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In this article, we will discuss how to do data analysis with Python. We will discuss all sorts of data analysis i.e. analyzing numerical data with NumPy, Tabular data with Pandas, data visualization Matplotlib, and Exploratory data analysis.

 Data Analysis With Python 

Data Analysis is the technique of collecting, transforming, and organizing data to make future predictions and informed data-driven decisions. It also helps to find possible solutions for a business problem. There are six steps for Data Analysis. They are: 

 
  • Ask or Specify Data Requirements
  • Prepare or Collect Data
  • Clean and Process
  • Analyze
  • Share
  • Act or Report

Analyzing Numerical Data with NumPy

NumPy is an array processing package in Python and provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python.

Arrays in NumPy

NumPy Array is a table of elements (usually numbers), all of the same types, indexed by a tuple of positive integers. In Numpy, the number of dimensions of the array is called the rank of the array. A tuple of integers giving the size of the array along each dimension is known as the shape of the array. 

Creating NumPy Array

NumPy arrays can be created in multiple ways, with various ranks. It can also be created with the use of different data types like lists, tuples, etc. The type of the resultant array is deduced from the type of elements in the sequences. NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation.

Create Array using numpy.empty(shape, dtype=float, order=’C’)

 
 

[GFGTABS]Python3

 
import numpy as np
 
b = np.empty(2, dtype = int)
print("Matrix b : 
", b)
 
a = np.empty([2, 2], dtype = int)
print("
Matrix a : 
", a)
 
c = np.empty([3, 3])
print("
Matrix c : 
", c)

[/GFGTABS]

Output:

Empty Matrix using pandas

Empty Matrix using pandas 

Create Array using numpy.zeros(shape, dtype = None, order = ‘C’)

[GFGTABS]Python3

import numpy as np
 
b = np.zeros(2, dtype = int)
print("Matrix b : 
", b)
 
a = np.zeros([2, 2], dtype = int)
print("
Matrix a : 
", a)
 
c = np.zeros([3, 3])
print("
Matrix c : 
", c)

[/GFGTABS]

 

Output:

Matrix b : 
 [0 0]

Matrix a : 
 [[0 0]
 [0 0]]

Matrix c : 
 [[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Operations on Numpy Arrays

Arithmetic Operations

  • Addition: 

[GFGTABS]Python3

import numpy as np

# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])

# Performing addition using arithmetic operator
add_ans = a+b
print(add_ans)

# Performing addition using numpy function
add_ans = np.add(a, b)
print(add_ans)

# The same functions and operations can be used for
# multiple matrices
c = np.array([1, 2, 3, 4])
add_ans = a+b+c
print(add_ans)

add_ans = np.add(a, b, c)
print(add_ans)

[/GFGTABS]

Output:

[  7  77  23 130]
[  7  77  23 130]
[  8  79  26 134]
[  7  77  23 130]
  • Subtraction:

[GFGTABS]Python3

import numpy as np

# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])

# Performing subtraction using arithmetic operator
sub_ans = a-b
print(sub_ans)

# Performing subtraction using numpy function
sub_ans = np.subtract(a, b)
print(sub_ans)

[/GFGTABS]

Output:

[ 3 67  3 70]
[ 3 67  3 70]
  • Multiplication:

[GFGTABS]Python3

import numpy as np

# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])

# Performing multiplication using arithmetic
# operator
mul_ans = a*b
print(mul_ans)

# Performing multiplication using numpy function
mul_ans = np.multiply(a, b)
print(mul_ans)

[/GFGTABS]

Output:

 
[  10  360  130 3000]
[  10  360  130 3000]
  • Division:

[GFGTABS]Python3

import numpy as np

# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])

# Performing division using arithmetic operators
div_ans = a/b
print(div_ans)

# Performing division using numpy functions
div_ans = np.divide(a, b)
print(div_ans)

[/GFGTABS]

Output:

[ 2.5        14.4         1.3         3.33333333]
[ 2.5        14.4         1.3         3.33333333]

For more information, refer to our NumPy – Arithmetic Operations Tutorial

NumPy Array Indexing

Indexing can be done in NumPy by using an array as an index. In the case of the slice, a view or shallow copy of the array is returned but in the index array, a copy of the original array is returned. Numpy arrays can be indexed with other arrays or any other sequence with the exception of tuples. The last element is indexed by -1 second last by -2 and so on.

