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Q1. A. Attempt the question
[1X3=3]
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a)Define Excel
Charts
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Excel charts are graphical representations of data
from an Excel worksheet that
help users to visualise and analyse information more effectively.
Excel provides a variety of chart types such as column, bar, line, pie, and
scatter charts, among others. Each type of chart is suited for different
kinds of data and analysis needs, making it easier to identify trends,
patterns, and insights from the data.
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b)What is difference between function
and formula?
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Function: A function in Excel is a predefined calculation that
simplifies complex operations. Functions are built-in and perform specific
tasks using a particular syntax. For example, SUM(A1:A10) is a function
that adds all the numbers in the range A1 to A10.
Formula: A formula is a user-defined
calculation that can include a combination of functions, cell references,
operators, and constants. For example, =A1 + B1 * C1 is a formula that
calculates the result based on the values in cells A1, B1, and C1. Formulas
can also include functions, such as =SUM(A1:A10) + A11.
In summary, a function is a
predefined operation in Excel, while a formula is an expression created by
the user that can include one or more functions.
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c) What is Conditional Formatting?
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Conditional
formatting in Excel is a feature that allows you to apply specific
formatting to cells based on certain criteria or conditions. This can
include changes in font color, cell color, borders, and other formatting
options. For example, you can use conditional formatting to highlight cells
that contain values above a certain threshold, identify duplicate values,
or visualize data trends with color gradients. This feature helps to make
important data stand out and facilitates easier data analysis by visually
emphasizing key information.
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B. Attempt the question [1X2=2]
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a)What is Power
Query?
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Power Query is a data transformation and data
preparation engine. Power Query comes with a graphical interface for
getting data from sources and a Power Query Editor for applying
transformations
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b)How does a Slicer work in excel?
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A Slicer in Excel is a tool that allows you to filter
data in PivotTables and PivotCharts easily. You insert a Slicer by
selecting your PivotTable or PivotChart and choosing "Slicer"
from the "Insert" tab. The Slicer displays buttons representing
the unique values of a field. By clicking these buttons, you can filter the
data to show only the items that match the selected values. You can select
multiple values by holding down the Ctrl key while clicking, and clear the
filter by clicking the "Clear Filter" button at the top of the Slicer.
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Q2. Attempt
any ONE out of TWO the following. [1X4=4]
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a)What
is data analysis, and why is it important in various fields?
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Data
analysis is the process of inspecting, cleaning, transforming, and modeling
data to extract meaningful insights and make informed decisions. It
involves applying statistical, mathematical, and computational techniques
to understand patterns, trends, and relationships within datasets.
In various fields, data
analysis is crucial for several reasons:
Informed
Decision Making: Data analysis provides
valuable insights that enable organizations and individuals to make
informed decisions. By analyzing data, businesses can identify
opportunities, mitigate risks, and optimize processes.
Improved
Performance: Analyzing data helps
businesses and organizations understand their performance metrics better.
It allows them to identify areas of improvement, optimize resource
allocation, and enhance overall efficiency.
Predictive
Analytics: Data analysis techniques such
as predictive modeling and machine learning enable organizations to
forecast future trends and outcomes. This helps in proactive
decision-making and strategic planning.
Customer
Insights: Understanding customer
behavior is essential for businesses to tailor products, services, and
marketing strategies effectively. Data analysis helps uncover patterns and
preferences among customers, leading to improved customer satisfaction and
retention.
Research
and Development: In scientific
research, data analysis is critical for hypothesis testing,
experimentation, and drawing conclusions. It helps researchers validate
theories, discover new phenomena, and advance knowledge in various fields.
Healthcare
and Medicine: Data analysis plays a vital
role in healthcare for patient diagnosis, treatment optimization, and
public health monitoring. Analyzing medical data helps identify trends in
diseases, track outbreaks, and improve healthcare delivery.
Financial
Analysis: In finance, data analysis is
essential for risk management, portfolio optimization, and investment
decision-making. Analyzing financial data helps identify market trends,
assess investment opportunities, and manage financial risks.
Policy Making:
Governments and policymakers rely on data analysis to formulate
evidence-based policies and strategies. Analyzing socioeconomic and
demographic data helps identify societal needs, assess policy
effectiveness, and address public concerns.
