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Statistical data analysis does more work for your business intelligence (BI) than most other types of data analysis. 

Also known as descriptive analysis, statistical data analysis is a wide range of quantitative research practices in which you collect and analyze categorical data to find meaningful patterns and trends. 

Statistical data analysis is often applied to survey responses and observational data, but it can be applied to many other business metrics as well. 

See below to learn more about statistical data analysis and the tools that help you to get the most out of your data: 

See more: What is Data Analysis?

Before you get started with statistical data analysis, you need two pieces in place: 1) a collection of raw data that you want to statistically analyze and 2) a predetermined method of analysis. 

Depending on the data you’re working with, the results you want, and how it is being presented, you may want to choose either of these two types of analysis:

Descriptive statistics: datadoesn’t mean much on its own, and the sheer quantity can be overwhelming to digest. Descriptive statistical analysis focuses on creating a basic visual description of the data, or turning information into graphs, charts, and other visuals that help people understand the meaning of the values in the data set. Descriptive analysis isn’t about explaining or drawing conclusions, though. It is only the practice of digesting and summarizing raw data, so it can be better understood.

This type of statistical analysis is all about visuals. Rawdatadoesn’t mean much on its own, and the sheer quantity can be overwhelming to digest. Descriptive statistical analysis focuses on creating a basic visual description of the data, or turning information into graphs, charts, and other visuals that help people understand the meaning of the values in the data set. Descriptive analysis isn’t about explaining or drawing conclusions, though. It is only the practice of digesting and summarizing raw data, so it can be better understood.

Statistical inference:

Inferential statistics practices involve more upfront hypothesis and follow-up explanation than descriptive statistics. In this type of statistical analysis, you are less focused on the entire collection of raw data and instead take a sample and test your hypothesis or first estimation. From this sample and the results of your experiment, you can use inferential statistics to infer conclusions about the rest of the data set.

Every company has several key performance indicators (KPIs) to judge overall performance, and statistical data analysis is the primary strategy for finding those accurate metrics. For internal, or team metrics, you’ll want to measure data like associated deals and revenue, hours worked, trainings completed, and other meaningful numerical values. It’s easy to collect this data, but to make meaning of it, you’ll want to statistically analyze the data to assess the performance of individuals, teams, and the company. Statistically analyzing your team is important, not only because it helps you to hold them accountable, but also because it ensures their performance is measured by unbiased numerical standards rather than opinions. 

If your organization sells products or services, you should use statistical analysis often to check in on sales performance as well as to predict future outcomes and areas of weakness. Here are a few areas of statistical data analysis that keep your business practices sharp:

Competitive analysis:

Statistical analysis illuminates your objective value as a company. More importantly, knowing common metrics like sales revenue and net profit margin allows you to compare your performance to competitors.

True sales visibility:

Your salespeople say they are having a good week and their numbers look

good, but how can you accurately measure their impact on sales numbers? With statistical data analysis, you can easily measure sales data and associate it with specific timeframes, products, and individual salespeople, which gives you better visibility on your marketing and sales successes.

Predictive analytics:

One of the most crucial applications of statistical data analysis, predictive analytics allow you to use past numerical data to predict future outcomes and areas where your team should make adjustments to improve performance.

See more: What is Raw Data?

In virtually any situation where you see raw quantitative and qualitative data in combination, you can apply statistical analysis to learn more about the data set’s value and predictive outcomes. Statistical analysis can be performed manually or through basic formulas in your database, but most companies work with statistical data analysis software to get the most out of their information. 

A couple of customers of top statistical data analysis software have also highlighted other uses they found in the software’s modules:

“[TIBCO Spotfire is a] very versatile and user friendly software that allows you to deploy results quickly, on the fly even. Data transparency and business efficiency is improved tremendously, without the need for an extensive training program or course. On the job is the best way to learn using it, figuring problems out with the aid of the community page and stackoverflow, and if all else fails there are committed consultancies that can sit with you and work out complex business needs, from which you will gain another level of understanding of the software onto which you can build further. We use this software not only for data analytics, but also for data browsing and data management, creating whole data portals for all disciplines in the business.”

-data scientist in the energy industry, review from

Gartner Peer Insights

“Although not a new tool, [IBM] SPSS is the best (or sometimes the only) tool to effectively analyze market research surveys

response level data. our team has explored many other solutions but nothing comes close…We conduct many consumer surveys. we need to analyze individual respondents, along with their individual responses or answers to each question

which creates an unlimited number of scenarios. SPSS is flexible enough for us to get answers to questions we may not have predicted at the beginning of a project.”

-senior manager of consumer insights and analytics in the retail industry, review from

Gartner Peer Insights

See more: Qualitative vs. Quantitative Data

The market for statistical analysis software hit $51.52 billion in 2023 and is expected to grow to $60.41 billion by 2027, growing at a steady annual rate of 2.3% between 2023 and 2027, according to Precision Reports. Statistical analysis software is used across industries like education, health care, retail, pharmaceuticals, finance, and others that work with a large amount of quantitative data. Companies of all sizes implement this kind of software, but most of the latest implementations come from individuals and small-to-medium enterprises (SMEs), Precision Reports says.

Are you curious about the different statistical data analysis tools on the market? Looking for a new solution to replace your current approach? Check out these top statistical data analysis tools or use this Data Analysis Platform Selection Tool from TechnologyAdvice to guide your search.



IHS Markit EViews

MathWorks MATLAB




SAS Institute

StataCorp Stata

TIBCO Spotfire

You're reading What Is Statistical Data Analysis?

What Is Big Data? Introduction, Uses, And Applications.

This article was published as a part of the Data Science Blogathon.

What is Big Data?

Big data is exactly what the name suggests, a “big” amount of data. Big Data means a data set that is large in terms of volume and is more complex. Because of the large volume and higher complexity of Big Data, traditional data processing software cannot handle it. Big Data simply means datasets containing a large amount of diverse data, both structured as well as unstructured.

