptive analytics work in data science?
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.
There are several uses for the metrics generated by descriptive analytics, including:
- Reports
- Visualizations
- Dashboard
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.
Reports
Metrics can be more effectively communicated to a larger audience by being displayed in charts and other graphic forms.
Visualizations
are a tool that executives, managers, and other staff members can use to monitor progress and organize their daily workload.
Dashboards
Five Steps Descriptive Data Science Involves:
Step 1: Define Business Metrics
Step 2: Identify Data Required
Step 3: Extract And Preprocess Data
Step 4: Data Analysis
Step 5: Present Data
These should represent the main organization’s objectives of each segment or the organization as a whole.
Define Business Metrics
Find the data you require to generate the desired stats. The data may be dispersed over numerous programmes and files at some businesses.
Identify Data Required
When data is gathered from several sources, extracting, integrating, and preprocessing it before analysis is a time-consuming but necessary step to ensure accuracy.
Extract and Preprocess Data
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.
Data Analysis
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.
Present Data
Most Common Descriptive Analysis Methods for Descriptive Analysis Statistics
- Frequency Distribution
- Bar Charts
- Pie Charts
- Scatter Plot
- Histogram
is a method that provides an overview of all the responses to a question.
Frequency Distribution
is a visual representation that displays how responses vary on different dimensions.
Bar Chart