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b)Explain
COUNTIF, AVERAGEIF, and
SUMIF functions in Excel
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COUNTIF
Excel has a built-in function
called COUNTIF that counts the given cells. The COUNTIF function can be
used in both straightforward and sophisticated applications. The
fundamental application of counting particular numbers and words is covered
in this.
=COUNTIF(range,criteria)
Range: The size of the cell
range to count.
Criteria: The standards by
which cells are selected for counting.
AVERAGEIF
An Excel built-in function
called AVERAGEIF determines the average of a range depending on a true or
false condition.
=AVERAGEIF(range, criteria,
[average_range])
Range: The size of the cell
range to count.
Criteria: The standards by
which cells are selected for counting.
Average Range: The range in
which the function computes the average is known as the average range. But
the average range is not required.
SUMIF
A built-in Excel function
called SUMIF determines
if a condition is true or false
before adding the values in a range.
=SUMIF(range, criteria,
[sum_range])
Range: The size of the cell
range to count.
Criteria: The standards by
which cells are selected for counting.
Sum
Range: The range that the function uses to calculate the total is known as
the sum range.
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Q3. Attempt
any ONE out of TWO the following. [1X4=4]
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a)Explain VLOOKUP and XLOOKUP
with steps ,example and Formula
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VLOOKUP:
1.Syntax:
=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])
2. Steps:
lookup_value: The value you want to search for.
table_array: The range of cells that contains the data.
col_index_num: The column number in the table from which to retrieve
the value.
range_lookup: Optional. A logical value indicating whether to find
an exact match or an approximate match. TRUE or 1 for approximate match
(default), FALSE or 0 for exact match.
3. Example:
Suppose you have a table containing student names in column A and
their corresponding grades in column B:
A B
John 85
Emma 92
Liam 78
Olivia 88
You want to find the grade for "Emma".
4.Formula:
=VLOOKUP("Emma", A1:B4, 2, FALSE)
This formula will search for "Emma" in the first column of
the range A1:B4 and return the corresponding grade from the second column
(column B).
XLOOKUP:
1.Syntax:
=XLOOKUP(lookup_value, lookup_array, return_array, [if_not_found],
[match_mode], [search_mode])
2. Steps:
lookup_value: The value you want to search for.
lookup_array: The range of cells that contains the values to be
searched.
return_array: The range of cells from which to retrieve the
corresponding values.
if_not_found: Optional. The value to return if the lookup_value is
not found.
match_mode: Optional. Specifies how XLOOKUP should match the
lookup_value: 0 for exact match (default), -1 for exact match or next
smaller item, 1 for exact match or next larger item.
search_mode: Optional. Specifies whether XLOOKUP should perform an
exact match or an approximate match. 1 for exact match (default), -1 for
first match, 2 for last match.
3. Example:-Using the
same example as before, finding the grade for "Emma".
4.Formula:=XLOOKUP("Emma",
A1:A4, B1:B4)
This formula will search for "Emma" in the range A1:A4 and
return the corresponding grade from the range B1:B4.
Key Differences:
XLOOKUP is more versatile than VLOOKUP as it can search in both
vertical and horizontal directions, and it supports approximate and exact
matches without the need for sorting data.
XLOOKUP also allows for searching from the end of the array, and it
can return an array of results.
VLOOKUP is limited to vertical
searches only and requires the data to be sorted in ascending order for
approximate matches.
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b)What are the advantages of performing
data analysis?
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Accessibility: Excel is widely available and familiar to many users,
making
it is accessible without the need for specialized software.
Versatility: Excel offers a wide range of
functions and tools for data analysis, including statistical functions,
pivot tables, and charting capabilities, enabling diverse analyses within a
single platform.
Ease of Use: Excel's user-friendly interface
allows users of all skill levels to perform data analysis tasks, from basic
calculations to more advanced statistical analyses, without extensive
training.
Integration: Excel seamlessly integrates with
other Microsoft Office applications, facilitating the incorporation of data
analysis results into reports, presentations, and other documents.