Python NumPy Array Indexing

[GFGTABS]Python3

# Python program to demonstrate 
# the use of index arrays.
import numpy as np
 
# Create a sequence of integers from
# 10 to 1 with a step of -2
a = np.arange(10, 1, -2) 
print("
 A sequential array with a negative step: 
",a)
 
# Indexes are specified inside the np.array method.
newarr = a[np.array([3, 1, 2 ])]
print("
 Elements at these indices are:
",newarr)

[/GFGTABS]

Output:

A sequential array with a negative step: 
 [10  8  6  4  2]

 Elements at these indices are:
 [4 8 6]

NumPy Array Slicing

Consider the syntax x[obj] where x is the array and obj is the index. The slice object is the index in the case of basic slicing. Basic slicing occurs when obj is :

  • a slice object that is of the form start: stop: step
  • an integer
  • or a tuple of slice objects and integers

All arrays generated by basic slicing are always the view in the original array.

 

[GFGTABS]Python3

# Python program for basic slicing.
import numpy as np

# Arrange elements from 0 to 19
a = np.arange(20)
print("
 Array is:
 ",a)

# a[start:stop:step]
print("
 a[-8:17:1] = ",a[-8:17:1])

# The : operator means all elements till the end.
print("
 a[10:] = ",a[10:])

[/GFGTABS]

Output:

Array is:
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]

a[-8:17:1]  =  [12 13 14 15 16]

a[10:] = [10 11 12 13 14 15 16 17 18 19]

Ellipsis can also be used along with basic slicing. Ellipsis (…) is the number of : objects needed to make a selection tuple of the same length as the dimensions of the array.

[GFGTABS]Python3

# Python program for indexing using basic slicing with ellipsis
import numpy as np

# A 3 dimensional array.
b = np.array([[[1, 2, 3],[4, 5, 6]],
            [[7, 8, 9],[10, 11, 12]]])

print(b[...,1]) #Equivalent to b[: ,: ,1 ]

[/GFGTABS]

Output:

[[ 2  5]
 [ 8 11]]

NumPy Array Broadcasting

The term broadcasting refers to how numpy treats arrays with different Dimensions during arithmetic operations which lead to certain constraints, the smaller array is broadcast across the larger array so that they have compatible shapes. 

 

Let’s assume that we have a large data set, each datum is a list of parameters. In Numpy we have a 2-D array, where each row is a datum and the number of rows is the size of the data set. Suppose we want to apply some sort of scaling to all these data every parameter gets its own scaling factor or say Every parameter is multiplied by some factor.

Just to have a clear understanding, let’s count calories in foods using a macro-nutrient breakdown. Roughly put, the caloric parts of food are made of fats (9 calories per gram), protein (4 CPG), and carbs (4 CPG). So if we list some foods (our data), and for each food list its macro-nutrient breakdown (parameters), we can then multiply each nutrient by its caloric value (apply scaling) to compute the caloric breakdown of every food item.

 

NumPy Array Broadcasting

With this transformation, we can now compute all kinds of useful information. For example, what is the total number of calories present in some food or, given a breakdown of my dinner know how many calories did I get from protein and so on.

Let’s see a naive way of producing this computation with Numpy:

[GFGTABS]Python3

import numpy as np

macros = np.array([
[0.8, 2.9, 3.9],
[52.4, 23.6, 36.5],
[55.2, 31.7, 23.9],
[14.4, 11, 4.9]
])

# Create a new array filled with zeros,
# of the same shape as macros.
result = np.zeros_like(macros)

cal_per_macro = np.array([3, 3, 8])

# Now multiply each row of macros by
# cal_per_macro. In Numpy, `*` is
# element-wise multiplication between two arrays.
for i in range(macros.shape[0]):
    result[i, :] = macros[i, :] * cal_per_macro

result

[/GFGTABS]

Output:

array([[  2.4,   8.7,  31.2],
       [157.2,  70.8, 292. ],
       [165.6,  95.1, 191.2],
       [ 43.2,  33. ,  39.2]])

