Introduction

Modern technologies such as artificial intelligence, machine learning, data science, and Big Data have become the phrases everyone talks about, but no one fully understands them. To a layman, they seem very complex. All these words resemble a business executive or a student from a non-technical background. People are often confused by words such as AI, ML, and data science.

People are often confused about using technology for growing their business. With a plethora of technologies available and rise and shine of data science in recent times, the decision makes individuals & companies face the consent dilemma of whether to choose big data or ML or data science which can boost their businesses. In this blog, we will understand different concepts and have a look at this problem.

Let us understand key terms first i.e data science, machine learning, and big data

What is Data Science

Data science is the umbrella under which all these terminologies take the shelter. Data science is a like a complete subject which has different stages within itself. Suppose a retailer wants to forecast the sales of an X item present in its inventory in the coming month. This is a business problem and data science aims to provide optimal solutions for the same.

Data science enables us to solve this business problem with a series of well-defined steps.

1. 1. Collecting data
   2. Pre-processing data
   3. Analyzing data
   4. Driving insights and generating BI report
   5. Taking insight-bases decisions

Generally, these are the steps we mostly follow to solve a business problem. All the terminologies related to data science falls under different steps which we are going to understand just in a while. Different terminologies fall under different steps listed above.

You can learn more about the different component in data science from here

If you want to learn data science online then follow the link here

What is Big Data

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.

Characteristics Of ‘Big Data’

Volume — The name ‘Big Data’ itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one characteristic which needs to be considered while dealing with ‘Big Data’.

Variety — The next aspect of ‘Big Data’ is its variety. Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data. Nowadays, analysis applications use data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured-data poses certain issues for storage, mining and analyzing data.

Velocity — The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous.

Variability — This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.

If you are looking to learn Big Data online then follow the link here

What is Machine Learning

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.

Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is a spam, or recognizing speech accurately enough to generate captions for a YouTube video.

The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the banana and the apple.

Instead, a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.

You can read more on how to be an expert in AI from here

The relationship between Data Science, Machine learning and Big Data

Data science is a complete journey of solving a problem using data at hand wheres Big data and machine learning are tools for the data scientists. It helps them to perform some specific tasks. While, Machine learning is more around making predictions using data present at hand whereas Big data emphasis on all the techniques that can be used to analyze a large set of data(thousands of petabytes may be, to begin with)

Let us understand in detail the difference between machine learning and Big Data

Big Data Analytics vs Machine Learning

You will find both similarities and differences when you compare between big data analytics and machine learning. However, the major differences lie in their application.

• • Big data analytics as the name suggest is the analysis of patterns or extraction of information from big data. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs but still produce desirable outputs.

• • Most data analysis activities which do not involve expert task can be done through big data analytics without the involvement of machine learning. However, if the computational power required is beyond human expertise, then machine learning will be required.

• Normal big data analytics is all about cleaning and transforming data to extract information, which then can be fed to a machine learning system in order to enable further analysis or predict outcomes without the requirement of human involvement.

Big data analytics and machine learning can go hand-in-hand and it would benefit a lot to learn both. Both fields offer good job opportunities as the demand is high for professionals across industries. When it comes to salary, both profiles enjoy similar packages. If you have skills in both of them, you are a hot property in the field of analytics.

However, if you do not have the time to learn both, you can go for whichever you are interested in.

So what to choose?

After understanding the 3 key phrases i.e Data science, Big data and machine learning, we are now in a better position to understand their selection and usage in business. We now know that data science is a complete process of using the power of data to boost business growth. So any decision-making process involving data has to involve data science.

There are few factors which may determine whether you should go for machine learning or Big data way for your organisation. Let us have a look at these factors and understand them in more detail

Factors affecting the selection

1. Goal

Selection of Big Data or Machine learning depends upon the end-goal of the business. If you are looking forward to generating predictions say based on customer behaviour or you want to build recommender systems then machine learning is the way to go. On the other hand, if you are looking for data handling and manipulation support where you can extract, load and transform data then Big Data will be the right choice for you.

