What is Data Science? History, Lifecycle, Prerequisites, Careers, Applications, Use cases
Data science courses are among the most popular globally, with a high likelihood of career prospects, according to the volume of internet searches for skill development or job-oriented courses. Data scientists are needed everywhere. The most fundamental prerequisite for developing any technology in this era of smart technology (which includes smartphones, televisions, watches, etc.) is data, and these data scientists serve as the foundation for machine learning and artificial intelligence specialists. A data scientist will also assist organizations in managing serious crises and assisting them in their resolution through the use of data-driven judgments.
What is Data Science
Data science is the study of analyzing and obtaining organized, unstructured, and noisy data from various sources. This analysis aids businesses in forecasting outcomes and making data-driven decisions.
Data that adheres to a data model, has a clearly defined structure, follows a persistent order, and is simple for both humans and programmes to retrieve is said to be structured data.
Unstructured data is not structured in a way that has been predefined, notwithstanding the possibility that it has a native, internal structure. The data is kept in its original format; there is no data model. Media, text, internet activity, monitoring photos, and more are typical instances of large datasets.
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Corrupted data, a type of unstructured data, is another name for noisy data. It also includes any information that a user’s system is unable to effectively analyze and interpret. If handled improperly, noisy data can have a negative impact on the outcomes of any data analysis and skew conclusions. Sometimes, statistical analysis is employed to remove noise from noisy data.
Why Data science is important
Consciousness is essential for any company that wants to develop and stand out. This evaluation is carried out by a data scientist. Data scientists are thus in high demand and will continue to be so in the near future. A data scientist uses a variety of instruments and spots patterns in data. Data science is significant because it enables organizations to develop insights that can be used to learn more about their consumers and community by using already-existing data that may not be helpful on its own and combining it with additional data points.
History of Data Science.
Data science began in the 1960s as a branch of computer science, but the term “data scientist” wasn’t coined until the late 2000s. Since the 1990s, data scientists have been collecting user data, but it wasn’t until the early 2010s that it was used to make sales and new technology.
Applications that collect and analyze data rely on statistics and statistical models to create outcomes. Such technology has evolved to include modern-day concepts and practices, such as the Internet of Things, machine learning, and artificial intelligence, to track online behaviors.
The Data Science Timeline: How It Changed the World
Although data science has had a short lifecycle, its impact on our modern world is clear. Data is so widely used that it’s hard to imagine life before it, even if you grew up before its boom.
Data Science Beginnings: 1962-1999
The data science timeline began in 1962 when John Tukey wrote a paper that discusses the merger between statistics and computers. But few discoveries were made before 1999:
- 1974: P. Naur wrote “Concise Survey of Computer Methods,” mentioning data science.
- 1977: The International Association for Statistical Computing was created, which sought to link statistical methods with computers. Tukey wrote a second paper about data.
- 1989: The Knowledge Discovery in Databases created their first workshop.
- 1994: Business Week ran a story that covered companies that gathered personal data.
- 1999: Jacob Zahavi stated that businesses need a tool to handle large amounts of data.
Data Science In the New Millenium: 2000-2015
At the turn of the millennium, computers were starting to appear in homes and offices. Data science is starting to become the norm and more accessible to the public via software:
- 2001: Software-as-a-service (SaaS) is created, a precursor to cloud-based technology. William S. Cleveland proposed a training manual for new data scientists.
- 2002: The International Council for Science published a Data Science Journal.
- 2006: Hadoop was created to help companies store and process huge amounts of data.
- 2009: NoSQL was reintroduced by Johan Oskarsson (and is still used today).
- 2011: Job listings for data scientists increased by 15K%, as data was seen as profitable.
- 2015: Google’s speech recognition, Google Voice, and deep learning techniques are more popular than ever. Jack Clark stated that artificial intelligence is now widely used.
Data Science of the Future: 2015-2035
Data scientists and data science as a whole have become essential in business and academic research. This technology can do everything from predict health outcomes to recessions with varying degrees of accuracy. Simpler algorithms tend to be more effective than complex ones
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Thomas Davenport, an American academic and publisher for Harvard Business Review, once said that Data Scientist is “the Sexiest Job of the 21st Century”. But why is there such a big hype and mythos about Data Scientists and Data Science?
