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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); } } }

Output

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)

Output

[1, 3, 4, 7, 9]

Next, you’ll learn about Bisection Method

Summary:

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.

You're reading Heap Data Structure: What Is Heap? Min & Max Heap (Example)

## What Is Capital Structure? Most Important Points

In this article, we will discuss the gist of the firm’s capital structure.

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The discount rate is a function of the risk inherent in any business and industry, the uncertainty regarding the projected cash flows, and the assumed capital structure. In general, discount rates vary across different businesses and industries. The greater the uncertainty about the projected cash stream, the higher the appropriate discount rate and the lower the current value of the cash stream.

Extract the Capital Structure from Annual Report

We require the proportion of Equity and Debt in the capital structure to calculate the discount rate using our ABC example. For the capital structure calculations, the annual reports of ABC have provided us with the following information on Debt and Equity related items from the footnotes.

The capitalization table of ABC company is as per below.

Understanding the Capital Structure of the Firm Short-Term Borrowings

Short-term borrowings are an account shown in the current liabilities portion of a company’s balance sheet. This account comprises any debt incurred by a company due within one year. A company typically takes out short-term bank loans to make up the debt in this account. ABC needs to pay \$5.2 million within one year and the interest (coupon) of 3.2%.

Revolver

Revolving credit is a type of credit that does not have a fixed number of payments, in contrast to installment credit. Examples of revolving credits used by consumers include credit cards or overdraft facilities. Corporate revolving credit facilities are typically useful to provide liquidity for a company’s day-to-day operations. In the context of company ABC, they have a pre-approved loan facility of up to \$30 million. However, ABC has drawn only \$14.2 from the bank.

Typical Characteristics of Revolver Loan

The borrower may use or withdraw funds up to a pre-approved credit limit.

The available credit decreases and increases as funds are borrowed and repaid.

The credit may be used repeatedly.

The borrower makes payments based only on the amount they’ve actually used or withdrawn, plus interest.

The borrower may repay over time (subject to any minimum payment requirement) or in full at any time.

In some cases, the borrower needs to pay a fee to the lender for any money that is undrawn on the revolver; this is especially true of corporate bank loan revolving credit facilities.

Bonds

The authorized issuer is obligated to pay the holders a debt in a bond and, depending on the bond terms, must pay interest (the coupon) and/or repay the principal at a later date, termed maturity. A bond is a formal contract to repay borrowed money with interest at fixed intervals. Company ABC has taken a loan of \$80 million, out of which ABC needs to repay the amortizing portion of the bond i.e. principal repayment of \$12 million within a year.

Long-term = \$80 – \$12 = \$68 (maturity of more than one year)

Short Term = \$ 12 million (amortizing portion, principal repayment)

Convertible Bonds

A convertible bond is a type of bond that the holder can convert into shares of common stock in the issuing company or cash of equal value at an agreed-upon price. It is a hybrid security with debt- and equity-like features. Although it typically has a low coupon rate, the instrument carries additional value through the option to convert the bond to stock, thereby participating in further growth in the company’s equity value. The investor receives the potential upside of conversion into equity while protecting the downside with cash flow from the coupon payments. In ABC, convertible bonds have a face value of \$100 and a coupon rate of 4.5% (interest expense). The conversion price is \$25, which implies each bond converts into 4 shares.

Straight Preferred Stocks

Preferred stock, also called preferred shares, is a special equity security that resembles properties of an equity and a debt instrument and is generally a hybrid instrument. Preferred Stocks are senior (i.e. higher ranking) to common stock but are subordinate to bonds.

This stock usually carries no voting rights but may carry priority over common stock in the payment of dividends and upon liquidation. Preferred stock may carry a dividend before any dividends are paid to common stockholders.

Cumulative versus Non-cumulative Preferred Stocks

Preferred stock can either be cumulative or noncumulative. A cumulative preferred stock requires that if a company fails to pay any dividend or amount below the stated rate, it must make up for it later. Dividends accumulate with each passed dividend period, which can be quarterly, semi-annually, or annually. When a company fails to declare a dividend in time, it is said to have “passed” the dividend, and all passed dividends on a cumulative stock become dividends in arrears. Investors refer to a stock that lacks this feature as a noncumulative or straight preferred stock, where any passed dividends are permanently lost if not declared.

Convertible Preferred Stocks

In ABC, the preferred stock’s face value (FV) is \$18. Each preferred stock converts to one ordinary share at a conversion price of \$20.

The key to getting WACC correct is to get the capital structure right. Hence, we need to classify our capitalization table from the perspective of Debt and Equity.

Summary of Classification as Debt and Equity

What Next?

In this article, we have understood the capital structure of the firm. In our next article, we will understand convertible features. Till then, Happy Learning!

Recommended Articles

Here are some articles that will help you to get more detail about the Detailed Capital Structure, so just go through the link.

## What Is Statistical Data Analysis?

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 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.

AcaStat

IBM SPSS

IHS Markit EViews

MathWorks MATLAB

MaxStat

Minitab

SAP

SAS Institute

StataCorp Stata

TIBCO Spotfire

## Introduction To Big O Notation In Data Structure

Introduction

One of the most essential mathematical notations in computer science for determining an algorithm’s effectiveness is the Big O notation. The length of time, memory, other resources, as well as a change in input size required to run an algorithm can all be used to evaluate how effective it is. Data structure’s Big O Notation provides information about an algorithm’s performance under various conditions. In other words, it provides the worst-case complexity or upper-bound runtime of an algorithm.