Customization: Excel allows extensive customization
of analyses, including formatting, conditional formatting, and the creation
of custom calculations and visualisations, enabling tailored analyses to
specific needs and preferences.
Cost-Effectiveness: Compared to specialised data
analysis software, Excel is often more cost-effective, particularly for
small to medium-sized businesses or individual users who may not require
advanced features
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Q4. Attempt
any ONE out of TWO of the following. [1X4=4]
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a)What is descriptive statistics Explain their five
functions with example
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Descriptive
statistics is a branch of statistics that deals with the collection,
analysis, interpretation, presentation, and organization of data. Its
primary purpose is to summarize and describe the main features of a
dataset, providing a clear and concise understanding of the essential
characteristics of the data.
Suppose we have the following
dataset in Excel:
A
10
15
20
25
30
Mean
(Average):
To calculate the mean (average)
in Excel, you can use the AVERAGE function. Here's how to do it:
Select an empty cell where you
want to display the mean.
Enter the following formula:
=AVERAGE(A1:A5)
This formula calculates the
average of the values in cells A1 through A5.
Median:
To calculate the median in
Excel, you can use the MEDIAN function. Here's how:
Select an empty cell where you
want to display the median.
Enter the following formula:
=MEDIAN(A1:A5)
This formula calculates the
median of the values in cells A1 through A5.
Minimum
(Min):
To find the minimum value in
Excel, you can use the MIN function. Here's how:
Select an empty cell where you
want to display the minimum.
Enter the following formula:
=MIN(A1:A5)
This formula calculates the
minimum value from the range A1:A5.
Maximum
(Max):
To find the maximum value in
Excel, you can use the MAX function. Here's how:
Select an empty cell where you
want to display the maximum.
Enter the following formula:
=MAX(A1:A5)
This formula calculates the
maximum value from the range A1:A5.
Range:
To calculate the range in
Excel, you can subtract the minimum value from the maximum value. Here's
how:
Select an empty cell where you
want to display the range.
Enter the following formula:
=MAX(A1:A5) - MIN(A1:A5)
This
formula subtracts the minimum value from the maximum value, giving you the
range of the dataset.
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b)Explain Requirements for an EDA project in Excel ?
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Microsoft Excel : A version of Microsoft Excel software, such as Microsoft Office,
installed on the computer to perform data analysis, visualization, and
reporting tasks.
Data : Relevant and
cleaned data that is organized in a structured format, such as a table, in
Excel. This data can come from various sources, such as spreadsheets,
databases, or external data files, and should be imported into Excel.
Data Cleaning Tools : Excel's
built-in data cleanings tools, such as filters, sorting, and data
validation, may be utilized to clean and preprocess the data, including
handling missing values, correcting data entry errors, and standardizing
data formats.
Data Analysis Tools : Excel provides
various data analysis tools, such as pivot tables, charts, and statistical
functions, that can be used to explore and analyze data. These tools can
help generate descriptive statistics, perform calculations, and visualize
data relationships.
Visualization Tools : Excel's charting
and graphing features can be used to create visualizations, such as bar
charts, line charts, scatter plots, and heatmaps, to represent the data
visually and identify patterns, trends, and outliers.
Statistical Functions : Excel's built-in statistical functions, such as mean, median,
mode, standard deviation, t-tests, ANOVA, regression analysis, and
correlation, can be used to perform hypothesis testing, calculate
statistical measures, and analyze data relationships.
Documentation Tools : Excel's features
for formatting, styling, and creating professional-looking reports or
presentations can be utilized to document the EDA process, including the findings,
insights, and conclusions from the analysis.
Additional Tools :
Depending on the specific requirements of the
project, additional tools or resources may be needed, such as external data
visualization tools, programming languages for advanced analysis, or
specialized add-ins for Excel.
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Q5. Attempt
any ONE out of TWO of the following. [1X4=4]
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a)Explain how to calculate
percentiles in Excel, and provide an example of how they are used to
analyse data?
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Calculating
percentiles in Excel involves using the PERCENTILE function, which returns
the value at a specified percentile in a range of values. Percentiles
divide a dataset into hundredths, indicating the percentage of values below
a given point.