Big Data allows companies to address issues they are facing in their business, and solve these problems effectively using Big Data Analytics. Companies try to identify patterns and draw insights from this sea of data so that it can be acted upon to solve the problem(s) at hand.

Although companies have been collecting a huge amount of data for decades, the concept of Big Data only gained popularity in the early-mid 2000s. Corporations realized the amount of data that was being collected on a daily basis, and the importance of using this data effectively.

5Vs of Big Data

Volume refers to the amount of data that is being collected. The data could be structured or unstructured.

Velocity refers to the rate at which data is coming in.

Variety refers to the different kinds of data (data types, formats, etc.) that is coming in for analysis. Over the last few years, 2 additional Vs of data have also emerged – value and veracity.

Value refers to the usefulness of the collected data.

Veracity refers to the quality of data that is coming in from different sources.

How Does Big Data Work?

Big Data helps corporations in making better and faster decisions, because they have more information available to solve problems, and have more data to test their hypothesis on.

Customer Experience Machine Learning

Machine Learning is another field that has benefited greatly from the increasing popularity of Big Data. More data means we have larger datasets to train our ML models, and a more trained model (generally) results in a better performance. Also, with the help of Machine Learning, we are now able to automate tasks that were earlier being done manually, all thanks to Big Data.

Demand Forecasting

Demand forecasting has become more accurate with more and more data being collected about customer purchases. This helps companies build forecasting models, that help them forecast future demand, and scale production accordingly. It helps companies, especially those in manufacturing businesses, to reduce the cost of storing unsold inventory in warehouses.

Big data also has extensive use in applications such as product development and fraud detection.

How to Store and Process Big Data?

The volume and velocity of Big Data can be huge, which makes it almost impossible to store it in traditional data warehouses. Although some and sensitive information can be stored on company premises, for most of the data, companies have to opt for cloud storage or Hadoop.

Cloud storage allows businesses to store their data on the internet with the help of a cloud service provider (like Amazon Web Services, Microsoft Azure, or Google Cloud Platform) who takes the responsibility of managing and storing the data. The data can be accessed easily and quickly with an API.

Hadoop also does the same thing, by giving you the ability to store and process large amounts of data at once. Hadoop is an open-source software framework and is free. It allows users to process large datasets across clusters of computers.

Apache Hadoop is an open-source big data tool designed to store and process large amounts of data across multiple servers. Hadoop comprises a distributed file system (HDFS) and a MapReduce processing engine.

Apache Spark is a fast and general-purpose cluster computing system that supports in-memory processing to speed up iterative algorithms. Spark can be used for batch processing, real-time stream processing, machine learning, graph processing, and SQL queries.

Apache Cassandra is a distributed NoSQL database management system designed to handle large amounts of data across commodity servers with high availability and fault tolerance.

Apache Flink is an open-source streaming data processing framework that supports batch processing, real-time stream processing, and event-driven applications. Flink provides low-latency, high-throughput data processing with fault tolerance and scalability.

Apache Kafka is a distributed streaming platform that enables the publishing and subscribing to streams of records in real-time. Kafka is used for building real-time data pipelines and streaming applications.

Splunk is a software platform used for searching, monitoring, and analyzing machine-generated big data in real-time. Splunk collects and indexes data from various sources and provides insights into operational and business intelligence.

Talend is an open-source data integration platform that enables organizations to extract, transform, and load (ETL) data from various sources into target systems. Talend supports big data technologies such as Hadoop, Spark, Hive, Pig, and HBase.

Tableau is a data visualization and business intelligence tool that allows users to analyze and share data using interactive dashboards, reports, and charts. Tableau supports big data platforms and databases such as Hadoop, Amazon Redshift, and Google BigQuery.

Apache NiFi is a data flow management tool used for automating the movement of data between systems. NiFi supports big data technologies such as Hadoop, Spark, and Kafka and provides real-time data processing and analytics.

QlikView is a business intelligence and data visualization tool that enables users to analyze and share data using interactive dashboards, reports, and charts. QlikView supports big data platforms such as Hadoop, and provides real-time data processing and analytics.

Big Data Best Practices

To effectively manage and utilize big data, organizations should follow some best practices:

Define clear business objectives: Organizations should define clear business objectives while collecting and analyzing big data. This can help avoid wasting time and resources on irrelevant data.

Collect and store relevant data only: It is important to collect and store only the relevant data that is required for analysis. This can help reduce data storage costs and improve data processing efficiency.

Ensure data quality: It is critical to ensure data quality by removing errors, inconsistencies, and duplicates from the data before storage and processing.

Use appropriate tools and technologies: Organizations must use appropriate tools and technologies for collecting, storing, processing, and analyzing big data. This includes specialized software, hardware, and cloud-based technologies.

Establish data security and privacy policies: Big data often contains sensitive information, and therefore organizations must establish rigorous data security and privacy policies to protect this data from unauthorized access or misuse.

Leverage machine learning and artificial intelligence: Machine learning and artificial intelligence can be used to identify patterns and predict future trends in big data. Organizations must leverage these technologies to gain actionable insights from their data.

Focus on data visualization: Data visualization can simplify complex data into intuitive visual formats such as graphs or charts, making it easier for decision-makers to understand and act upon the insights derived from big data.


1. Data Growth

Managing datasets having terabytes of information can be a big challenge for companies. As datasets grow in size, storing them not only becomes a challenge but also becomes an expensive affair for companies.

To overcome this, companies are now starting to pay attention to data compression and de-duplication. Data compression reduces the number of bits that the data needs, resulting in a reduction in space being consumed. Data de-duplication is the process of making sure duplicate and unwanted data does not reside in our database.

2. Data Security

Data security is often prioritized quite low in the Big Data workflow, which can backfire at times. With such a large amount of data being collected, security challenges are bound to come up sooner or later.

Mining of sensitive information, fake data generation, and lack of cryptographic protection (encryption) are some of the challenges businesses face when trying to adopt Big Data techniques.

Companies need to understand the importance of data security, and need to prioritize it. To help them, there are professional Big Data consultants nowadays, that help businesses move from traditional data storage and analysis methods to Big Data.

