Technology, though it hit the market many years ago, developed and multiplied exponentially at a startling rate only in the last few decades. Various applications and other products being launched regularly have increased their consumption by the masses. Such a use, by almost all the world, has brought data barrelling into servers of incompatible capacity. And this piled-up data needs to be sorted, structured and analyzed to attain useful business results. That’s what data science does.
Data Science is the study of the combined application of concepts like Mathematics, Computer Science, Business Administration, Analysis, Statistics, Programming, and other technical knowledge to lead businesses to a pinnacle.
Data science is the science of sorting, processing, and extracting data from multiple sources and applying statistics, algorithms, and analysis to transform the data into meaningful insights.
Since its evolution in the 1960s, data science has helped businessmen realize the value of data in growing businesses by tapping into the voluminous data collecting dust in the servers. Data science has helped file the task of sorting, structuring, analyzing, and suggesting improvizations in business tactics and strategies all under one head - Data Scientist.
A Data Scientist peruses this data, cultivates business acumen, and offers insights and constructive criticism on the performance of a business or a company while simultaneously making use of the abundant data available for further advancements like Machine Learning, AI (Artificial Intelligence), and cybersecurity.
John Tukey, William Cleveland, and Leo Breiman are said to be the fathers of data science. They identified the need for merging statistics, data, and analysis to introduce a new field of science called Data Science to make use of the information and deduce from it beneficial results. Earlier statisticians believed that data could be transformed into information and knowledge, whereas the world saw only unimportant figures and algorithms.
Data science has come a long way from just numbers, expressions, and traditional statistics. The evolution of data science brought into the spotlight its many benefits to advance towards the future i.e., artificially intelligent technology and smart systems to make life simpler, and satisfactory.
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As shown in the lifecycle picture above, data science involves, firstly, a good business understanding.
Meaning, that a data scientist must understand the needs of a business that will advance it, and accordingly, search for trends, and patterns, and perform statistics and analysis to discover new business potential.
Data mining involves accessing databases, data warehouses, and extraction from other similar sources to gather required data in one place and apply ML (Machine Learning) algorithms to it for processing datasets.
After data is mined, it needs to be cleaned or sorted by removing errors such as incorrect data, wrongly formatted data, duplicate data, incomplete data, and corrupted data.
The structured data is used as a rough blueprint to explore through and identify patterns, characteristics, and trends.
Feature engineering involves the application of algorithms on predictive models to improve accuracy and automate the process of extraction of raw data. It is a part of Machine Learning.
Predictive modeling involves the application of mathematical concepts like statistics and analysis of the data or input; to predict favorable outcomes.
Visualization of data requires an eye for detail and strong storytelling skills to represent and communicate the results of the analysis with the team to determine the next business decision.
Data science, since its evolution, has made its place in almost all sectors – finance, healthcare, transport, e-commerce, etc. That makes it necessary for us to understand what data science is, and what it is capable of.
Here are a few scenarios of how data science has brought in a significant change in the following sectors:
Monitoring irregular transactions, maintaining customer data, recognizing a customer’s investment patterns to recommend plans & policies suitable for them, account & card fraud detection, analysis of the bank’s performance for owners to realize lacking areas to provide better customer service.
Predictive modeling to predict future events and the probability of outcomes or results, predictive analysis to scrutinize facts and data for favorable outcomes, such as predicting the stock market’s jump towards green or drop in a share’s price.
Machine Learning algorithms have enhanced the efficiency of manufacturing and producing systems and machines by way of automation. An analysis of customer feedback helps manufacturers understand the customer’s requirements and expectations to alter their products accordingly to increase sales.
Insight from travel records is gathered to recognize what days or times passengers prefer to travel to adjust and arrange transportation schedules and frame a ticket price structure. Data also helps understand the best time for delivery of a product. A survey once showed that most customers prefer deliveries between 10 AM to 5 PM.
A user’s buying and selling patterns, likes, and dislikes, are stored by businesses to derive useful insights. Data Science helps achieve business growth by analyzing a buyer’s search patterns, recent trends, user behavior, and a few other metrics to enhance the digital experience.
Though data science is widely used, there is a noticeable vacancy for individuals with the required skills to assess this data hence, creating a huge demand for data science practitioners in all fields.
In a neck-to-neck race of who comes out on top, data science has changed businesses’ perspective of looking at data. The feedback a business receives from its customers is considered of utmost importance to drive significant impact and compete with other companies. The only way to beat the competition is to recognize customer needs and expectations and how you can deliver them faster and better than your competitor.