The role of the Data Scientist came up with the Big Data area. But it’s not a quite new role in the enterprise business. Before we called them statisticians or subject matter experts. So what makes him now so different and what skills brings a Data Scientist to his role? Drew Conway published a very interesting illustration, called the “Data Science Venn Diagram”:
This diagram explains the 3 major skills a good Data Scientist must have. Interesting is the area where math & statistics knowledge is missing, which is called “Danger Zone!”. So to make it clear, a good programmer with a good substantive expertise in a certain area is not enough to become a good Data Scientist. According to DJ Patil, chief scientist at LinkedIn, the best data scientists tend to be “hard scientists,” particularly physicists, rather than computer science majors. Physicists have a strong mathematical background, computing skills, and come from a discipline in which survival depends on getting the most from the data. They have to think about the big picture, the big problem. But why are math & statistics knowledge so important?
Read more about Data Science –What’s the big deal about it?
What is data science used for?
Data science is used to study data in four main ways:
1. Descriptive analysis
Descriptive analysis examines data to gain insights into what happened or what is happening in the data environment. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service.
2. Diagnostic analysis
Diagnostic analysis is a deep-dive or detailed data examination to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.For example, the flight service might drill down on a particularly high-performing month to better understand the booking spike. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event.
3. Predictive analysis
Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. The computer program or algorithm may look at past data and predict booking spikes for certain destinations in May. Having anticipated their customer’s future travel requirements, the company could start targeted advertising for those cities from February.
4. Prescriptive analysis
Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.
Read more about 10 Types of Data Analysis methods
Data Science Life Cycle
The Team Data Science Process (TDSP) provides a recommended lifecycle that you can use to structure your data-science projects. The lifecycle outlines the complete steps that successful projects follow.
This lifecycle is designed for data-science projects that are intended to ship as part of intelligent applications. These applications deploy machine learning or artificial intelligence models for predictive analytics. Exploratory data-science projects and improvised analytics projects can also benefit from the use of this process. But for those projects, some of the steps described here might not be needed.
Five lifecycle stages
The TDSP lifecycle is composed of five major stages that are executed iteratively. These stages include:
- Business understanding
- Data acquisition and understanding
- Customer acceptance
Here is a visual representation of the TDSP lifecycle:
What are different data science technologies?
Data science practitioners work with complex technologies such as:
Artificial intelligence: Machine learning models and related software are used for predictive and prescriptive analysis.
Cloud computing: Cloud technologies have given data scientists the flexibility and processing power required for advanced data analytics.
Internet of things: IoT refers to various devices that can automatically connect to the internet. These devices collect data for data science initiatives. They generate massive data which can be used for data mining and data extraction.
Quantum computing: Quantum computers can perform complex calculations at high speed. Skilled data scientists use them for building complex quantitative algorithms.
Prerequisites for Data Science
Here are some of the technical concepts you should know about before starting to learn what is data science.
1. Machine Learning
Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.
Data science Course Details
How can I become a data scientist, data science engineer, and what is the precise process to become a data science professional? is an inquiry that a significant number of you have. If you don’t have a technological background, you can take a short-term Data Science Course or pursue a diploma in data science to become a professional in the field.
Additionally, you must pursue a relevant degree in data science in order to pursue a postgraduate diploma in the field. To qualify, you must have a background in science, technology, math, or engineering.
You must have completed at least the 12th grade and have a foundational understanding of computer science, statistics, and mathematics in order to enroll in a data science course. If you don’t have a technical background, you can take a brief data science course in addition to those that include coding to better comprehend the ideas of data science. For students who have never coded before, some schools provide a data science course; for those individuals, separate coding sessions are organized.
Numerous programming languages, including Java, Python, SQL, R, Scala, and many others, are used in the data science courses that are available. On the other hand, programming languages like Python and R are frequently used in data science courses.
When comparing Python and R programming, applicants with both technical and non-technical backgrounds prefer Python. R programming, on the other hand, favors those with a computer background and necessitates coding ability.
An overview of data science and a quick introduction to it are included in the data science course syllabus. You will learn about the entire application process and the concepts included in data science, such as data extraction, data cleansing, exploratory data analysis, research, statistical inference, regression models, and machine learning, among others, depending on the programming language you choose, whether it’s R, Python, or any other language.
What is the role of Data Science Professional
Data scientists are quantitative professionals that use their knowledge of social science and technology to identify patterns and manage data. They search for answers for business issues involving their mastery in the business, their perception of the specific situation, and their wariness of assumptions.. They also have the greatest impact on the advancement of artificial intelligence and machine learning.
