Data Analytics versus Big Data versus Data Science

Information is only useful when it’s understood.

This quote has become an emotion in this new era of business, where technology acted as a game-changer. In this day and age where anything can be considered as data and one man’s trash is another man’s gold, how do you navigate your company to higher prospects?

To make data usable it has to first be sifted through and made relevant.

That task is taken up by a talented Analyst. 

What is Data Analytics?

What is Data Analytics?

Data analytics is an umbrella term that encompasses many different types of analysis. Data can be subjected to various kinds of analytic techniques and tools to refine it and make it useful information. Think of data as crude oil. It’s valuable only because of what it can be converted into. Data analysis is the refinery that separates information from crude data. And just like refineries don’t run themselves, you need specialised people – called data analysts- who are responsible for gaining key insights from the refined information given to them.

Data Analytics can be useful to any industry, it all depends on what the company is looking to improve. Several sectors that have a high turnover, like medical, hospitality, travel, etc, use customer data such as personal information, reviews, complaints, compliments, guest preferences, review forms, etc, to fix or make improvements to existing protocols. Data Analytics in retails helps keep track of the latest trends, bestselling products, and the average spending power of customers, which helps retailers stay afloat in a vastly competitive field. In healthcare, copious amounts of structured and unstructured data, which include past patient records, are refined to make informed and quick decisions.

The goal of Data Analytics in any sector is to make enlightened decisions based on past records, behaviours, patterns, trends, preferences, and any kind of relevant information from the data pool.

So how is Data Analytics any different from Data Science or Big Data? 

All three of these terms share certain similarities. They all use the data available around us to improve decision-making and provide key insights for the company.

But the difference emerges in how they derive these insights and what they use to derive them.

To better comprehend how these 3 are different let’s review Big Data and Data science once.

Big Data Analytics: A revolution in data management.

Big Data Analytics: A revolution in data management.

Big Data, just like the name symbolises, deals with data in colossal quantities. Where traditional data sets are mostly in gigabytes or terabytes at most, big data comes in petabytes, zettabytes, or exabytes.

To put this into perspective, if one byte is equal to one metre, then 1GB of data is 1Million kilometres. That is the value traditional datasets work with. 1 petabyte is 1 billion Gigabytes. That is 1000,000,000,000,000,000 metres or bytes. Traditional storage systems are ill-equipped to handle a data set of this size. Big Data is most often stored on the cloud or needs a specialised storage solution depending on where the data is currently residing.

Big Data is differentiated based on these three components:

Volume, as discussed above, means the size of each data set.

Velocity is the speed at which data is derived. The reason Big Data is as big as it is, is because data is constantly generated.

Variety refers to the various sources from which data is collected. Big Data considers data present in texts, comments, likes, etc.

Due to the large volume and various sources of data, Big Data is mostly unstructured in nature and needs a different set of tools to be analysed.

Data Science - A step further from analytics.

Data Science – A step further from analytics. 

Now that we’ve taken cognisance of Big Data and Data Analytics, let’s delve into Data Science. Data science is a multifaceted field, involving extracting information from:

  • Scientific methods,
  • Maths and statistics
  • Programming 
  • Advanced analytics
  • Machine Learning
  • Artificial Intelligence
  • and Deep Learning

Since the scope of Data Science far exceeds its purpose, i.e, to gain meaningful insights, Data Science deals with analysing complex data, creating new analytics algorithms, tools to further distil the data and even building dynamic visualisations.

Ultimately, there is definitely a degree of truth to the saying data is the new oil.

We have yet to determine the true potential of using data and as we continue our discovery of the subject, the value of data is only going to grow exponentially. This in turn implies that the sectors that discern data are going to become integral to the growth of businesses across continents.

Data Science: Your ticket to a high paying job as a fresher

When Iron Man wasn’t suiting up in his den, he’d keep up a lively conversation with F.R.I.D.A.Y, flicking a chart here, expanding a graph there, and coming up with a brilliant solution to an end-of-the-world problem. In that one moment, he was being a data scientist – correlating huge volumes of information to make a decision.