Broadcasting Rules: Broadcasting two arrays together follow these rules:

  • If the arrays don’t have the same rank then prepend the shape of the lower rank array with 1s until both shapes have the same length.
  • The two arrays are compatible in a dimension if they have the same size in the dimension or if one of the arrays has size 1 in that dimension.
  • The arrays can be broadcast together if they are compatible with all dimensions.
  • After broadcasting, each array behaves as if it had a shape equal to the element-wise maximum of shapes of the two input arrays.
  • In any dimension where one array had a size of 1 and the other array had a size greater than 1, the first array behaves as if it were copied along that dimension.

[GFGTABS]Python3

import numpy as np

v = np.array([12, 24, 36])
w = np.array([45, 55])

# To compute an outer product we first
# reshape v to a column vector of shape 3x1
# then broadcast it against w to yield an output
# of shape 3x2 which is the outer product of v and w
print(np.reshape(v, (3, 1)) * w)

X = np.array([[12, 22, 33], [45, 55, 66]])

# x has shape 2x3 and v has shape (3, )
# so they broadcast to 2x3,
print(X + v)

# Add a vector to each column of a matrix X has
# shape 2x3 and w has shape (2, ) If we transpose X
# then it has shape 3x2 and can be broadcast against w
# to yield a result of shape 3x2.

# Transposing this yields the final result
# of shape 2x3 which is the matrix.
print((X.T + w).T)

# Another solution is to reshape w to be a column
# vector of shape 2X1 we can then broadcast it
# directly against X to produce the same output.
print(X + np.reshape(w, (2, 1)))

# Multiply a matrix by a constant, X has shape 2x3.
# Numpy treats scalars as arrays of shape();
# these can be broadcast together to shape 2x3.
print(X * 2)

[/GFGTABS]

Output:

 
[[ 540  660]
 [1080 1320]
 [1620 1980]]
[[ 24  46  69]
 [ 57  79 102]]
[[ 57  67  78]
 [100 110 121]]
[[ 57  67  78]
 [100 110 121]]
[[ 24  44  66]
 [ 90 110 132]]

Analyzing Data Using Pandas

Python Pandas Is used for relational or labeled data and provides various data structures for manipulating such data and time series. This library is built on top of the NumPy library. This module is generally imported as:

import pandas as pd

Here, pd is referred to as an alias to the Pandas. However, it is not necessary to import the library using the alias, it just helps in writing less amount code every time a method or property is called. Pandas generally provide two data structures for manipulating data, They are: 

  • Series
  • Dataframe

Series: 

 

Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called indexes. Pandas Series is nothing but a column in an excel sheet. Labels need not be unique but must be a hashable type. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index.

It can be created using the Series() function by loading the dataset from the existing storage like SQL, Database, CSV Files, Excel Files, etc., or from data structures like lists, dictionaries, etc.

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Data analysis is an essential aspect of modern decision-making processes across various sectors, including business, healthcare, finance, and academia. As organizations generate massive amounts of data daily, understanding how to extract meaningful insights from this data becomes crucial. In this article, we will explore the fundamental concepts of data analysis, its types, significance, methods, and the tools used for effective analysis. We will also address common queries related to data analysis, providing clarity on its definition and applications in various fields.

Table of Content

  • What Do You Mean by Data Analysis?
  • Data Analysis Definition
  • Data Analysis in Data Science
  • Data Analysis in DBMS
  • Why Data Analysis is important?
  • The Process of Data Analysis
  • Analyzing Data: Techniques and Methods

What Do You Mean by Data Analysis?

In today’s data-driven world, organizations rely on data analysis to uncover patterns, trends, and relationships within their data. Whether it’s for optimizing operations, improving customer satisfaction, or forecasting future trends, effective data analysis helps stakeholders make informed decisions. The term data analysis refers to the systematic application of statistical and logical techniques to describe, summarize, and evaluate data. This process can involve transforming raw data into a more understandable format, identifying significant patterns, and drawing conclusions based on the findings.