Python, Scala, C/C++, SQL, and Java are just a few of the programming languages that a data science expert needs to be proficient in. These coding languages help in the association of unstructured informational indexes for data scientists.
A Career in Data Science
The remuneration structure is not the only consideration if you’re hoping to land one of the most wanted data science jobs in India. An appealing career has potential for growth, job security, and good reputation.
If you’re interested in finding out which data science positions pay the most, There are many employment openings in the data science field that don’t require a lot of experience, providing the applicants meet the requirements and exhibit the appropriate skill sets. I’d also want to provide a few well-known job titles for your reference.
- Data Science Intern
- Data Scientist
- Machine Learning Engineer
- Applications Infrastructure Architect
- Data Analyst
- Enterprise Architect
- Data Architect
Why is it Important for Students to Learn about Data Science?
Data science is an amalgamation of programming, statistics, mathematics, data analytics and probability. It structures huge amounts of incoming data, models it and streamlines it so it can flow to a specific destination. Businesses need meaningful and accurate data if they are to enhance productivity and profitability.
Students studying in the field of data science are sought after professionals in the job market today. Robotics, healthcare, finance, and security are just some of the fields where data science is necessary.
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Pursuing A Career In Data Science
Looking to pursue a career as a data scientist? This is a field that many people are interested in because the job prospects are so good, with data playing such a pivotal role in modern-day business. Data scientists can benefit from great job security, lucrative pay, career development opportunities, and rewarding and interesting work. While there are many perks to a career as a data scientist, you should also know that it is not an easy job and will be competitive. With this in mind, keep reading for a few pieces of advice that should come in useful for anyone looking to pursue a career as a data scientist.
Technical skills are hugely important in this field. When you are pursuing a career in data science, you will want to choose one language and be proficient in it as opposed to having basic knowledge of a few. The technical skills that you should start working on as early as possible include cloud computing, Java and Python, DevOps, big data, and Linux, just as a few examples.
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What Are the Skills You Need to Enter the Data Science Market?
More and more companies have become reliant on data science and analytics. When journalists pay as much heed to data scientists as they do political pundits during election years, it’s clear that the data science industry has become a high-visibility, high demand profession. The relative newness of this emerging career path has made the type of education needed to pursue it not as definable as other more traditional markets. Though every company values such skills and tools differently from one another, this is a discernible pattern for those who have found success in the data science market. The following list of qualifications is just the tip of the iceberg as to what type of skills and training can lead to a lucrative role in this field.
- Soft Skills
- Domain knowledge
- Communication and influencing skills
- Technical skills
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Data Science Tips For Beginners
Today, an organisation collects and creates enormous volumes of data, and a data scientist applies a multidisciplinary approach to extracting actionable insights from that Data. The data science field includes preparing, processing, and analysing data to reveal patterns and presenting the results to enable stakeholders to reach informed conclusions. To prepare data for processing, it can be cleaned, aggregated, or manipulated.
Developing algorithms, analytics, and artificial intelligence models are part of the analysis process. By turning data patterns into predictions, it enables businesses to make smarter, more informed decisions. Its software crawls through data to find ways and then converts them into forecasts. Tests and experiments designed to validate the accuracy of these predictions should be conducted. To make these results easily accessible, data visualisation tools should be used. These tools make it possible for anyone to see patterns and see trends in data.
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Data Science books
In-Demand Data Science Careers
If you have a passion for computers, math, and discovering answers through data analysis, then earning an advanced degree in data science or data analytics might be your next step. Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one-half of one percent of U.S. employees.
Here are the top free Data Science Books for students and people must add to their list in 2023 in order to improve data science skills and to get data science jobs.
Things to know about Data Science
Data science is an increasingly vital discipline that has applications in almost every industry and sector, so getting to know a little more about it is certainly sensible, especially as its importance is only going to grow.
With that in mind, here are a few things to note about data science which should demonstrate the benefits it can bring to all sorts of organizations.
Data science consulting can help you get started
The good news is that with the help of a professional data science consultant you can accelerate the rate at which your business adopts the tools and methodologies used to extrapolate meaning from the vast reserves of information which most organizations are responsible for today.