3. Data Integration

Data is coming in from a lot of different sources (social media applications, emails, customer verification documents, survey forms, etc.). It often becomes a very big operational challenge for companies to combine and reconcile all of this data.

There are several Big Data solution vendors that offer ETL (Extract, Transform, Load) and data integration solutions to companies that are trying to overcome data integration problems. There are also several APIs that have already been built to tackle issues related to data integration.

Advantages of Big Data

Improved decision-making: Big data can provide insights and patterns that help organizations make more informed decisions.

Increased efficiency: Big data analytics can help organizations identify inefficiencies in their operations and improve processes to reduce costs.

Better customer targeting: By analyzing customer data, businesses can develop targeted marketing campaigns that are relevant to individual customers, resulting in better customer engagement and loyalty.

New revenue streams: Big data can uncover new business opportunities, enabling organizations to create new products and services that meet market demand.

Privacy concerns: Collecting and storing large amounts of data can raise privacy concerns, particularly if the data includes sensitive personal information.

Risk of data breaches: Big data increases the risk of data breaches, leading to loss of confidential data and negative publicity for the organization.

Technical challenges: Managing and processing large volumes of data requires specialized technologies and skilled personnel, which can be expensive and time-consuming.

Difficulty in integrating data sources: Integrating data from multiple sources can be challenging, particularly if the data is unstructured or stored in different formats.

Complexity of analysis: Analyzing large datasets can be complex and time-consuming, requiring specialized skills and expertise.

Implementation Across Industries 

Here are top 10 industries that use big data in their favor – 

IndustryUse of Big dataHealthcareAnalyze patient data to improve healthcare outcomes, identify trends and patterns, and develop personalized treatmentRetailTrack and analyze customer data to personalize marketing campaigns, improve inventory management and enhance CXFinanceDetect fraud, assess risks and make informed investment decisionsManufacturingOptimize supply chain processes, reduce costs and improve product quality through predictive maintenanceTransportationOptimize routes, improve fleet management and enhance safety by predicting accidents before they happenEnergyMonitor and analyze energy usage patterns, optimize production, and reduce waste through predictive analyticsTelecommunicationsManage network traffic, improve service quality, and reduce downtime through predictive maintenance and outage predictionGovernment and publicAddress issues such as preventing crime, improving traffic management, and predicting natural disastersAdvertising and marketingUnderstand consumer behavior, target specific audiences and measure the effectiveness of campaignsEducationPersonalize learning experiences, monitor student progress and improve teaching methods through adaptive learning

The Future of Big Data

The volume of data being produced every day is continuously increasing, with increasing digitization. More and more businesses are starting to shift from traditional data storage and analysis methods to cloud solutions. Companies are starting to realize the importance of data. All of these imply one thing, the future of Big Data looks promising! It will change the way businesses operate, and decisions are made.


In this article, we discussed what we mean by Big Data, structured and unstructured data, some real-world applications of Big Data, and how we can store and process Big Data using cloud platforms and Hadoop. If you are interested in learning more about big data uses, sign-up for our Blackbelt Plus program. Get your personalized career roadmap, master all the skills you lack with the help of a mentor and solve complex projects with expert guidance. Enroll Today!

Frequently Asked Questions

Q1. What is big data in simple words?

A. Big data refers to the large volume of structured and unstructured data that is generated by individuals, organizations, and machines.

Q2. What is big data in example?

A. An example of big data would be analyzing the vast amounts of data collected from social media platforms like Facebook or Twitter to identify customer sentiment towards a particular product or service.

Q3. What are the 3 types of big data?

A. The three types of big data are structured data, unstructured data, and semi-structured data.

Q4. What is big data used for?

A. Big data is used for a variety of purposes such as improving business operations, understanding customer behavior, predicting future trends, and developing new products or services, among others.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 


What Is The Difference Between Data Science And Machine Learning?

Introduction  Data Science vs Machine Learning

AspectData Science Machine Learning DefinitionA multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.A subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that allow computer systems to learn and make predictions or decisions without being explicitly programmed.ScopeBroader scope, encompassing various stages of the data lifecycle, including data collection, cleaning, analysis, visualization, and interpretation.Narrower focus on developing algorithms and models that enable machines to learn from data and make predictions or decisions.GoalExtract insights, patterns, and knowledge from data to solve complex problems and make data-driven decisions.Develop models and algorithms that enable machines to learn from data and improve performance on specific tasks automatically.TechniquesIncorporates various techniques and tools, including statistics, data mining, data visualization, machine learning, and deep learning.Primarily focused on the application of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.ApplicationsData science is applied in various domains, such as healthcare, finance, marketing, social sciences, and more.Machine learning finds applications in recommendation systems, natural language processing, computer vision, fraud detection, autonomous vehicles, and many other areas.

What is Data Science? 

Source: DevOps School

What is Machine Learning? 

Computers can now learn without being explicitly programmed, thanks to the field of study known as machine learning. Machine learning uses algorithms to process data without human intervention and become trained to make predictions. The set of instructions, the data, or the observations are the inputs for machine learning. The use of machine learning is widespread among businesses like Facebook, Google, etc. 

Data Scientist vs Machine Learning Engineer

While data scientists focus on extracting insights from data to drive business decisions, machine learning engineers are responsible for developing the algorithms and programs that enable machines to learn and improve autonomously. Understanding the distinctions between these roles is crucial for anyone considering a career in the field.

Data ScientistMachine Learning EngineerExpertiseSpecializes in transforming raw data into valuable insightsFocuses on developing algorithms and programs for machine learningSkillsProficient in data mining, machine learning, and statisticsProficient in algorithmic codingApplicationsUsed in various sectors such as e-commerce, healthcare, and moreDevelops systems like self-driving cars and personalized newsfeedsFocusAnalyzing data and deriving business insightsEnabling machines to exhibit independent behaviorRoleTransforms data into actionable intelligenceDevelops algorithms for machines to learn and improve

What are the Similarities Between Data Science and Machine Learning?