When we ask, “What do you mean by data analysis?” it essentially refers to the practice of examining datasets to draw conclusions about the information they contain. The process can be broken down into several steps, including:

  1. Data Collection: Gathering relevant data from various sources, which could be databases, surveys, sensors, or web scraping.
  2. Data Cleaning: Identifying and correcting inaccuracies or inconsistencies in the data to ensure its quality and reliability.
  3. Data Transformation: Modifying data into a suitable format for analysis, which may involve normalization, aggregation, or creating new variables.
  4. Data Analysis: Applying statistical methods and algorithms to explore the data, identify trends, and extract meaningful insights.
  5. Data Interpretation: Translating the findings into actionable recommendations or conclusions that inform decision-making.

By employing these steps, organizations can transform raw data into a valuable asset that guides strategic planning and enhances operational efficiency.

To solidify our understanding, let’s define data analysis with an example. Imagine a retail company looking to improve its sales performance. The company collects data on customer purchases, demographics, and seasonal trends.

By conducting a data analysis, the company may discover that:

  • Customers aged 18-25 are more likely to purchase specific products during holiday seasons.
  • There is a significant increase in sales when promotional discounts are offered.

Based on these insights, the company can tailor its marketing strategies to target younger customers with specific promotions during peak seasons, ultimately leading to increased sales and customer satisfaction.

Data Analysis Definition

To further clarify the concept, let’s define data analysis in a more structured manner. Data analysis can be defined as:

“The process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.”

This definition emphasizes the systematic approach taken in analyzing data, highlighting the importance of not only obtaining insights but also ensuring the integrity and quality of the data used.

Data Analysis in Data Science

The field of data science relies heavily on data analysis to derive insights from large datasets. Data analysis in data science refers to the methods and processes used to manipulate data, identify trends, and generate predictive models that aid in decision-making.

Data scientists employ various analytical techniques, such as:

  • Statistical Analysis: Applying statistical tests to validate hypotheses or understand relationships between variables.
  • Machine Learning: Using algorithms to enable systems to learn from data patterns and make predictions.
  • Data Visualization: Creating graphical representations of data to facilitate understanding and communication of insights.

These techniques play a vital role in enabling organizations to leverage their data effectively, ensuring they remain competitive and responsive to market changes.

Data Analysis in DBMS

Another area where data analysis plays a crucial role is within Database Management Systems (DBMS). Data analysis in DBMS involves querying and manipulating data stored in databases to extract meaningful insights. Analysts utilize SQL (Structured Query Language) to perform operations such as:

  • Data Retrieval: Extracting specific data from large datasets using queries.
  • Aggregation: Summarizing data to provide insights at a higher level.
  • Filtering: Narrowing down data to focus on specific criteria.

Understanding how to perform effective data analysis in DBMS is essential for professionals who work with databases regularly, as it allows them to derive insights that can influence business strategies.

Why Data Analysis is important?

Data analysis is crucial for informed decision-making, revealing patterns, trends, and insights within datasets. It enhances strategic planning, identifies opportunities and challenges, improves efficiency, and fosters a deeper understanding of complex phenomena across various industries and fields.

  1. Informed Decision-Making: Analysis of data provides a basis for informed decision-making by offering insights into past performance, current trends, and potential future outcomes.
  2. Business Intelligence: Analyzed data helps organizations gain a competitive edge by identifying market trends, customer preferences, and areas for improvement.
  3. Problem Solving: It aids in identifying and solving problems within a system or process by revealing patterns or anomalies that require attention.
  4. Performance Evaluation: Analysis of data enables the assessment of performance metrics, allowing organizations to measure success, identify areas for improvement, and set realistic goals.
  5. Risk Management: Understanding patterns in data helps in predicting and managing risks, allowing organizations to mitigate potential challenges.
  6. Optimizing Processes: Data analysis identifies inefficiencies in processes, allowing for optimization and cost reduction.

The Process of Data Analysis

A Data analysis has the ability to transform raw available data into meaningful insights for your business and your decision-making. While there are several different ways of collecting and interpreting this data, most data-analysis processes follow the same six general steps.