Most of all by working with an outside contractor, this means you do not need to go through the rigmarole of hiring a full time team member to take on this role, which could be costly as well as potentially being overkill, depending on the scale of your operations.
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Math to Data science
The leap from simple math to data science is a phenomenal one, but it is worth trying because it can open a world of opportunities for you. Data scientists are making it big, which means you can make big money with this academic transition. The industry is opening up to Big Data technologies, and the scope is all set to grow in the future. But before you take the first step, you must have a fair idea of the journey you will have to navigate to explore the opportunity. Here are some tips that can help you take this transitional step successfully.
Read more about Math To Data Science- How To Make The Phenomenal Leap
Revolutionize business with Data Science
Data science is without a doubt a true business game-changer. We believe that implementing data science is one of the biggest milestones your company can take. With data science, your work is more accurate and efficient, your assets are better used and protected, and finally – you can significantly reduce diverse operational costs. It really sounds interesting, doesn’t it? In this article, we want to show you how you can revolutionize your company using data science.
For obvious reasons, it’s impossible to squeeze the entire subject of data science in business in one short text. Therefore, we want to show you just five crucial aspects of running a business with data science support.
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Data Science and How Data Scientists Add Value to Business
No matter what industry you’re in, you certainly have a lot of business data. You gather data about your customers, competitors, products or services, job candidates, employee performance, and various day-to-day operations.
But how well do you understand all that data? Do you leverage business analytics to follow it and gain actionable insights from it? Or do you simply look at the numbers and play the guessing game to try and identify opportunities?
Even if you know how to interpret all the business data you gather, data scientists know how to dig deeper. That’s especially important when it comes to unstructured data.
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Data Science Trends and Predictions For 2023
Data Science is one of the fastest-growing areas within the technology industry. Data science platform is changing the way we approach data and analytics in both the workplace and in our day-to-day lives. Here are the most important Data Science trends and predictions that will affect the way we use data and analytics to drive business growth in 2023.
- Small Data and TinyML
- Data-Driven Customer Experience
- Deepfakes, Generative AI and Manufactured Information
- Automation of Machine Learning – AutoML
- Data Science on The Cloud
- Increase in Use of Natural Language Processing
- Use of Augmented Analytics
- Focus on Edge Intelligence
- Quantum Computing for Faster Analysis
- Democratizing AI and Data Science
Read more about Data Science Trends and Predictions For 2023
Applications of Data Science in Business
The following are some important areas of the application of data science to business:
Decision making: Decision making is certainly an anchor point in any self-respecting business; for anyone with a business it is important to make the right decisions at the right time.
Sales optimization: It is clear that the goal of any salesperson is to optimize their income; this optimization can be done in a scientific way.
Stock market forecast: Trading and therefore stock market forecasts is an area of application of data science par excellence, and for good reason: a good trader will use all his technical faculties to study the stock market.
Optimization of website traffic: If you have a website that is not of high visibility on the internet, it is worth investigating the reasons why this visibility is low, and again: involved in data science, it studies and analyzes a set of data characterizing the most visited sites on the net and extracts the most useful information for direct application to your website, a technical practice which can be carried out fully by exploiting the possibilities offered by a ModelOps.
Advanced image, speech, or character recognition: Facial recognition algorithms on Facebook, speech recognition products, such as Siri, Cortana, Alexa, etc., and Google Lens are all perfect examples of data science applications in image, speech, and character recognition.
Gaming: Today, games use machine learning algorithms to improve or upgrade themselves as players move up to higher levels. In motion gaming, the opponent (computer) is able to analyze a player’s previous moves and accordingly shape up its game. This is all possible because of data science.
Augmented reality (AR): Augmented reality promises an exciting future through Data Science. A VR headset, for example, contains algorithms, data, and computing knowledge to offer the best viewing experience.
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Who is a Data Scientist?
Data Scientist is a term recently coined to define a person who is able to play with the data by applying scientific tools to draw significant results. The term has become popular over the past few years owing to the growing applications of data analytics! However, it is still difficult to give an exact explanation of who is a data scientist!
Usually, a data scientist is considered to be the same as a data statistician or a data engineer. But, as a matter of fact, a data scientist is the one who knows little about both these fields and is able to apply his findings on a corporate level.
Data scientists are excellent mathematicians with extensive cross-disciplinary knowledge and analytical skills. This specialist’s job is to identify the best formula for teaching artificial intelligence. They should search among all current algorithms for the one that is most suited to fixing the project’s difficulties and determining what is going wrong.