When we talk about Data Science vs Machine Learning, Data Science and Machine Learning are closely related fields with several similarities. Here are some key similarities between Data Science and Machine Learning:

1. Data-driven approach: Data Science and Machine Learning are centered around using data to gain insights and make informed decisions. They rely on analyzing and interpreting large volumes of data to extract meaningful patterns and knowledge.

2. Common goal: The ultimate goal of both Data Science and Machine Learning is to derive valuable insights and predictions from data. They aim to solve complex problems, make accurate predictions, and uncover hidden patterns or relationships in data.

3. Statistical foundation: Both fields rely on statistical techniques and methods to analyze and model data. Probability theory, hypothesis testing, regression analysis, and other statistical tools are commonly used in Data Science and Machine Learning.

4. Feature engineering: In both Data Science and Machine Learning, feature engineering plays a crucial role. It involves selecting, transforming, and creating relevant features from the raw data to improve the performance and accuracy of models. Data scientists and machine learning practitioners often spend significant time on this step.

5. Data preprocessing: Data preprocessing is essential in both Data Science and Machine Learning. It involves cleaning and transforming raw data, handling missing values, dealing with outliers, and standardizing or normalizing data. Proper data preprocessing helps to improve the quality and reliability of models.

Where is Machine Learning Used in Data Science?

In Data Science vs Machine Learning, the skills required for ML Engineer vs Data Scientist are quite similar. 

Skills Required to Become Data Scientist

Exceptional Python, R, SAS, or Scala programming skills

SQL database coding expertise

Familiarity with machine learning algorithms

Knowledge of statistics at a deep level

Skills in data cleaning, mining, and visualization

Knowledge of how to use big data tools like Hadoop.

Skills Needed for the Machine Learning Engineer

Working knowledge of machine learning algorithms

Processing natural language

Python or R programming skills are required

Understanding of probability and statistics

Understanding of data interpretation and modeling.

Source: AltexSoft

Data Science vs Machine Learning – Career Options

There are many career options available for Data Science vs Machine Learning.

Careers in Data Science

Data scientists: They create better judgments for businesses by using data to comprehend and explain the phenomena surrounding them.

Data analysts: Data analysts collect, purge, and analyze data sets to assist in resolving business issues.

Data Architect: Build systems that gather, handle, and transform unstructured data into knowledge for data scientists and business analysts.

Business intelligence analyst: To build databases and execute solutions to store and manage data, a data architect reviews and analyzes an organization’s data infrastructure.

Source: ZaranTech

Careers in Machine Learning

Machine learning engineer: Engineers specializing in machine learning conduct research, develop, and design the AI that powers machine learning and maintains or enhances AI systems.

AI engineer: Building the infrastructure for the development and implementation of AI.

Cloud engineer: Builds and maintains cloud infrastructure as a cloud engineer.

Computational linguist: Develop and design computers that address how human language functions as a computational linguist.

Human-centered AI systems designer: Design, create, and implement AI systems that can learn from and adapt to humans to enhance systems and society.

Source: LinkedIn


Data Science and Machine Learning are closely related yet distinct fields. While they share common skills and concepts, understanding the nuances between them is vital for individuals pursuing careers in these domains and organizations aiming to leverage their benefits effectively. To delve deeper into the comparison of Data Science vs Machine Learning and enhance your understanding, consider joining Analytics Vidhya’s Blackbelt Plus Program.

The program offers valuable resources such as weekly mentorship calls, enabling students to engage with experienced mentors who provide guidance on their data science journey. Moreover, participants get the opportunity to work on industry projects under the guidance of experts. The program takes a personalized approach by offering tailored recommendations based on each student’s unique needs and goals. Sign-up today to know more.

Frequently Asked Questions

Q1. What is the main difference between Data Science and Machine Learning?

A. The main difference lies in their scope and focus. Data Science is a broader field that encompasses various techniques for extracting insights from data, including but not limited to Machine Learning. On the other hand, Machine Learning is a specific subset of Data Science that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions.

Q2. Are the skills required for Data Science and Machine Learning the same?

A. While there is some overlap in the skills required, there are also distinct differences. Data Scientists need strong statistical knowledge, programming skills, data manipulation skills, and domain expertise. In addition to these skills, Machine Learning Engineers require expertise in implementing and optimizing machine learning algorithms and models.

Q3. What is the role of a Data Scientist?

A. The role of a Data Scientist involves collecting and analyzing data, extracting insights, building statistical models, developing data-driven strategies, and communicating findings to stakeholders. They use various tools and techniques, including Machine Learning, to uncover patterns and make data-driven decisions.

Q4. What is the role of a Machine Learning Engineer?

A. Machine Learning Engineers focus on developing and implementing machine learning algorithms and models. They work on tasks such as data preprocessing, feature engineering, model selection, training and tuning models, and deploying them in production systems. They collaborate with Data Scientists and Software Engineers to integrate machine learning solutions into applications.


Heap Data Structure: What Is Heap? Min & Max Heap (Example)

What is a Heap?

Heap is a specialized tree data structure. The heap comprises the topmost node called a root (parent). Its second child is the root’s left child, while the third node is the root’s right child. The successive nodes are filled from left to right. The parent-node key compares to that of its offspring, and a proper arrangement occurs. The tree is easy to visualize where each entity is called a node. The node has unique keys for identification.

Why do you need Heap Data Structure?

Here are the main reasons for using Heap Data Structure:

The heap data structure allows deletion and insertion in logarithmic time – O(log2n).

The data in the tree is fashioned in a particular order. Besides updating or querying things such as a maximum or minimum, the programmer can find relationships between the parent and the offspring.

You can apply the concept of the Document Object Model to assist you in understanding the heap data structure.

Types of Heaps

Heap data structure has various algorithms for handling insertions and removing elements in a heap data structure, including Priority-Queue, Binary-Heap, Binomial Heap, and Heap-Sort.