  1. Define Objectives and Questions: Clearly define the goals of the analysis and the specific questions you aim to answer. Establish a clear understanding of what insights or decisions the analyzed data should inform.
  2. Data Collection: Gather relevant data from various sources. Ensure data integrity, quality, and completeness. Organize the data in a format suitable for analysis. There are two types of data: qualititative and quantitative data.
  3. Data Cleaning and Preprocessing: Address missing values, handle outliers, and transform the data into a usable format. Cleaning and preprocessing steps are crucial for ensuring the accuracy and reliability of the analysis.
  4. Exploratory Data Analysis (EDA): Conduct exploratory analysis to understand the characteristics of the data. Visualize distributions, identify patterns, and calculate summary statistics. EDA helps in formulating hypotheses and refining the analysis approach.
  5. Statistical Analysis or Modeling: Apply appropriate statistical methods or modeling techniques to answer the defined questions. This step involves testing hypotheses, building predictive models, or performing any analysis required to derive meaningful insights from the data.
  6. Interpretation and Communication: Interpret the results in the context of the original objectives. Communicate findings through reports, visualizations, or presentations. Clearly articulate insights, conclusions, and recommendations based on the analysis to support informed decision-making.

Analyzing Data: Techniques and Methods

When discussing analyzing data, several methods can be employed depending on the nature of the data and the questions being addressed. These methods can be broadly categorized into three types:

There are various data analysis methods, each tailored to specific goals and types of data. The major Data Analysis methods are:

1. Descriptive Analysis

A Descriptive Analysis is foundational as it provides the necessary insights into past performance. Understanding what has happened is crucial for making informed decisions in data analysis. For instance, data analysis in data science often begins with descriptive techniques to summarize and visualize data trends.

2. Diagnostic Analysis

Diagnostic analysis works hand in hand with Descriptive Analysis. As descriptive Analysis finds out what happened in the past, diagnostic Analysis, on the other hand, finds out why did that happen or what measures were taken at that time, or how frequently it has happened. By analyzing data thoroughly, businesses can address the question, “what do you mean by data analysis?” They can assess what factors contributed to specific outcomes, providing a clearer picture of their operational efficiency and effectiveness.

3. Predictive Analysis

By forecasting future trends based on historical data, Predictive analysis predictive analysis enables organizations to prepare for upcoming opportunities and challenges. This analysis type answers the inquiry of what is data science analysis by leveraging data trends to predict future behaviors and trends. This capability is vital for strategic planning and risk management in business operations. 

4. Prescriptive Analysis

Prescriptive Analysis is an advanced method that takes Predictive Analysis insights and offers actionable recommendations, guiding decision-makers toward the best course of action. It extends beyond merely analyzing data to suggesting optimal solutions based on potential future scenarios, thus addressing the need for a structured approach to decision-making.

5. Statistical Analysis

Statistical Analysis is essential for summarizing data, helping in identifying key characteristics and understanding relationships within datasets. This analysis can reveal significant patterns that inform broader strategies and policies, thereby allowing analysts to provide a robust review of data analytics practices within an organization.

6. Regression Analysis

Regression analysis is a statistical method extensively used in data analysis to model the relationship between a dependent variable and one or more independent variables. This method is particularly useful in establishing the relationship between variables, making it vital for forecasting and strategic planning, as analysts often define data analysis with examples that utilize regression techniques to illustrate these concepts.

7. Cohort Analysis

By examining specific groups over time, cohort analysis aids in understanding customer behavior and improving retention strategies. This approach allows businesses to tailor their services to different segments, thereby effectively utilizing data storage and analysis in big data to enhance customer engagement and satisfaction.

8. Time Series Analysis

Time series analysis is crucial for any domain where data points are collected over time, allowing for trend identification and forecasting. Businesses can utilize this method to analyze seasonal trends and predict future sales, addressing the question of what do you understand by data analysis in the context of temporal data.

9. Factor Analysis

Factor analysis is a statistical method that explores underlying relationships among a set of observed variables. It identifies latent factors that contribute to observed patterns, simplifying complex data structures. This technique is invaluable in reducing dimensionality, revealing hidden patterns, and aiding in the interpretation of large datasets.