Roles and responsibilities of a Data Scientist
A data scientist can use a range of different techniques, tools, and technologies as part of the data science process. Based on the problem, they pick the best combinations for faster and more accurate results.
A data scientist’s role and day-to-day work vary depending on the size and requirements of the organization. While they typically follow the data science process, the details may vary. In larger data science teams, a data scientist may work with other analysts, engineers, machine learning experts, and statisticians to ensure the data science process is followed end-to-end and business goals are achieved.
However, in smaller teams, a data scientist may wear several hats. Based on experience, skills, and educational background, they may perform multiple roles or overlapping roles. In this case, their daily responsibilities might include engineering, analysis, and machine learning along with core data science methodologies.
How to become Data scientist
Data Science / Data Analytics / Business analytics is all about analyzing the data, which is getting generated through multiple sources. Sources range from traditional databases to satellite signals to sensors in Internet of Things, and the list will go endlessly. Easier asked question is, “Where is data not getting generated?” Also the technological advancements are happening at a pace, which will leave us dumbstruck
Analyzing such wide variety of data, which is getting generated at a rapid continuous pace, requires extraordinary reasoning and skills. To cater to these needs, one should have knowledge about 4 important areas of study, which includes Statistical Analysis, Data Mining, Forecasting (Time series) & Data Visualization.
Learning how to become data scientist can be quite costly, with an average cost of $9,600 (according to extension.harvard.edu). But if you know which skills employers are looking for you can find many free resources online. That is exactly what we did for you! Below is the required skills set for becoming a data scientist with top free resources to learn each skill online.
Software Engineer Vs Data scientist
Both data scientists and engineers must be responsible for the issue and must try to solve the issue at any step of the work. Continuous communication ensures that possible discrepancies are recognized in the early stage. In this article, we will look at the challenges faced by software engineers and data scientists throughout the process and how their teamwork can be improved for the best results.
Challenges faced by Software Engineers and Data Scientists and ways to solve them
Scientists assist engineers in developing analytical and research abilities to build better code by working closely with data. The interchange of information between users of data warehouses and data lakes is increasing, making projects more adaptable and giving longer-term benefits that are more sustainable.
The data scientist and the engineers have two goals: enhancing the products for consumers and improving the business’s choices. However, during the process, many challenges arise and experts must collaborate to address them:
Skills of Data scientists
Since 2012, the data scientist’s role has grown by over 650%, and by 2026, there will be 11.5 million jobs in this field. The field has become more lucrative than before, painting an optimistic picture for the jobs in 2022 and beyond. The recent openings in the tech industry are more about the machine or artificial learning.
The following diagram shows the skillsets required for a Data Scientist. As we can see, this responsibility is a combination of multiple skillsets and expertise compared to a typical Big Data Developer or Business Analyst.
Data science is one of the lucrative careers that’s gaining quite a traction in tech. Just like any other career, getting to pro-level data science takes time, effort, and lots of passion. As a beginner in the world of data, you are perhaps wondering what it takes to get to that level. Well, this quick guide gives you some essential tips to help you grow into a pro. Besides your academic qualifications, the following skills can help you excel in your career in Data Science.
- Python (Pandas, Numpy, Scipy, matplotlib, Seaborn)
- Machine learning
- Data visualization
- Business knowledge
Read more on Essential Skills You Need To Be A Data Scientist!
Tools of Data Scientist
The data science profession is challenging, but fortunately, there are plenty of tools available to help the data scientist succeed at their job.
- Data Analysis: SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
- Data Warehousing: Informatica/ Talend, AWS Redshift
- Data Visualization: Jupyter, Tableau, Cognos, RAW
- Machine Learning: Spark MLib, Mahout, Azure ML studio
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Qualities of Data scientist
So what makes a great data scientist?