There’s a good reason why Harvard, way back in 2012, called Data Scientist, the sexiest job of the 21st century. And it still holds true! Imagine sitting on a wealth of information and being able to see a picture that nobody else could. 

But getting there takes a while. And that’s why, if you’re enamoured by this role, you should consider upskilling as early as you can.

But what exactly is Data Science?

But what exactly is Data Science?

Considering how large and complex the digital data universe is, it’s difficult to come up with a precise definition of a Data Scientist.

Broadly speaking, data scientists organise and interpret data, and specialise in one of the below aspects:

  • Advanced Analytics –  Be it in supply chain, IoT, healthcare or finance, Advanced Analytics plays a critical role in understanding customers. Advanced Analytics predicts patterns in customer behaviour, forecasting the likelihood of future events and allowing organisations with better decision making. 
  • Cyber Security- Anyone who has watched any series based on the 21st century, understands what Cyber Security is. But less is known about Data Science’s role in the same. When data science – specifically machine learning – is applied to cyber security, organisations are able to detect loopholes in their digital environment. Data Science also processes vast amounts of data to understand the behaviour of an attacker.
  • Data Mining – Data mining is like any other form of mining. It involves cleaning, selecting and transforming – but the object that gets mined is data. Data mining is used to solve specific business problems like recognising fraudulent actions, supporting R&D processes, discovering relationships between customers and services, and more.
  • Data Visualisation – As the name suggests, it is the process of visual representation of data and relationships. If you’re only thinking of pie charts and graphs, think harder. From box-and-whisker plots to choropleth maps, there’s a world of figures out there to visualise data and bring to light hidden correlations between various activities in business.

As you can see, these fields also overlap in many principles, and the common ground is the ability to understand and interpret data. No matter what specialisation you choose, or if you choose more than one, there are many exciting opportunities in front of you.

Scope of Entry-level Data Science Jobs

Clearly, Data Science is the stuff of big guns. So to do well, you need either a Bachelor’s Degree in this field or some work experience. If you don’t have any of these, you’d definitely need a certification in Data Science that covers Python, SQL and Machine Learning.

Scope of Entry-level Data Science Jobs

Once you have these skills, here are some positions you should look for if you have specialised knowledge of Data Science

  • Business Analyst: A business analyst interfaces with internal departments to collect, distribute and manage organisational data. This responsibility can also expand to analysing requirements, documentation of processes, user acceptance testing and more.
  • Data Analyst: Starting from collecting and storing data, a data analyst must apply technical expertise to ensure the accuracy and quality of the data and come up with actionable insights. It’s a must for data analysts to have an eye for detail, a knack for scouring through data and putting the pieces together to form the big picture.
  • Data Engineer: A data engineer applies machine learning techniques and develops algorithms to convert raw data into actionable information. Considering every business has its own type of information and setup, a data engineer fills in a key position to meet the specific data needs of an organisation.
  • Data Generalist: Domain-agnostic by nature, a data generalist conducts an in-depth analysis of the organisation’s information systems, extracts insights from data and communicates them to business leaders. As they never deep dive into a specific vertical, a data generalist needs to have expertise in exploratory data analysis tools, scripting and modelling, data visualisation, etc.
  • Business Intelligence Developer: A regular engineering position – the role of a Business Intelligence Developer is to interpret and present organisational data using business intelligence technology. Developing a BI interface needs an understanding of software, databases, data analysis and some level of industry-specific knowledge.
  • Machine Learning Engineer: Another true-to-form technical position, machine learning engineers develop ML algorithms that analyse huge volumes of unstructured data and turn them into useful information. Text-to-speech conversions, meaningful auto-recommendations and self-running AI systems are some of the cutting-edge missions on the plate of a machine learning engineer.