10. Text Analysis

Text analysis involves extracting valuable information from unstructured textual data. Utilizing natural language processing and machine learning techniques, it enables the extraction of sentiments, key themes, and patterns within large volumes of text. analyze customer feedback, social media sentiment, and more, showcasing the practical applications of analyzing data in real-world scenarios.

Tools for Data Analysis

Several tools are available to facilitate effective data analysis. These tools can range from simple spreadsheet applications to complex statistical software. Some popular tools include:

  • SAS :SAS was a programming language developed by the SAS Institute for performed advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. , SAS was developed for very specific uses and powerful tools are not added every day to the extensive already existing collection thus making it less scalable for certain applications.
  • Microsoft Excel :It is an important spreadsheet application that can be useful for recording expenses, charting data, and performing easy manipulation and lookup and or generating pivot tables to provide the desired summarized reports of large datasets that contain significant data findings. It is written in C#, C++, and .NET Framework, and its stable version was released in 2016.
  • R :It is one of the leading programming languages for performing complex statistical computations and graphics. It is a free and open-source language that can be run on various UNIX platforms, Windows, and macOS. It also has a command-line interface that is easy to use. However, it is tough to learn especially for people who do not have prior knowledge about programming.
  • Python: It is a powerful high-level programming language that is used for general-purpose programming. Python supports both structured and functional programming methods. Its extensive collection of libraries make it very useful in data analysis. Knowledge of Tensorflow, Theano, Keras, Matplotlib, Scikit-learn, and Keras can get you a lot closer to your dream of becoming a machine learning engineer.
  • Tableau Public: Tableau Public is free software developed by the public company “Tableau Software” that allows users to connect to any spreadsheet or file and create interactive data visualizations. It can also be used to create maps, dashboards along with real-time updation for easy presentation on the web. The results can be shared through social media sites or directly with the client making it very convenient to use.
  • Knime :Knime, the Konstanz Information Miner is a free and open-source data analytics software. It is also used as a reporting and integration platform. It involves the integration of various components for Machine Learning and data mining through the modular data-pipe lining. It is written in Java and developed by KNIME.com AG. It can be operated in various operating systems such as Linux, OS X, and Windows.
  • Power BI: A business analytics service that provides interactive visualizations and business intelligence capabilities with a simple interface.

Conclusion

In conclusion, data analysis is a vital process that involves examining, cleaning, transforming, and modeling data to extract meaningful insights that drive decision-making. With the vast amounts of data generated daily, organizations must harness the power of data analysis to remain competitive and responsive to market trends.

Understanding the different types of data analysis, the tools available, and the methods employed in this field is essential for professionals aiming to leverage data effectively. As we move further into the digital age, the significance of data analysis will continue to grow, shaping the future of industries and influencing strategic decisions across the globe.

Data Analysis- FAQs

What is the definition of data analysis in data science?

The define data analysis in data science refers to the methodology of collecting, processing, and analyzing data to generate insights and support data-driven decisions within the field of data science.

What is Data Analysis Examples?

To define data analysis with an example, consider a retail company analyzing sales data to identify trends in customer purchasing behavior. This can involve descriptive analysis to summarize past sales and predictive analysis to forecast future trends based on historical data.

How to do data analysis in Excel?

Import data into Excel, use functions for summarizing and visualizing data. Utilize PivotTables, charts, and Excel’s built-in analysis tools for insights and trends.

How does data storage and analysis work in big data?

Data storage and analysis in big data involves utilizing technologies that manage and analyze vast amounts of structured and unstructured data. This enables organizations to derive meaningful insights from large datasets, driving strategic decision-making.

What is computer data analysis?

Computer data analysis refers to the use of computer software and algorithms to perform data analysis. This method streamlines the process, allowing for efficient handling of large datasets and complex analyses.

Where can I find a review of data analytics?

A review of data analytics can be found on various platforms, including academic journals, industry reports, and websites like Geeks for Geeks that provide comprehensive insights into data analytics practices and technologies.

What are the benefits of data analysis?

The benefits of data analysis include improved decision-making, enhanced operational efficiency, better customer insights, and the ability to identify market trends. Organizations that leverage data analysis gain a competitive advantage by making informed choices.

Authors: T. C. Okenna
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