The best data scientists go-beyond degrees (PhD or MS or BS) and trending technical skills (being born and raised on Hadoop); they embrace a true passion for problem solving. It’s much harder to teach someone qualitative skills, such as communications and curiosity, than it is to teach the latest algorithm or programming platform. Great data scientists are constantly evolving their technical and problem-solving skills to anticipate the next big data breach or to catch a hacker before they get to the consumer. Here’s more about what to look for in great data scientists:
- A passion for solving problems
- Hard skills and soft skills
- A team player
Read more on top 3 qualities of a great data scientist
SAS for Data scientist
One of the foremost skills that a data scientist must have is a good knowledge of SAS programming language and a certificate of clearing the exam. The ability of SAS to read data from various databases and its data handling is second to none. So, if you have good SAS training, handling big data becomes a piece of cake. SAS can pull parallel computations as well process data on RAM. You can easily use it for judging the probability of the distribution of data and complex simulations. Nowadays, enterprises look for candidates who can drive insights by analyzing the attained data easily.
Data scientist to know SQL
If you’re an aspiring data scientist or someone already working the field, you’ve probably heard about structured query language or SQL. People typically refer to it as “sequel” rather than by its full name or acronym. It’s the database language used when querying or managing relational database management systems (RDBMS).
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Devops for data science
However, most data scientists are, at heart, statistical analysts. While conducting their deep data explorations, they may not be focusing on their downstream production performance of the analytic models they build and refine. If the regression, neural-network, or natural language processing algorithms they’ve incorporated don’t scale under heavy loads, the models may have to be scrapped or significantly reworked before they can be considered production-ready.
Here’s where devops can assist. Devops is a software development method that stresses collaboration and integration between developers and operations professionals. It’s not yet in the core vocabulary of business data scientists, but it should be. Intensifying performance requirements on advanced analytics will bring greater focus on the need for rapid, thorough performance testing of analytic models in production-grade environments. As these needs grow, the mismatches in perspective and practice between data scientists (who may treat performance as an afterthought) and IT administrators (who live and breathe performance) will become more acute.
Read more on Devops can take data science to the next level
Apache spark for Data scientists
Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.
Data science is a broad church. I am a data scientist — or so I’ve been told — but what I do is actually quite different from what other “data scientists” do. For example, there are those practicing “investigative analytics” and those implementing “operational analytics.” (I’m in the second camp.)
Data scientists performing investigative analytics use interactive statistical environments like R to perform ad-hoc, exploratory analytics in order to answer questions and gain insights. By contrast, data scientists building operational analytics systems have more in common with engineers. They build software that creates and queries machine-learning models that operate at scale in real-time serving environments, using systems languages like C++ and Java, and often use several elements of an enterprise data hub, including the Apache Hadoop ecosystem.
And there are subgroups within these groups of data scientists. For example, some analysts who are proficient with R have never heard of Python or scikit-learn, or vice versa, even though both provide libraries of statistical functions that are accessible from a REPL (Read-Evaluate-Print Loop) environment.
Interconnection between Data Science and AI
Data Science and artificial intelligence are not the same, but they are one way or the other connected.
Data science is an interdisciplinary area that needs concepts and techniques like statistics, visualization, machine learning, etc. In general terms, Data science deals with the methods and processes which carefully examine, analyze and alter data. Data Science also endows artificial intelligence to identify relevant information and gather massive amounts of data with greater efficiency and speed. To define data science and artificial intelligence, it’s easier to get confused than to understand the actual meaning of these two as each of them are quite vast and complicated at the same time. As a matter of fact, both of these terms are often discussed by highly interested people and hyped up rather than addressing the actual problems as a result the whole scenario gets more confusing and complex.
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Python For Data Science
Python is the most famous computer programming language used to develop software and websites, automate systems, and analyse data. Python is a general-purpose language that is useful in generating various applications and can’t be customised for any particular problem. Its scalability and novice-friendliness have lifted it to the best of the list of programming languages today worldwide. It got ranked as the second-most popular programming language among coders in a review by the market research company RedMonk. Python is a pen name for Monty Python. While creating Python, Guido van Rossum enjoyed Monty Python’s Flying Circus scripts from the BBC. He loved the name Python to be both simple and primarily enigmatic.
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Business Intelligence in Data Science
What is Business intelligence?
Business intelligence is strategically designed to help businesses make better decisions. And also, it’s a pillar for digital transformation. Any business looking to stay relevant and meet modern business demands must employ business intelligence practices, methodologies, processes, and tools for relevant data collection, storage, analysis, and decision-making.The goal of BI is to derive actionable intelligence from data. Some of the actions that BI may enable are:
- Gaining a better understanding of the market
- Uncovering new revenue opportunities
- Improving business processes
- Staying ahead of competitors
The most impactful enabler of BI in recent years has been cloud computing. The cloud has made it possible to process more data, from more sources, more efficiently than was ever possible before cloud technologies came into use.