2. Scale of operations

The scale of operation is one deciding factor between Big data and machine learning. If you have lots and lots of data like thousands of TB’s etc then employing Big data capabilities is the only choice. Traditional systems are not built to handle this much amount of data. Various businesses these days are sitting over huge chunks of data collected but lack the ability to meaningfully process them. Big Data systems allow handling of such amounts of data. Big data employs the concept of parallel computing which eases enables the systems to process and manipulate data in bulk quantities

3. Available resources

Employing Big data or machine learning capabilities requires a lot of investment both in terms of human resource and capital. If an organisation has resources trained for big data capabilities, then only they can manage such big infrastructure and leverage its benefits

 

Applications of Machine Learning

1. Image Recognition

It is one of the most common machine learning applications. There are many situations where you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image.

2. Speech Recognition

Speech recognition (SR) is the translation of spoken words into text. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or “speech to text” (STT).

3. Learning Associations

Learning association is the process of developing insights into various associations between products. A good example is how seemingly unrelated products may reveal an association with one another. When analyzed in relation to buying behaviours of customers.

4. Recommendation systems

These applications have been the bread and butter for many companies. When we talk about recommendation systems, we are referring to the targeted advertising on your Facebook page, the recommended products to buy on Amazon, and even the recommended movies or shows to watch on Netflix.

Applications of Big Data

1. Government

Big data analytics has proven to be very useful in the government sector. Big data analysis played a large role in Barack Obama’s successful 2012 re-election campaign. The Indian Government utilizes numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation.

2. Social Media Analytics

The advent of social media has led to an outburst of big data. Various solutions have been built in order to analyze social media activity like IBM’s Cognos Consumer Insights, a point solution running on IBM’s BigInsights Big Data platform, can make sense of the chatter. Social media can provide valuable real-time insights into how the market is responding to products and campaigns. With the help of these insights, the companies can adjust their pricing, promotion, and campaign placements accordingly.

3. Technology

The technological applications of big data comprise of the following companies which deal with huge amounts of data every day and put them to use for business decisions as well. For example, eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising. Inside eBay‟s 90PB data warehouse. Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers.

4. Fraud detection

For businesses whose operations involve any type of claims or transaction processing, fraud detection is one of the most compelling Big Data application examples. Big Data platforms that can analyze claims and transactions in real time, identifying large-scale patterns across many transactions or detecting anomalous behaviour from an individual user, can change the fraud detection game.

Examples

1. Amazon

Amazon employs both machine learning and big data capabilities to serve its customers. It uses ML in form of recommender systems to suggest new products to its customers. They use big data to maintain and serve all the products data they have. Right from processing all the images and the content, to displaying them over the website, it is handled by the employed big data systems.

2. Facebook

Facebook similarly like Amazon has loads and loads of user data available with it. It uses machine learning to segment all the users based on their activity. Then, Facebook finds the best advertisements for its users in order to increase the clicks on the ads. All this is done through machine learning. With large user data at disposal, traditional systems can not process this data and make it ready for machine learning purposes. Facebook has employed big data systems so that they can process and transform this huge data and actually can derive insights out of it. Big data is required to make all this huge data processable.

Conclusion

In this blog, we learned how data science, machine learning and Big data link with each other. Whenever you want to solve any problem by using data at hand, data science is the process to solve it. If the data is too large and traditional systems or small-scale machines cannot handle it then BIG data techniques are the option to analyze such large chunks of data set. Machine learning covers the part when you want to make predictions of some kind, based on data you have at your end. These predictions will help you in validating your hypothesis around data and will enable smarter decision making.

Follow this link, if you are looking to learn more about data science online!

You can follow this link for our Big Data course!

Additionally, if you are interested in learning Data Science, click here to get started

Furthermore, if you want to read more about data science, you can read our blogs here

Also, the following are some suggested blogs you may like to read