Priority-Queue: It is an abstract data structure containing prioritized objects. Each object or item has a priority pre-arranged for it. Therefore, the object or item assigned higher priority is getting the service before the rest.

Binary-Heap: Binary heaps are suitable for simple heap operations such as deletions and insertions.

Binomial-Heap: A binomial heap consists of a series of collections of binomial trees that make up the heap. Binomial Heap tree is no ordinary tree as it is rigorously defined. The total number of elements in a binomial tree always possess 2n nodes.

Heap-Sort: Unlike most sorting algorithms, heap-sort uses O(1) space for its sort operation. It’s a comparison-based sorting algorithm where sorting occurs in increasing order by first turning it into a max heap. You can look at a Heapsort as an upgraded quality binary search tree.

Typically, a heap data structure employs two strategies. For input 12 – 8 – 4 – 2 and 1

Min Heap – least value at the top

Max Heap – highest value at the top

Min Heap

In the Min Heap structure, the root node has a value either equal to or smaller than the children on that node. This heap node of a Min Heap holds the minimum value. All in all, its min-heap structure is a complete binary tree.

Once you have a Min heap in a tree, all the leaves are viable candidates. However, you need to examine each of the leaves in order to get the exact Max-heap value.

Min Heap Example

In the diagrams above, you can notice some clear sequence from the root to the lowest node.

Suppose you store the elements in Array Array_N[12,2,8,1,4]. As you can see from the array, the root element is violating the Min Heap priority. To maintain the Min heap property, you have to perform the min-heapify operations to swap the elements until the Min heap properties are met.

Max Heap

In Max Heap’s structure, the parent or root node has a value equal to or larger than its children in the node. This node holds the maximum value. Moreover, it’s a complete binary tree, so you can build a max heap from a collection of values and run it on O(n) time.

Here are a few methods for implementing a java max heap

Add (): place a new element into a heap. If you use an array, the objects are added at the end of the array, while in the binary tree, the objects are added from top to bottom and then after left to right.

Remove (): This method allows you to remove the first element from the array list. As the newly added element is no longer the largest, the Sift-Down method always pushes it to its new location.

Sift-Down (): This method compares a root object to its child and then pushes the newly added node to its rightful position.

Sift-Up (): if you use the array method to add a newly inserted element to an array, then the Sift-Up method helps the newly added node relocate to its new position. The newly inserted item is first compared to the parent by simulating the tree data structure.

Apply formula Parent_Index=Child_Index/2. You continue doing this until the maximum element is at the front of the array.

Basic Heap Operations

For you to find the highest and lowest values in a set of data, you need lots of basic heap operations such as find, delete, and insert. Because elements will constantly come and go, you have to:

Find – Look for an item in a heap.

Insert – Add a new child into the heap.

Delete – Delete a node from a heap.

Create Heaps

The process of constructing heaps is known as creating heaps. Given a list of keys, the programmer makes an empty heap and then inserts other keys one at a time using basic heap operations.

So let’s begin building a Min-heap using Willaim’s method by inserting values 12,2,8,1 and 4 in a heap. You can build the heap with n elements by starting with an empty heap and then filling it successively with other elements using O (nlogn) time.

Heapify: in insertion algorithm, which helps insert elements into a heap. Checks, whether the property heap data structure highlighted, are followed.

For instance, a max heapify would check if the value of the parent is greater than its offspring. The elements can then be sorted using methods like swapping.

Merge: Considering you have two heaps to combine into one, use merge heaps to bring the values from the two heaps together. However, the original heaps are still preserved.

Inspect Heaps

Inspecting Heaps refers to checking the number of elements in the heap data structure and validating whether the heap is empty.

It is important to inspect heaps as sorting or queueing of elements. Checking if you have elements to process using Is-Empty() is important. The heap size will help locate the max-heap or min-heap. So, you need to know the elements following the heap property.

Size – returns the magnitude or length of the heap. You can tell how many elements are in sorted order.

Is-empty – if the heap is NULL, it returns TRUE otherwise, it returns FALSE.

Here, you are printing all elements in the priorityQ loop and then checking that priorityQ is not empty.

While (!priorityQ.isEmpty()) { System.out.print(priorityQ.poll()+" ");

Uses of Heap Data Structure

Heap data structure is useful in many programming applications in real life like:

Helps in Spam Filtering.

Implementing graph algorithms.

Operating System load balancing, and data compression.

Find the order in the statistics.

Implement Priority queues where you can search for items in a list in logarithmic time.

Heap data structure also use for sorting.

Simulating customers on a line.

Interrupt handling in Operating System.

In Huffman’s coding for data compression.

Heap Priority Queue Properties

In priority heaps, the data items in the list are compared to each other to determine the smaller element.

An element is placed in a queue and afterward removed.

Every single element in the Priority Queue has a unique number related to it identified as a priority.

Upon exiting a Priority Queue, the top priority element exits first.

Steps for implementing the heap Priority Queue in Java

Heap Sort in JAVA with Code Example import java.util.Arrays; public class HeapSort { public static void main(String[] args) { int[] arr = {5, 9, 3, 1, 8, 6}; heapSort(arr); System.out.println(Arrays.toString(arr)); } public static void heapSort(int[] arr) { heapify(arr, arr.length, i); } int temp = arr[0]; arr[0] = arr[i]; arr[i] = temp; heapify(arr, i, 0); } } public static void heapify(int[] arr, int n, int i) { int largest = i; int left = 2 * i + 1; int right = 2 * i + 2; largest = left; } largest = right; } if (largest != i) { int temp = arr[i]; arr[i] = arr[largest]; arr[largest] = temp; heapify(arr, n, largest); } } }