These are but a few of the roles available in a career in Data Science. As organisations are unearthing the importance and the complexity of data, more domain-specific, function-specific and technology-specific roles are coming up, even as you are reading this.

Paygrade of Data Scientist

Paygrade of Data Scientist

Being a vast and yet potentially untapped field, the pay range for a Data Scientist is as varied as the responsibilities they are able to fulfil. The more cross-skilling a Data Science professional is willing to do, the higher the pay they are assured.

In India, a Data Science professional, at an entry-level, can expect a minimum of ~ Rs. 3.5 lakhs and an average salary of Rs. 7 lakhs. Most MNCs, large organisations and even some startups offer a data science professional, without any field experience, a package of Rs. 5 lakhs, provided they know their stuff. In just 3 to 5 years of work experience, this number can raise up to Rs. 14-20 lakhs per annum.

The easiest way to fast-track this journey is by upskilling the right way – which involves not only training in technology, industry trends and emerging tools, but also how these aspects can be applied to real-world problems. Explore upGrad Campus’s industry-ready course in Data Science and Artificial Intelligence to know more about the career opportunities in this field.

Best data analytics online courses in 2021

With the advancement of technology, there is abundant data that needs to be managed by the organization to get the maximum benefit in business. Data analysis has become the core competency of any success-oriented company. To excel in the field and get competitive advantages, an organization has to take the help of data analytics to make strategic decisions. In today’s scenario, every organization needs to collect, preserve, and analyze their data for improving business and gaining a competitive advantage. So, for skilled data analytics professionals, there is an ocean of opportunities across industries and to become skilled in this field one needs to enrol in Data analytics online courses.

Data Analytics Online Courses 

With such a huge volume of data, the role of data analytics has become more important in the organizational scenario. A data analytics course helps you to acquire the right skill set and knowledge that can open up new avenues and opportunities. Data analytics courses for beginners are designed with industry-relevant curriculum and case studies, which provides the beginners with valuable skills and expertise to serve the industry. Many basic data analytics courses for beginners focus on providing knowledge about the basic tools and software used in data analytics courses. 

Best Data Analytics Courses

Best data analytics courses focus on developing the ability to transform data into business insights for impactful decision making. The courses help to build the capability in business intelligence tools and their practical applications across multiple domains, to gain effective data-driven problem-solving skills. 

upGrad Campus, Coursera etc are some top online platforms that offer data analytics online courses. These data analytics courses are suitable for freshers, working professionals as well as entrepreneurs interested in building a strong foundation in modern business practices using an advanced analytical approach. The data analytics online courses cover job-relevant topics through an applied learning model with live sessions by leading industry professionals. The course curriculum includes hands-on applied projects through an online platform, live virtual classrooms, and access to regular learning support. 

Some colleges and universities offer data analytics courses in India at bachelor’s and master’s levels. The programs available for Bachelors degree in data analytics are B.Tech (Data Science and Data Analytics), B.Sc with specialization in data analytics, M.Sc in Data Analytics or other degrees with data analytics as one of the specializations. Anyone interested in a career boost in data analytics can opt for data analytics certification. 

The following data analytics certification courses offer ample opportunities to aspiring candidates looking for a successful career in data analytics.

Google Data Analytics Certification is a professional certification course by Google which aims at giving a thorough understanding of practices and processes of data analytics in day-to-day jobs. 

Amazon Web Services (AWS) data analytics certification course provides knowledge on analytical tools and applications using cutting-edge technology and real-life projects. It offers the opportunity to professionals associated with AWS, having few years of experience.

Microsoft Certified Data Analysts Associate Course teaches about the need for business intelligence and the use of BI tools that are used by data analysts. It is of two types of course; online for free and instructor-led paid courses.

IBM Data Analytics Professional Certificate on Coursera aims at building proficiencies for using tools in various data analysis tasks. It’s a free enrollment course with a duration of 11 months at 3 hours/week. It is a beginner-level course and no prior experience is needed, except basic computer skills.