Data Science has now entered sectors like banking & finance, healthcare, transport, e-commerce, and other common business sectors like the fashion and food industry. The graph below is a representation of the availability of jobs in the respective sectors.
The graph shows the demand for Data Science practitioners in descending order with availability in diverse sectors including Banking & Financial Services, Energy & Utilities, Pharma & Healthcare, E-commerce, Media & Entertainment, Telecom, Automobile, Retail & CPG, Travel & Hospitality.
With Big Data growing by the hour, it would only be right to assume that the need for Data Scientists, Data Engineers, Data Analysts, Business Analysts, Database Administrators, Data Architects, Machine Learning Engineers, Deep Learning Engineers, etc.,
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There is worldwide demand for data science practitioners. And as per reports, big data is predicted to get even bigger in the coming future and will generate an outrageous demand for data science professionals in top-tier companies.
Here are a few reasons that have motivated many to choose Data Science as their career:
Data Science received undivided attention and recognition after Harvard declared it to be the sexiest job of the 21st century in its business review. The acclaimed fame garnered the curiosity of business industries and realized the scope and potential of data science, making businesses race to get their hands on the best data science practitioners.
Companies and businesses are quite willing to pay Data Science professionals with a minimum of 5 years of experience the salary they expect (sometimes as high as 1.3 crores per year or more).
Data Science is being implemented in many spheres like healthcare, Life sciences, retail, fashion, food, producing & manufacturing, and now, even sports. This diversity leaves wide job opportunities for Data Science professionals to choose from.
Data Scientists are well-versed in dealing with data pertaining to various backgrounds like healthcare, banking, finance, food, fashion, automobiles, and other expansive sectors. A Data Scientist is, like his machines, always learning. Data Science jobs offer their aspirants the most valuable quality that only a few jobs can boast of: Knowledge. A Data Scientist is abreast of new updates in technology, recent trends, happenings around the world, important events, new business equipment, philosophies and so much more. A Data Scientist is knowledgeable across an array of platforms that only a few other job professions can boast of.
Any graduate with a degree in Science, Technology, Engineering, and Mathematics is eligible for learning data science. Individuals with basic high school education and above can also apply for learning data science. Additional technical knowledge for taking up a course in data science includes R Programming, Hadoop, MATLAB, Python, SAS, and SQL.
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The in-demand jobs in data science are:
Machine Learning Engineers primarily work with machines and are proficient in programming languages. A Machine Learning Engineer (ML), develops algorithms for accuracy in a machine’s functioning and instils the ability to learn from new and previous operations by storing these operations or interactions.
A Data Scientist is also well-knowledged in the field of Machine Learning and Deep Learning. But a Data Scientist’s focus is more inclined toward business operations for growth. A Data Scientist peruses data to suggest ideas for the development of a business.
Data Analysts mostly deal with transforming data into coherent and consumable formats and are responsible for data visualization and its representation to communicate the performed analysis. They are critical thinkers with an eye for detail.
Data Architects create a robust structure for rapid inflow and outflow of data without any obstruction or security risks. They work with Database Administrators to create secure databases and monitor access activity.
Data Engineers build pipelines for data flow. They perform ETL/ELT processes on the data which is later sent to databases or data warehouses for storage. The Engineers are skilled in SQL (Structured Query Language) and thus, responsible for database management.
There will always be scope for data science jobs in the future as the world is opting out of manual labour and leaning more towards artificially intelligent machines and systems to enhance productivity. Corporations and governments across the globe have begun hiring Data Science professionals to deliver satisfactory products and to better administrate citizens.
A certified Data Science practitioner earns an average of INR 8 Lakh a year.
With Big Data increasing ten-fold in the near future, Data Science jobs like Data Analyst, Data Scientist, Database Administrator, Data Architect, Data Engineer, Cybersecurity/Information security Engineer, Machine Learning Engineer, Blockchain Engineer, Cloud Computing Engineer, Deep Learning Engineer, and IoT Engineer, are believed to experience high demand for their skills and handsome salaries.
Top 30 frequently asked Data Science Interview Questions!