Original Array: 5 9 3 1 8 6 Heap after insertion: 9 8 6 1 5 3 Heap after sorting: 1 3 5 6 8 9 Heap Sort in Python with Code Example def heap_sort(arr): """ Sorts an array in ascending order using heap sort algorithm. Parameters: arr (list): The array to be sorted. Returns: list: The sorted array. """ n = len(arr) # Build a max heap from the array for i in range(n heapify(arr, n, i) # Extract elements from the heap one by one for i in range(n - 1, 0, -1): arr[0], arr[i] = arr[i], arr[0] # swap the root with the last element heapify(arr, i, 0) # heapify the reduced heap return arr def heapify(arr, n, i): """ Heapifies a subtree with the root at index i in the given array. Parameters: arr (list): The array containing the subtree to be heapified. n (int): The size of the subtree. i (int): The root index of the subtree. """ largest = i # initialize largest as the root left = 2 * i + 1 # left child index right = 2 * i + 2 # right child index # If left child is larger than root largest = left # If right child is larger than largest so far largest = right # If largest is not root if largest != i: arr[i], arr[largest] = ( arr[largest], arr[i], ) # swap the root with the largest element heapify(arr, n, largest) # recursively heapify the affected subtree arr = [4, 1, 3, 9, 7] sorted_arr = heap_sort(arr) print(sorted_arr)


[1, 3, 4, 7, 9]

Next, you’ll learn about Bisection Method


Heap is a specialized tree data structure. Let’s imagine a family tree with its parents and children.

The heaps data structure in Java allows deletion and insertion in logarithmic time – O(log2n).

Heaps in Python has various algorithms for handling insertions and removing elements in a heap data structure, including Priority-Queue, Binary-Heap, Binomial Heap, and Heapsort.

In the Min Heap structure, the root node has a value equal to or smaller than the children on that node.

In Max Heap’s structure, the root node (parent) has a value equal to or larger than its children in the node.

Inspecting Heaps refers to checking the number of elements in the heap data structure and validating whether the heap is empty.

What Is The Best Way To Get Stock Data Using Python?

In this article, we will learn the best way to get stock data using Python.

The yfinance Python library will be used to retrieve current and historical stock market price data from Yahoo Finance.

Installation of Yahoo Finance(yfinance)

One of the best platforms for acquiring Stock market data is Yahoo Finance. Just download the dataset from the Yahoo Finance website and access it using yfinance library and Python programming.

You can install yfinance with the help of pip, all you have to do is open up command prompt and type the following command show in syntax:

Syntax pip install yfinance

The best part about yfinance library is, its free to use and no API key is required for it

How to get current data of Stock Prices

We need to find a ticker of the stock Which we can use for data extraction. we will show the current market price and the previous close price for GOOGL in the following example.


The following program returns the market price value,previous close price value,ticker value using yfinance module −

import yfinance as yf ticker = yf.Ticker('GOOGL').info marketPrice = ticker['regularMarketPrice'] previousClosePrice = ticker['regularMarketPreviousClose'] print('Ticker Value: GOOGL') print('Market Price Value:', marketPrice) print('Previous Close Price Value:', previousClosePrice) Output

On executing, the above program will generate the following output −

Ticker Value: GOOGL Market Price Value: 92.83 Previous Close Price Value: 93.71 How to get Historical data of Stock Prices

By giving the start date, end date, and ticker, we can obtain full historical price data.


The following program returns the stock price data between the start and end dates −

# importing the yfinance package import yfinance as yf # giving the start and end dates startDate = '2023-03-01' endDate = '2023-03-01' # setting the ticker value ticker = 'GOOGL' # downloading the data of the ticker value between # the start and end dates resultData =, startDate, endDate) # printing the last 5 rows of the data print(resultData.tail()) Output

On executing, the above program will generate the following output −

[*********************100%***********************] 1 of 1 completed Open High Low Close Adj Close Volume Date 2023-02-22 42.400002 42.689499 42.335499 42.568001 42.568001 24488000 2023-02-23 42.554001 42.631001 42.125000 42.549999 42.549999 27734000 2023-02-24 42.382500 42.417999 42.147999 42.390499 42.390499 26924000 2023-02-27 42.247501 42.533501 42.150501 42.483501 42.483501 20236000 2023-02-28 42.367500 42.441502 42.071999 42.246498 42.246498 27662000

The above example will retrieve data of stock price dated from 2023-03-01 to 2023-03-01.

If you want to pull data from several tickers at the same time, provide the tickers as a space-separated string.

Transforming Data for Analysis

Date is the dataset’s index rather than a column in the example above dataset. You must convert this index into a column before performing any data analysis on it. Here’s how to do it −


The following program adds the column names to the stock data between the start and end date −

import yfinance as yf # giving the start and end dates startDate = '2023-03-01' endDate = '2023-03-01' # setting the ticker value ticker = 'GOOGL' # downloading the data of the ticker value between # the start and end dates resultData =, startDate, endDate) # Setting date as index resultData["Date"] = resultData.index # Giving column names resultData = resultData[["Date", "Open", "High","Low", "Close", "Adj Close", "Volume"]] # Resetting the index values resultData.reset_index(drop=True, inplace=True) # getting the first 5 rows of the data print(resultData.head()) Output

On executing, the above program will generate the following output −

[*********************100%***********************] 1 of 1 completed Date Open High Low Close Adj Close Volume 0 2023-03-02 28.350000 28.799500 28.157499 28.750999 28.750999 50406000 1 2023-03-03 28.817499 29.042500 28.525000 28.939501 28.939501 50526000 2 2023-03-04 28.848499 29.081499 28.625999 28.916500 28.916500 37964000 3 2023-03-05 28.981001 29.160000 28.911501 29.071501 29.071501 35918000 4 2023-03-06 29.100000 29.139000 28.603001 28.645000 28.645000 37592000

The above converted data and data we acquired from Yahoo Finance are identical

Storing the Obtained Data in a CSV File

The to_csv() method can be used to export a DataFrame object to a CSV chúng tôi following code will help you export the data in a CSV file as the above-converted data is already in the pandas dataframe.