Big O Notation in Data Structure

A change in input size can affect how well an algorithm performs. Asymptotic notations, such as Big O notation, are useful in this situation. When the input goes toward a particular or limiting value, asymptotic notations can be used to represent how long an algorithm will run.

Algebraic terms are used to indicate algorithmic complexity using the Big O Notation within data structures. It determines the time and memory required to run an algorithm for a given input value and represents the upper bound of an algorithm’s runtime.

A mathematical notation called Big O is named after the phrase “order of the function,” which refers to the growth of functions. It is a member of the Asymptotic Notations family and is also known as Landau’s Symbol.

Mathematical Explanation

Consider the functions f(n) & g(n), where f and g have unbounded definitions on the collection of positive real numbers. Every big value of n has a strict positive value for g(n).

The following can be written:

Where n goes to infinity (n ), f(n) = O(g(n)).

The expression above can be expressed succinctly as:

f(n) = O(g(n)).

Analysis of Algorithm

The following describes the general step-by-step process for Big-O runtime analysis

Determine the input and what n stands for.

Describe the algorithm’s highest limit of operations in terms of n.

Remove all but the terms with the highest order.

Eliminate all the consistent elements.

The following are some of the Big-O notation analysis’s beneficial characteristics

If f(n) = f1(n) + f2(n) + — + FM(n) and fi(n) fi+1(n) i=1, 2, –, m, then the Summation Function is: Hence, O(f(n)) = O(max(f1(n), f2(n), -, fm(n))

If f(n) = log an and g(n) = log bn, then the Logarithmic Function is O(f(n)) = O(g(n)) .

If f(n) = g(n), then

f(n) = a0 + a1.n + a2.n2 + — + chúng tôi if polynomial function, then O(f(n)) = O(nm) (nm).

We must compute and analyze the very worst runtime complexities of an algorithm to evaluate and assess its performance. The quickest runtime for an algorithm is O(1), also known as Constant Running Time, and it takes the same amount of time regardless of the quantity of the input. Despite being the optimal runtime for an algorithm, Constant Running Time is rarely achieved because the duration relies on the size of n inputs.

Examples of typical algorithms with high runtime complexity

Linear Search Runtime Complexity: O (n)

Binary Search Runtime Complexity – O (log n)

Bubble sorting, insertion sorting, selection sorting, and bucket sorting have runtime complexity of O(nc).

Exponential algorithms like the Tower of Hanoi have runtime complexity of O(cn).

Heap Sort and Merge Sort Runtime Complexity in O (n log n).

Analyzing Space Complexity

Determining an algorithm’s space complexity is also crucial. This is because the space complexity of an algorithm shows how much memory it requires. We contrast the algorithm’s worst-case space complexities. Functions are categorized using the Big O notation according to how quickly they expand; many functions with the same rate of growth could be written using the same notation.

Since a function’s order is also referred to as its development rate, the symbol O is used. A function’s development rate is typically only constrained by the upper bound in a large O notation representation of the function.

The following actions must be taken first before Big O notation may analyze the Space complexity

Program implementation for a specific algorithm.

It is necessary to know the amount of input n to determine how much storage every item will hold.

Some Typical Algorithms’ Space Complexity

Space Complexity is O for linear search, binary search, bubble sort, selection sort, heap sort, and insertion sort (1).

Space complexity for the radix sort is O(n+k).

Space complexity for quick sorting is O (n).

Space complexity for a merge sort is O (log n).

Let us Explore Some Examples:

void

linearTimeComplex

(

int

a

[],

int

s

)

{

for

(

int

i

=

0

;

i

<

s

;

i

++)

{

}

}

This function executes in O(n) time, sometimes known as “linear time,” where n is just the array’s size in items. We must print 10 times if the array contains 10 elements. We must print 1000 times if there are 1000 items and the complexity we get is O(n).

void

(

int

a

[],

int

s

)

{

for

(

int

i

=

0

;

i

<

s

;

i

++)

{

for

(

int

j

=

0

;

j

<

s

;

j

++)

{

}

}

}

We are layering two loops here. When there are n items in our array, the outer loop iterates n times, the inner loop iterates n times for every iteration of an outer loop, and the result is n2 total prints. We must print 100 times if the array contains 10 elements. We must print 1000000 times if there are 1000 items. So, this function takes O(n2) time to complete, and we get complexity as O(n^2).

void

constTimeComplex

(

int

a

[])

{

printf

(

"First array element = %d"

,

a

[

0

]);

}

In relation to its input, this function executes in O(1) time, sometimes known as “constant time.” There need only be one step for this method, regardless of whether the input array contains 1 item or 1,000 things.

Conclusion

Big O Notation is particularly helpful in understanding algorithms if we work with big data. The tool helps programmers to determine the scalability of an algorithm or count the steps necessary to produce outputs based on the data the programme utilizes. If users are attempting to run our code to increase its efficiency, the Big O Notation in Data Structures can be particularly helpful.

## 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.

Challenges

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.

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.

EndNote

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!

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.

Related

## 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.

Conclusion

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.

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.

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