You can become a Data Scientist if you have the following qualifications:
Hard Skills - Mathematics, Computer Science, Big data, fundamentals of data science, Programming languages like Python and R, Statistics, Data wrangling, Data Extraction, Transformation and Loading, Data manipulation and analysis (involves knowledge of SQL and NoSQL), Data visualization tools like Tableau, Power BI, Dundas BI, Machine learning, Deep learning, Software, SAS, MATLAB, Apache Spark, Hadoop, etc.,
Soft Skills - Problem-solving, storytelling, collaboration, curiosity, communication, creativity, adaptability, product understanding, team player, business acumen, and critical thinking.
A Data Scientist filters, structures, sorts and analyzes data. He also performs predictive analysis and modeling for efficiency. For these actions to be achieved successfully, a Data Scientist must be aware of the best tools to suit each purpose.
A Data Scientist uses the following tools in the following lines of work:
Programming language - the majority of data scientists prefer using Python for programming as it is more suited for almost all kinds of operations.
Machine learning tools - Machine Learning tools are used mostly depending on the type of objects to be fulfilled. Machine Learning tools are h20.ai, Tensor Flow, Apache Mahout, and Accord.Net.
Data visualization tools - Tableau is the most popular data visualization tool other data visualization tools include PowerBI, Dundas BI, Infogram, and Plotly.
Database Management - SQL, NoSQL, MySQL, PostgreSQL, MongoDB, Hadoop HDFS, etc.,
Related Article: Big Data vs Data Science
The availability of all sorts of data brought solutions for every problem. Data Scientists use this data for providing solutions to the following challenges
Data Science offers suggestions for the betterment of a patient’s health, diagnosis, precautions, and detection of any chronic, genetic or hereditary diseases.
New technological concepts like Augmented Reality and Virtual Reality are made possible by implementing Data Science. Machine Learning, Deep Learning, and Natural Language Processing (NLP) have opened gateways for the futuristic way of life.
Data Science uses predictive analytics to help farmers study the current conditions of their crops. It helps them monitor activities like pest control, better irrigation technique, and diseases to ensure maximum productivity and benefits. Data Science has enabled the usage of Sensors, applications, and other technology to help farmers observe efficient cultivation, and farming techniques.
Data Science curbed the Finance industry’s need for security and automation for its operation. Data Science professionals run processes of Risk analysis, and Fraud detection to intercept identity, authorization, and credibility for confidential or vulnerable transactions. A Data Scientist or a Data Analyst performs analysis to develop prime techniques and applications for customer data management and provision of the best services.
Data Analysts gather information such as a customer’s age, location, occupation, gender, and a few other metrics for analysis to determine a successful marketing technique for the product.
With Big Data Analytics in the field, sports statisticians can predict the outcome of a game by comparing each player’s performance with the rival team’s. Data Science has helped teams figure out the best gameplay and player to win the game.
Data Science gathers route-related information — traffic, accidents, or social or political incidents like strikes, rallies, etc., and uses predictive analysis for selecting the best route for faster and safe travel.
Selling a property is a lot of work and the results are often predetermined by many social, local, political, and economic factors that are often overlooked by Real Estate agents. Data Science calculates all these factors along with other geographical factors of a property such as its location, area, directions, etc., to increase sales.
Food industries have begun using data science to understand better delivery timings by viewing aspects like climate, traffic, and road obstructions. Considering aspects like temperature, hygiene, location, and storage has helped food and nutrition industries in preventing food-borne diseases like Salmonella, Listeria, Staph infections, and other such diseases caused by ingesting improperly processed or stale food.
Data Science predicts the influx of customers in rental businesses by considering factors like which month a person prefers for vacation, popular locations, tourist spots, hotels, weather, festivals, etc., to help businesses providing rental bikes, automobiles, clothing, accessories, rooms, and apartments.
Records of natural phenomena like volcano eruptions, floods, droughts, and earthquakes and the destruction caused, can be studied to predict and prepare for dire circumstances in advance. Though nature takes heed of no being, Data Science proves advantageous by running predictive analysis to mitigate the risk and minimize damage. Pollution and the reasons behind it can be calculated to manage vehicular and industrial pollution to improve the quality of natural resources.
A person, after his data science course completion, is eligible for the following roles:
India has experienced significant growth in businesses in recent years. This increase brought in the need for data science professionals in almost all sectors. Ever since data science came to be recognized as an emerging discipline, countless corporates set out to employ Data Science professionals to quench their thirst for competition.
Data Scientists, Data Analysts, Data Engineers, Data Architects, and other Data science professional fields unlocked the key to translating uncomprehending data into indisputable business growth, and ultimately steady income and desired rate of returns on investments for investors.