Data Analytics Courses Fees

The data analytics course fees for certification ranges between INR 12,000 to INR 22,000. The data analytics courses for beginners in India provide Bachelors’ degrees with specialization in data analytics and also diploma or PG diploma. The data analytics course fees vary between institutions and depend on the level of degree/certification provided. Given below are some of the data analytics courses offered by different institutes and their corresponding fees.

  • Diploma in Data Analytics (MDU Rohtak): INR 30,000
  • Post Graduate Diploma in Data Analytics (SPPU Pune): INR 20,000
  • BSc (Hons) Data Science & Analytics (Sharda University, Greater Noida): INR 92,000
  • BTech with Specialization in Big Data Analytics ( LPU Jalandhar): INR 2,40,000
  • BTech with Specialization in Big Data Analytics in association with IBM (PDM University): INR 1,35,000

Data Analytics Courses Syllabus

The data analytics course syllabus aims at developing knowledge on analytics tools and their applications across business functions. It helps to build a successful analytics capability that has a very important application in the organizational environment. The basic topics that form the part of the data analytics course syllabus are as follows: Data structure and algorithm, Statistical Analysis, Machine languages, Data Visualization, Marketing analytics, Supply chain Analytics, Forecasting Analytics, Relational Database Management.

Data Structures and AlgorithmsSupply Chain Analytics
Probability and StatisticsCustomer Analytics
Relational Database Management SystemsRetail Analytics
Business FundamentalsSocial Network Analysis
Text AnalyticsPricing Analytics
Data CollectionMarketing Analytics
Data VisualizationOptimization
Statistical AnalysisMachine Learning
Forecasting AnalyticsSimulation

The best data analytics courses build a strong foundation on how to solve real business problems using analytical tools. upGrad Campus courses help to develop an in-depth understanding of artificial intelligence, machine learning, and other things to strengthen data-driven decision-making in organizations. 

What is big data analytics?

Big Data Analytics – These are the most heard words these days. Why is it trending? Because of the big amount of data produced these days by the consumers, this data acts as a treasure for organizations.

Big data analytics describes the process of collecting, structuring, and analyzing large amounts of raw data to help make informed decisions. This process applies statistical analysis techniques to analyze extensive data with the help of various tools. The software and hardware capabilities made it possible for organizations to integrate vast amounts of complex information. 

Let’s Have a Broader View of How Big Data Analysts Work?

The huge information or ‘Big Data’ mentioned above needs to be utilized in a meaningful way which is done through big data analytics. This analysis and review of data guides organizations to make smarter business decisions which lead to higher profits and satisfied customers. The big data analytics process includes collecting, processing, organizing, and analyzing data. Organizations access both structured and unstructured data from a variety of sources using the latest technology. The collected and stored data are organized properly to ensure accurate results on analysis. The organized data is analyzed through advanced analytics processes to turn them into big insights for business operations.

Companies and enterprises use various big data analytics tools to manage a huge volume of data generated. Big Data Analytics tools also help businesses to analyze data efficiently which saves time and money. There are various types of tools that serve to improve the process of data analysis and gaining insights to facilitate data-driven decisions. 

Big Data Analytics Tools

Some of the key big data analytics tools are listed below:

  • R-Programming is a programming language specifically designed for statistical analysis.
  • Lumify is a tool that enables the exploration of connections and relationships between data. 
  • RapidMiner is a software platform used to integrate data and machine learning. 
  • Microsoft Azure is a platform handled by Microsoft and provides services like computing, analysis, and storage of data.
  • Apache Spark is a robust big data analytics tool that can quickly possess a large amount of data
  • Xplenty is a cloud-based platform that allows data flow across sources and destinations. 