# importing yfinance module with an alias name import yfinance as yf # giving the start and end dates startDate = '2023-03-01' endDate = '2023-03-01' # setting the ticker value ticker = 'GOOGL' # downloading the data of the ticker value between # the start and end dates resultData =, startDate, endDate) # printing the last 5 rows of the data print(resultData.tail()) # exporting/converting the above data to a CSV file resultData.to_csv("outputGOOGL.csv") Output

On executing, the above program will generate the following output −

[*********************100%***********************] 1 of 1 completed Open High Low Close Adj Close Volume Date 2023-02-22 42.400002 42.689499 42.335499 42.568001 42.568001 24488000 2023-02-23 42.554001 42.631001 42.125000 42.549999 42.549999 27734000 2023-02-24 42.382500 42.417999 42.147999 42.390499 42.390499 26924000 2023-02-27 42.247501 42.533501 42.150501 42.483501 42.483501 20236000 2023-02-28 42.367500 42.441502 42.071999 42.246498 42.246498 27662000 Visualizing the Data

The yfinance Python module is one of the easiest to set up, collect data from, and perform data analysis activities with. Using packages such as Matplotlib, Seaborn, or Bokeh, you may visualize the results and capture insights.

You can even use PyScript to display these visualizations directly on a webpage.


In this article, we learned how to use the Python yfinance module to obtain the best stock data. Additionally, we learned how to obtain all stock data for the specified periods, how to do data analysis by adding custom indexes and columns, and how to convert this data to a CSV file.

Dogecoin Analysis – Is Doge A Good Investment In July 2023?

Dogecoin did what no other cryptocurrency had done before

Prior to December 2013, no one in the history of mankind had built a currency based on a dog. And not just any dog. But a Japanese Shiba Inu hunting dog named Kusama.

If you’ve had an Internet connection in the past 10 years, you can’t have missed out on the ‘doge’ meme featuring Kusama and typically surrounded by Comic Sans phrases like ‘wow’, ‘what r u doing?’, ‘so scare’, ‘concern’ and ‘keep your hands away from me’. 

Two software engineers – Billy Markus and Jackson Palmer – decided to satirise wild speculation in the 2013 crypto market by creating Dogecoin. The DOGE token had zero utility and an unlimited supply. It was the most wild-card currency ever invented.

Not even the founders believed Dogecoin had a future. 

As of July 2023, Dogecoin is the tenth largest cryptocurrency by market cap ($8 billion) and is very serious business. The top 100 Dogecoin wallets are all worth over $6 million – the top five Dogecoin whales have wallets worth over $60 million. 

Even during a bear market in July 2023 the price of Dogecoin is still 78,000% above its all-time lows. Had you bought $100 of the joke when no one else was interested, you would have a wallet worth $78,000 today. 

The question today: is Dogecoin still a good investment in July 2023? 

Read on to find out. 

What is Dogecoin (DOGE)?

Following the launch of Bitcoin in 2009 – and the Bitcoin Pizza Day in May 22, 2010 – there was an explosion of new cryptocurrencies all vying for investor attention.

(Bitcoin Pizza Day refers to the first documented pricing of Bitcoin, when a Florida man paid 10,000 BTC for two pizzas.)

Many new cryptocurrencies had zero utility. But the possibility for developers to build new tokens, with their own blockchain network, led Jackson Palmer and Billy Markus to make fun of the trend with Dogecoin. 

Against the odds, Dogecoin became an instant fan favourite. 

Dogecoin started off as a currency for tipping on Reddit. But then, a group of Dogecoin supporters raised more than $30,000 in 2014 to help send the Jamaican bobsleigh team to the Winter Olympics in Sochi. The official Dogecoin Foundation has continued to promote charitable endeavours, turning DOGE into an acronym for Do Only Good Everyday. 

The CEO of Tesla and SpaceX, Elon Musk, became interested in Dogecoin in 2023 and began writing Tweets in support of the decentralised meme-coin. Musk’s appearance on Saturday Night Live in early May 2023 triggered a huge price rally that took Dogecoin to $0.7376 – the DOGE all-time high.

The news grabbed headlines around the world. Even more news media became interested when Glauber Contessoto (aka SlumDOGE Millionaire) revealed in April of 2023 he’d become the first Dogecoin millionaire after investing his life savings – $250,000 – in DOGE back in February.  

Are Cryptocurrencies Still Founded on Memes in 2023?

Dogecoin was the first meme-coin – but it was far from the last.

Shiba Inu is the next most famous meme-coin and currently sits at 14th place in the cryptocurrency market cap rankings. There’s also ApeCoin, which loosely references the popular BAYC NFT collection, as well as Baby Doge Coin, Dogelon Mars, Pitbull, MonaCoin, Samoyedcoin, and hundreds more. 

But success is not a given for meme-coins – and in many occasions, they are either flash-in-the-pan hypes or outright scams.

One of the newest cryptocurrencies to take the market by storm is EverGrow Coin. The EGC token has become hugely popular as one of the newest cryptos that’s most likely to explode in 2023.

While the meme-coin market is saturated, EverGrow Coin is leading a new breed of reflection tokens. EverGrow Coin charges a 14% transaction tax and its investors are rewarded with 8% of that, every day, in the BUSD stablecoin.

The popularity of the token – and the $37 million paid out in BUSD stablecoin rewards since launching in late 2023 – are making many analysts believe that 2023 is the year where a reflection token will become the next top 20 cryptocurrency. 

5 Reasons why Dogecoin is a Good Investment in July 2023 1. Dogecoin is Decentralised

Dogecoin dipped under the radar before Elon Musk brought it back into the spotlight in 2023. 

Since then, many of Dogecoin’s original developers returned to improve the Dogecoin code and support the global adoption of DOGE. Key to Dogecoin’s success is that anyone can mine DOGE without requiring complex equipment.

Decentralisation also has seen hundreds of Dogecoin-supporting projects spring up around the world. The Dogecoin Foundation aims to bring Dogecoin closer to the 1.7 billion unbanked people in the world, through projects like Gigawallet and RadioDoge. 

2. Doge is a Fan Favourite 

Dogecoin will also remain the first meme-coin in cryptocurrency.

Every new meme-coin project will have to do something increasingly extraordinary to take DOGE’s spot – and no one has flipped its market cap to date. 