Calculate
percentiles in Excel:
Syntax of
PERCENTILE function:
=PERCENTILE(array,
k)
array: The array or range of data values.
k: The percentile value, specified as a
number between 0 and 1. For example, to find the 75th percentile, k would
be 0.75.
Steps to calculate a percentile:
Select an
empty cell where you want to display the result.
Enter the PERCENTILE function, specifying the
range of data and the desired percentile value.
Example:
Suppose we have the following dataset in
Excel:
A
10
15
20
25
30
35
40
45
50
We want to
find the 75th percentile of this dataset.
Formula:
=PERCENTILE(A1:A9,
0.75)
This formula calculates the value at the 75th
percentile (or the 75th percentile) of the data in cells A1 through A9.
Percentiles
can be used to understand the distribution of data:
Identifying
Central Tendency: Percentiles help in understanding
where the majority of data values lie within a dataset. For instance, the
median represents the 50th percentile, indicating the midpoint of the data
distribution.
Assessing
Data Spread: Percentiles provide insights into the
spread or dispersion of data. Comparing percentiles such as the 25th and
75th percentiles (interquartile range) helps in assessing the spread of the
middle 50% of the data.
Identifying
Outliers: Percentiles can help identify outliers or
extreme values in a dataset. Values significantly higher or lower than
certain percentiles may be considered outliers.
Comparing Datasets: Percentiles
enable comparisons between different datasets, helping in understanding
differences in distribution and central tendency.
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b)What is Flash
Fill in Excel, and how does it help in data cleaning?
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Flash Fill is a feature
in Excel that helps automate and streamline the process of cleaning and
transforming data by recognizing patterns and automatically filling in
values based on those patterns. It allows you to extract, combine, or
format data quickly and efficiently without the need for complex formulas
or manual data manipulation.
Here's how Flash Fill
works and how it helps in data cleaning and analysis:
1.Automatic
Data Extraction: Flash Fill can
automatically extract specific parts of data from a column based on
patterns. For example, if you have a column containing full names, Flash
Fill can extract first names, last names, or other relevant information by
recognizing common patterns in the data.
2.Data
Formatting: It can automatically format
data based on specified patterns. For instance, if you have a column
containing dates in different formats (e.g., "MM/DD/YYYY" and
"YYYY-MM-DD"), Flash Fill can unify the format across the entire
column.
3.Data
Transformation: Flash Fill can perform
various data transformations, such as combining or splitting data. For
example, if you have separate columns for first names and last names, Flash
Fill can combine them into a single column containing full names.
4.Error
Correction: It can help identify and
correct errors in the data by recognizing inconsistencies and filling in
missing values based on surrounding data patterns.
5.Efficiency
and Time-saving: Flash Fill
significantly reduces the time and effort required for data cleaning and
manipulation tasks. Instead of writing complex formulas or performing
manual data cleaning procedures, you can simply demonstrate the desired
transformation to Excel, and Flash Fill will automatically apply the
transformation to the entire column.
Use
Flash Fill in Excel:
1.Enter the desired
transformation or pattern in a new column adjacent to the data you want to
clean or transform.
2.Excel will recognize the
pattern and suggest the remaining values in the column using Flash Fill.
3.Press Enter, or click the
Flash Fill button that appears next to the cell, to apply the suggested
values to the entire column.
4.Review
the results and make any necessary adjustments if Flash Fill didn't capture
the desired transformation accurately.
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Q6. Short
Notes Attempt any TWO out of THREE the following. [2X2=4]
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a)Types of data analysis techniques
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Data
analysis techniques can be broadly categorized into several types, each
serving different purposes and employing various methods to derive insights
from data. Here are the main types of data analysis techniques:
1.
Descriptive Analysis
Descriptive analysis focuses on summarizing and
describing the features of a dataset. It helps in understanding the current
state of data by providing simple summaries and visualizations.
Techniques:
Measures
of Central Tendency: Mean, median, mode
Measures
of Dispersion: Range, variance, standard deviation
Visualization:
Charts, histograms, bar graphs, pie charts
2.
Exploratory Data Analysis (EDA)
EDA involves analyzing data sets to summarize their
main characteristics, often with visual methods. It is used to uncover
patterns, spot anomalies, and test hypotheses.