Types of Big Data Analytics

Big data analytics has various stages and types that have to be followed to get desired results. In general, the types of big data analysis are as follows:

  • Prescriptive – The objective of prescriptive analytics is to recommend the best possible solutions for a present situation as the analysis suggests from the available data. 
  • Predictive – It is used to forecast future conditions based on suggested patterns in the data. It is the most commonly used type of analytics for predicting the outcome depending on how a company responds to a situation.
  • Diagnostic – This type of data analytics is used to diagnose the response and corresponding outcome mostly related to a past event or situation.
  • Descriptive – Descriptive analytics uses statistics and segmentation techniques to analyze what has happened. This can be time-sensitive so it should be used with more recent data.

Big Data Analytics Applications

Big data analytics applications in various sectors of industries are as follows:

  • E-commerce – Data analysis to predict customer trends and optimizing prices 
  • Marketing – Implement data-driven marketing strategies for better ROI and improve sales
  • Education – Develop new courses and improve existing learning methods based on data about student requirements and preferences
  • Healthcare – Analysis of patient’s medical history helps to address health issues and proactively decide on treatment procedures 
  • Media and entertainment – Analysis of customer feedback data to deliver a personalized program list
  • Banking – Analysis of customer profile helps to select various banking offers
  • Telecommunications – Use of data to assess network capacity to improve customer experience.

In general, big data analytics help organizations to build a competitive advantage through efficient productions, innovative solutions, and conscious business decisions. Organizations adopt big data analytics to get ahead of the competition by improving their existing services and products. With real-time big data analytics applications, businesses can make timely decisions to maximize revenue. They can improve productivity through workforce data analysis and monitor product performance over a specific period. As this large amount of data moves across systems daily, information security can be a persistent concern. A critical analysis of such data reflects trends and patterns which can be judiciously used to detect and address chances of fraud.

Big Data Solutions

Big data solutions are necessary measures that involve capturing all of the organization’s data, utilizing it for analytics and making strategic decisions to get solutions. Analytics should start from the leadership stage and be embedded into entire business processes. It provides the solution to different levels of management operations which leads to effective utilization of capacity and resources. Some pertinent solutions that big data analytics provide are given as follows: 

  • Risk Management: Big data analytics is used to identify discrepancies in the system, analyze root causes of problems to take corrective actions, and provide solutions for the mitigation of risks.
  • Product Development and Innovations: Manufacturing sectors across the globe uses big data analytics to analyze the efficiency of product performance and plan accordingly for improvements.
  • Quick and Better Decision Making: Big data analytics provides relevant recommendations through analysis of data, which helps to make effective and timely decisions.
  • Improve Customer Experience: The data regarding customers’ feedback is analyzed for both positive and negative aspects. By addressing the issues and offering solutions, it helps the organization to build a good customer base.

Big Data Analytics Challenges

It’s a challenge for enterprises to store, manage, utilize, and analyze such an enormous amount of data. Large business organizations are constantly looking for ways to make this huge amount of data useful for their business purpose. Some of the big data analytics challenges being faced are described as below:

  • As data sources are becoming bigger and more diverse, there is a big challenge to synchronize them.
  • It is important for business organizations to effectively use the data skills of professionals who are experts in data analytics. So a major challenge faced by businesses is the shortage of professionals who can handle big data analytics.
  • A major challenge faced by the companies in big data analytics is that only the relevant department should have access to this information, to get better insights for making decisions. It is considered vital to making data access easy and convenient for brand owners and stakeholders.
  • Another big challenge in big data analytics is to find the best-suited technology to handle a variety of data and address new problems and potential risks.
  • A real challenge is the storage of a massive amount of data, both structured and unstructured. Inconsistent data, logic conflicts, and duplication are some of the data quality challenges.
  • Big data analytics offers a wide range of possibilities and opportunities, but also involves the potential risks associated in terms of privacy and security of data.

Big Data Analytics uses advanced analytical techniques to a diverse, large data set that consists of unstructured, semi-structured and structured data, collected from different sources. Analysis of this big data by analysts and researchers helps business users to make better and quicker decisions using data in comparison to the traditional way of decision making using data that is inaccessible or unusable.