Dogecoin has a huge community with almost 5 million Dogecoin wallets across the world. Dogecoin is the go-to meme-coin for any interested cryptocurrency investor and – crucially – is readily available to buy on most leading exchanges and platforms. 

The backing from Elon Musk and investor Mark Cuban have bolstered the Dogecoin community, and will continue to do until the end of 2023 and beyond. 

3. Dogecoin Has its Own Blockchain Network 

Unlike Shiba Inu and Baby Doge Coin, Dogecoin has its own blockchain network. 

Transactions on the Dogecoin network are low-cost and far lower than the cost of sending Bitcoin or Ethereum. The blockchain platform is decentralised and protects the uncensored, cryptographic, globally transferable and scalable features that made crypto a success. 

Furthermore, Dogecoin code continues to be updated by software engineers who are also invested in the network by mining or running DOGE-related projects. Even if the Dogecoin Foundation and other central parties lose interest in Dogecoin, that won’t stop others from stepping in to take up the torch.

4. Merchant adoption 

Dogecoin is one of the most widely accepted cryptocurrencies by merchants.

Dogecoin is accepted by the BitPay payment service provider, which powers more than big-name 250 companies and stores and thousands more small to medium-sized eCommerce and shopfront businesses.

You can use Dogecoin in shops and marketplaces, to buy Internet services, crypto services, business services, web development services, in gaming, for tourism, traveling and renting as well as to buy from big-name fashion outlets from Gucci and even buy Porsches and real estate from certain US-based companies.

Elon Musk recently announced his Boring Company will accept Dogecoin as payment for rides on its Las Vegas transit system, Loop.

5. Dogecoin accumulation in July 2023

Despite the low Dogecoin prices in July 2023, the largest wallets are continuing to buy up DOGE and add to their totals.

Blockchain auditor @WhaleStats reported a huge uptick in Dogecoin accumulation in the first two weeks of July. In particular, Dogecoin was among the top 10 buys for BSC (Binance Smart Chain) whales – one BSC whale alone bought more than 18 million DOGE in July ($1.25 million).

While whales control a large amount of the Dogecoin supple, tracking their movements is key to predicting how DOGE will fair in 2023. At present in July 2023, things are looking positive.

3 Reasons why Dogecoin is not a Good Investment in July 2023 1. Dogecoin is Inflationary 

Dogecoin has an unlimited coin supply, with around 5 billion DOGE entering circulation each year (4% of current supply).

There is nothing in the Dogecoin code to stop inflation. This can have the effect of driving the Dogecoin price down unless accumulation continues at rates faster than inflation. This is completely different to cryptocurrencies like Bitcoin, which have a fixed supply.

2. Dogecoin Depends on Popularity 

Dogecoin has zero utility. If big names like Elon Musk, Mark Cuban or the Dogecoin Foundation were to pull out of DOGE this could drastically affect the price.

Dogecoin has already seen its develop teams abandon the project – and even the Dogecoin co-founder Jackson Palmer has made public statements with that crypto was ‘over’, calling it a ‘ponzi’ scheme designed to make money from uninformed investors. 

If prices fall, developers could leave the project in swathes. This would dent further market adoption and create a vicious cycle.

3. Watch out for Dogecoin Pump and Dumps 

As high as the Dogecoin highs have, so has DOGE regularly plummeted to lows. 

Dogecoin is currently 92% down from the all-time high of $0.7376 last year. Meanwhile, Bitcoin is down 70% from the all-time high, and Ethereum is down 78%. 

The top 20 individuals whales (i.e. not including exchange wallets) control more than 10% of the total Dogecoin supply. While there are only 60 wallets with more than $10 million worth of Dogecoin, there are 2.5 million wallets holding between $1 to $100 worth of DOGE.

With so much Dogecoin held in just a few hands, the chances of whales pumping and dumping is likely. 

How Do Other Cryptocurrencies Address Dogecoin’s Problems?

Inflation was a problem tackled by Bitcoin, with its fixed supply at 21 million, back in 2009.

But many newer cryptocurrencies go even further than having a fixed supply to become hyper-deflationary. These tokens actually have a decreasing supply, which tends to increase prices in the short and long term. 

EverGrow Coin, with 53% of its initial supply already burned, is a great example.

The EverGrow Coin 14% transaction tax sees 2% set aside for strategic buyback and burn. When prices are low, the EverGrow Coin core development team use the buyback & burn fund to destroy EGC tokens, by sending them to a dead wallet.

EverGrow Coin is committed to being one the most transparent token in crypto – you can view the burn wallet on BSC Scan.

The core development team in EverGrow Coin also regularly publish their wallets, to prove they are not selling anything. Instead, they earn salaries from BUSD rewards like every other investor. The 14% transaction tax also discourages selling because a significant proportion is inevitably redistributed among all existing investors.

EverGrow Coin also has a whale tax which limits the order size to discourage any pump and dump activity.

Dogecoin Price in July 2023 – is it a Good Buy?

Dogecoin is trading at a price range between $0.06 and $0.072 in July 2023.

Dogecoin prices are 65% down from the beginning of 2023, when DOGE had a value of $0.17. While prices are low currently, the lowest price of the year was $0.049 on June 18th.

The macroeconomic environment is volatile with CPI data at record highs, and the chance of the US economy going into a technical recession when Q2 GDP data is revealed. Interest rates are also expected to continue rising to battle inflation – each of these can suppress crypto prices in the short term.

That said, this summer is likely to see the bottom of the crypto market and the return to rising prices. So if you want to buy Dogecoin in 2023, July is likely the best month of the year to buy DOGE. 

But the problems of inflation, pump & dump and popularity mean no one should go all in without significant risk to their portfolio.

A much wiser choice is to diversify investments, with Dogecoin, other major cryptocurrencies as well as small-market cap cryptocurrencies most likely to explode in 2023. 

In the latter case, EverGrow Coin with a market cap of just $60 million – and built-in mechanisms which resolve many of Dogecoin’s problems already in place – is a great asset to any cryptocurrency investor’s portfolio.

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