Data has slowly become an intrinsic part of our daily lives in more than many ways that we are able to realize. There is exponential growth in the amount of data we create and the amount that already exists. One of the estimates in 2021 said there would be 74 zettabytes total of data generated and the figure is expected to multiply twice in number by 2024. Several data analytics companies today are creating ripples globally with their exemplary work.
Hence, we direly need professionals who understand what is the basics of data science, data analytics, and big data.
The three terms are often found to be used in the industry, where the meanings might share similarities, they essentially mean different. In the article, we will try covering the three topics so that you get a clear realization of the significance and relevance of each term needed to become a data analyst, data specialist, and data scientist. We will also mention why data analytics services are so special and what they signify.
To understand the subject better, let’s understand some of the terminologies better:
- What exactly is data science?
- What is the significance of Big Data?
- What is the meaning and application of Data Science?
While we are dealing with structured and unstructured data, Data science happens to be that field comprising of all related to data cleaning, followed by preparation, and analysis.
Data science comes as a combination of several subjects – statistics, mathematics, problem-solving, programming, capturing your data in indigenous methods. It gives you the ability to look at everything differently, along with the activity of cleaning, preparing also aligning data. This term is an umbrella that includes a combination of techniques that are used while you need to extract information and insights from any data.
Understand Big Data
Big Data signifies mammoth volumes of data, something that cannot be effectively processed with the currently used traditional applications. We begin processing raw data that hasn’t been aggregated and is most of the time impossible for storage within the memory space of one solo computer.
A buzzword used in describing huge volumes of data, including structured and unstructured, big data comes with the ability to inundate your business every day. Big Data gets used in analyzing insights, which may eventually lead to strategic business moves and better decisions.
Gartner has given the following definition of big data: “Big data is a high-volume, and a high-variety with a high-velocity asset that demands innovative, and cost-effective kinds of information processing that brings better insight, process automation, and decision making.”
Significance of Data Analytics
This is that science that examines raw data so that certain conclusions can be reached. Data Analytics entails applying certain mechanical or algorithmic processes so that you could derive insights and run through several types of data sets and look for meaningful correlations. It gets used in various industries, which helps businesses and data analytics businesses to create informed decisions, along with verifying and disproving current theories or models. The focus with data analytics exists in inference – a process where conclusions can be derived solely based on what the researchers already know.
Applications of Data Science
Data science algorithms are used by search engines to deliver the best kinds of results for all kinds of search queries in seconds.
The entire spectrum of digital marketing uses algorithms in data science, from display banners to digital billboards. This is the prime reason that digital ads come with higher click-through rates than traditional advertisements.
The recommender system not just makes it lucid to identify the relevant products from the huge amounts of available products, but also adds a lot more to the user experience. Many companies utilize this system for the promotion of their products along with suggestions aligned to the user’s past search results.
Applications found in Big Data
For the Financial Services
Several credit card companies, banks, advisories that manage private wealth, insurance firms, venture funds, and all the institutional banks use Big Data to understand and enhance their financial services. There is a common problem existing among all the financial systems – massive quantity of multi-structured data that is clustered in several disparate systems which the Big Data can solve that includes:
- Analysis of the customer
- Compliance analytics
- Operational Analytics
- Fraud Analytics
Big Data used in Communications
Gathering new subscribers, customer retention, along expanding the existing subscriber lists are its top priorities in the field of telecommunication service providers. The solution for all the said challenges lies in the strength of combining and analyzing the masses of data that are customer-generated also machine-generated, which gets created on a regular basis.
Using Big Data for Retail
Be it an eCommerce retailer or a brick-and-mortar one, the method of staying in the game also being competitive is to gather a better understanding for the customer. This needs that your business can analyze all the separate data sources that your company deals with regularly, including social media, transaction data, credit card data, and loyalty data.
Data Analytics Applications
The prime challenge for hospitals would be to treat as many numbers of patients as is efficiently possible, at the same time providing high-quality treatment. Machine and instrument data are rather highly getting used to tracking followed by optimizing patient flow, equipment, and treatment used in the hospitals. It has been estimated that there would be a one percent gain in efficiency that can possibly yield higher than $63 billion in global savings in healthcare by using the software from all analytics companies.
Data analytics has the power to optimize the buying experience using mobile/weblog along with analyzing social media. Travel websites have the power to gain insights into customer preferences. Products could be upsold if businesses could correlate current sales to the next set of browsing to increase the conversion rate from browsing to buying using tailormade offers and packages. Data analytics based on social media data will also be able to deliver customized travel recommendations.
Data analytics can help in collecting the data to optimize also spend across and with games. Gaming companies can readily learn further about what their users prefer and don’t.
Most of the firms are utilizing data analytics towards energy management, including management of smart-grid functions, energy optimization, building automation, and energy distribution in the utility companies. The application within this framework is focused on the control and monitor of dispatch crews and network devices, along with managing services outage. Utilities possess the ability to integrate millions of data points within a given network performance and empowers the engineers with the opportunity of using analytics to monitor your network.
Skills needed to turn into a data scientist
Education – 88 percent carry masters and 46 come with a PhD
In-depth understanding of R or SAS. For the field of Data Science, R is usually preferred
Python Coding – This is the most common coding language used in data science with Java, C/C++, and Perl.
Hadoop platform – Although this is not a requirement always, knowing Hadoop is a preference in this field. If your team has experience in Pig or Hive, it comes in handy.
SQL database/coding – Even though Hadoop or NoSQL has become a significant element within Data Science, it still finds preference if you can write also execute complex queries in the SQL format.
Working on data that is unstructured – It is highly essential that your data scientist is able to work with the unstructured data, be it on social media, audio, or video feeds.
Skills needed to be a specialist in Big Data
Analytical skills – These skills are essential in making sense of data and identifying which of the data is relevant while creating the reports and identifying the solutions.
Creativity – You will need to have the ability to create newer methods to gather, finely interpret, and finally analyze a specific strategy for data. The age-old number crunching, (math and stats skills is also needed in big data, data analytics, and data science.
Computer skills – Computer remain to be the backbone of each data strategy. Programmers would have a pressing need to create algorithms so that data can be processed into insights.
Business Skills – Big data professionals would require a proper understanding of the objectives of the business and the inner processes that drive the business towards profit and growth.
Skills needed to be a Data Analyst
Programming skills – Knowledge of programming languages, like Python and R, are vital for data analyst.
Mathematics and Statistical skills – Inferential and descriptive statistics, along with experimental designs, are essential skills for data scientists.
Machine learning skills
Skills in Data wrangling – The ability of mapping raw data and converting it to other formats enables greater convenience in the consumption of the data
Data visualization and communication skills
Data intuition – it is highly crucial that the individual is able to think like that of a data analyst.
The idea behind writing this piece of content was to make you aware of the immense scope that data analytics holds. While there are several fields of study in the software domain, Data Science, Data Analytics, and Big Data are some of the most highly coveted ones. The scope of the subject and prospect of employment within data analytics companies is mammoth and is today considered one of the most sought after and highly paid jobs.