Techniques:
●
Visualization: Scatter plots, box plots, heat maps
● Data
Profiling: Distribution analysis, correlation analysis
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Summary Statistics: Descriptive statistics to
understand data distributions
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b)Pivot table in
Excel
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A
PivotTable in Excel is a powerful tool used for summarizing, analyzing,
exploring, and presenting large amounts of data. PivotTables allow you to
transform data into meaningful insights with minimal effort. Here’s a brief
overview of how they work and their key features:
Creating a PivotTable
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Select Data: Highlight the range of data you want to
analyze.
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Insert PivotTable: Go to the "Insert" tab
and click on "PivotTable." You can choose to place the PivotTable
in a new worksheet or an existing one.
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Configure PivotTable: A PivotTable Field List will
appear on the right side of the Excel window.
Components of a PivotTable
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Rows: Drag fields here to display data vertically.
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Columns: Drag fields here to display data
horizontally.
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Values: Drag fields here to perform calculations
(sum, average, count, etc.) on the data.
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Filters: Drag fields here to filter the entire
PivotTable based on specific criteria.
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Data Summarization: Automatically sums, averages,
counts, or performs other calculations on data.
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Dynamic Updates: Automatically updates as the source
data changes.
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Sorting and Filtering: Easily sort and filter data
to find patterns and trends.
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Grouping: Group data into categories, such as
grouping dates into months or years.
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Drill Down: Double-click on summarized data to see
the underlying details.
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Calculated Fields: Create custom calculations using
existing data fields.
Example Use Case
Suppose
you have sales data with columns for "Date," "Region,"
"Product," and "Sales." You can create a PivotTable to:
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Display total sales by region and product.
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Filter sales data for specific regions or products.
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Show monthly or quarterly sales trends.
Steps to Create a Basic PivotTable
●Select
the Data Range: Highlight the data range you want to use for the
PivotTable.
●Insert PivotTable: Go to the "Insert" tab,
select "PivotTable," and choose where to place it.
●Add Fields: Drag fields from the PivotTable Field
List into the Rows, Columns, Values, and Filters areas.For example, drag
"Region" to Rows, "Product" to Columns, and
"Sales" to Values to see sales figures summarized by region and
product.
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c)Anomaly detection
in Excel
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Anomaly detection in
data analysis is the process of identifying unusual patterns or data points
that deviate significantly from the norm or expected behavior within a
dataset. These anomalies, also known as outliers, can indicate important,
actionable insights, such as errors, rare events, or novel information.
Anomaly detection is crucial in various fields like finance, healthcare,
cybersecurity, and manufacturing, where it helps in identifying fraud,
diagnosing diseases, detecting network intrusions, and spotting defects,
respectively.
Applications of Anomaly
Detection
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Fraud Detection:
Identifying fraudulent transactions in financial systems.
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Network Security:
Detecting intrusions or malicious activity in networks.
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Healthcare: Diagnosing
diseases by identifying abnormal medical test results.
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Manufacturing:
Detecting defects or faults in production lines.
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Marketing: Spotting
unusual customer behavior or market trends.
Steps
in Anomaly Detection Process
- Data Collection: Gather relevant and high-quality data.
- Data Preprocessing: Clean and prepare the data by
handling missing values, normalizing, and transforming data.
- Model Selection: Choose an appropriate anomaly
detection method based on the nature of the data and the specific
requirements.
- Model Training and Testing: Train the model using
historical data and validate its performance.
- Anomaly Detection: Apply the model to identify
anomalies in new or real-time data.
- Analysis and Action: Investigate the identified
anomalies and take necessary actions.
Key Aspects of Anomaly
Detection
Definition
of Anomalies:
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Point
Anomalies: Single data points that are significantly
different from the rest of the data.
●
Contextual
Anomalies: Data points that are
anomalous in a specific context but not necessarily in others (e.g., a high
sales figure that is normal during holiday seasons but unusual otherwise).
●
Collective
Anomalies: A set of data points that
collectively deviate from the norm, even if individual points are not
anomalous.
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Types
of Anomalies:
●
Global Anomalies:
Deviations from the entire dataset.
●
Local Anomalies:
Deviations from a localized or specific subset of the data.
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