According to India Today, the demand for data science professionals is expected to increase with 11 million job openings just by 2026. The demand for Data Science practitioners will continue to bud so long as the word ‘business’ exists. In today’s world, and even in the future it would be impossible to pursue a business without Data Science supporting it as a backbone.
Data science has now become the air any business or organization needs to survive. Industries with a never-ending demand for data science professionals include:
You can browse for data science jobs in these best job hunting portals mentioned below:
The best companies hiring data science professionals are:
Related Article: Cyber Security VS Data Science
Data has proved to be an integral part of today’s world. Many businesses have recognized the untapped potential of the data they receive and the advantages that can be unfurled by using this data.
Data science deals precisely with this insurmountable data by transforming, sorting, managing, and studying it to drive impact in all sectors and occupations.
‘Data Science’ has become a common jargon in the business world and IT. Many individuals are seeking out training institutes to gain proficiency in data science with dreams to be hired by top companies. Now let’s take a look at the most talked-about advantages and rarely mentioned disadvantages of data science.
A lot of websites have started publishing blogs that are of much help to aspiring data scientists and other data science professionals. The best data science blog websites are mentioned below:
SuperDataScience - SuperDataScience hosts podcasts by experienced business, artificial intelligence, Python, Machine Learning, Deep Learning, and Data Science professionals with around 570 podcasts on the topics of business, data visualization, Machine Learning, Python, Deep Learning, Artificial Intelligence, R Programming, Data Science, Tableau, Excel, Blockchain, and Database.
Data Skeptic - Data Skeptic addresses remote work issues, the unsupervised learning k-means, time series, definition of consensus in distributed systems, database designs, and voting algorithms and around 90 mini-episodes on Machine Learning concepts. Listen to the episodes on Apple Podcasts, Spotify, Google Podcasts, Stitcher, Castbox, Amazon Music, TuneIn, Podcast Addict, Pocket Casts, and Player FM.
The Artists of Data Science - The Artists of Data Science’s podcasts can be tuned into through Apple Podcasts, Google Podcasts, Overcast, Castbox, Spotify, TuneIn, and Stitcher.
Lights On Data Show - The Podcast is hosted by George Firican where he discusses data science topics with industry experts. You can listen to the podcasts on Apple Podcasts, Castbox, Google podcast, Overcast, Pocket Casts, Radio Public, and Spotify.
Domino Data Lab - Domino Data Lab launched the Data Science Leaders podcast hosted by Dave Cole. Each podcast is an interview with data science experts where they give insights, recommendations, and breakthrough strategies to achieve enterprise data science success. The interviews can be listened to on Apple Podcasts, Google Podcasts, Stitcher, and Spotify.
Harvard Data Science Review Podcast - The prestigious Harvard Data Science Review (HDSR) podbeans reveal case studies of data science — how it has been used, abused, and manipulated to drive shocking results. The podbeans explore data science topics with data science experts and CEOs around the globe.
Data Science At Home - Data Science At Home, a show hosted by Dr Francesco Gadaleta, is about Machine Learning, algorithms, and Artificial Intelligence. The podcasts air interviews with the most influential figures in the field of data science. The podcasts can be listened to on Apple Podcasts, and Spotify.
Community Alteryx - Community hosts ‘Alter Everything’ and ‘Data Science Mixer’ podcasts covering the broad analytical culture, Machine Learning, NLP, Python, Big Data, Datasets, and many other data science topics with industry experts. The platform has 150+ podcasts that can be listened to through Apple Podcasts, YouTube, Spotify, and Overcast
Women in Data Science - Stanford - Women in Data Science is a special initiative taken up by Stanford University to encourage women across the world to lead in their domains with the help of data science. The podcast is hosted by Professor Margot Gerritsen of Stanford University as she converses with influential women in data science working in healthcare, cosmology, finance, human rights, and more. The WiDS podcast can be tuned into through Apple Podcasts, Google Podcasts, Stitcher, Spotify, and Overcast.
O’Reilly Data Show - The O’Reilly Data Show is a podcast hosted by Ben Lorica, Chief Data Scientist at O’Reilly media. Ben has also held the position of Program Chair head of the Strata Data Conference, the O’Reilly Artificial Intelligence, and TensorFlow world. Ben questions Machine Learning and Data Science professionals on techniques driving Big Data, Data Science, and AI. The show explores Machine Learning concepts extensively. You can listen to their latest episodes on Apple Podcasts, Google Play, Stitcher, and RSS.
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