Data science vs. business intelligence
It is helpful to understand the differences between data science and business intelligence. It is equally helpful to understand how they work hand in hand. It is not a matter of choosing one or the other. It comes down to selecting the right business intelligence tools to get the insights you are looking for. Most often, that means using both data science and BI.
Perhaps the easiest way to differentiate is to think of data science in terms of the future and BI in terms of the past and present. Data science deals with predictive analysis and prescriptive analysis, while BI deals with descriptive analysis. Other factors that differentiate are scope, data integration, and skill set.
|Data Science||Business Intelligence|
|Type of Analysis||Predictive, PrescriptiveWhat will happen?||DescriptiveWhat has happened?|
|Skill Set||Data scientist||Business analyst, Business user|
Use Cases of Data Science
Here are some use cases, showing data science’s versatility.
Law Enforcement: In this scenario, data science is used to help police in Belgium to better understand where and when to deploy personnel to prevent crime. With only limited resources and a large area to cover data science used dashboards and reports to increase the officers’ situational awareness, allowing a police force that’s spread thin to maintain order and anticipate criminal activity.
Pandemic Fighting: The state of Rhode Island wanted to reopen schools, but was naturally cautious, considering the ongoing COVID-19 pandemic. The state used data science to expedite case investigations and contact tracing, enabling a small staff to handle an overwhelming number of concerned calls from citizens. This information helped the state set up a call center and coordinate preventative measures.
Driverless Vehicles: Lunewave, a sensor manufacturing company, was looking for a way to make sensor technology more cost-effective and accurate. They turned to data science and machine learning to train their sensors to be safer and more reliable, as well as using data to improve their 3D-printed sensor manufacturing process.
Entertainment: Data science enables streaming services to follow and evaluate what consumers view, which aids in the creation of new TV series and films. Data-driven algorithms are also utilised to provide tailored suggestions based on the watching history of a user.
Finance: Banks and credit card firms mine and analyse data in order to detect fraudulent activities, manage financial risks on loans and credit lines, and assess client portfolios in order to uncover upselling possibilities.
Manufacturing: Data science applications in manufacturing include supply chain management and distribution optimization, as well as predictive maintenance to anticipate probable equipment faults in facilities before they occur.
Healthcare: Machine learning models and other data science components are used by hospitals and other healthcare providers to automate X-ray analysis and assist doctors in diagnosing illnesses and planning treatments based on previous patient outcomes.
Retail: Retailers evaluate client behavior and purchasing trends in order to provide individualized product suggestions as well as targeted advertising, marketing, and promotions. Data science also assists them in managing product inventories and supply chains in order to keep items in stock.
How do Top Companies are using Data Science?
Data Science is an amalgamation of several disciplines, including computer science, statistics, and machine learning. As the world on the internet is becoming our second home, Big Data has exploded. Data Science is the study of this big data to derive a meaningful pattern. All the businesses are now looking to explore this gold mine of information to solve already existing problems.Some of the biggest companies that are hiring data scientists at competitive salaries are listed below:
- Mu Sigma
Mistakes Companies Make with Data Science
Today, such concepts as “big data” and “real-time” analytics are strongly entering the enterprise world. Data-driven decision-making has proven to outperform intuition and questionable judgement. How can companies tackle advanced data technologies to achieve their goals? Which issues can they face? Here are several things to avoid on their data science path.
- The absence of defined metrics.
- Employing a wrong professional.
- Focus on the buzzwords.
- Leaving data quality issues unattended.
- A failure to apply agile management.
Read more about Top 5 Mistakes Companies Make with Data Science
Salaries and Data Science Jobs Available in Different Countries
The demand for data scientists is very much outstripping supply and that companies in the United States alone will need to hire between 140,000 – 190,000 data scientists if they are to keep up with the new data economy.
Ironically, there is a great deal of conflicting data on the average salary for a data scientist, however, what is clear is that the average salary does tend to be inherently concurrent with the high demand level for data scientists.An average salary of about $120,000 doesn’t seem too far fetched.
The average data scientist salary is $100,560, according to the U.S. Bureau of Labor Statistics. The driving factor behind high data science salaries is that organizations are realizing the power of big data and want to use it to drive smart business decisions. And because the supply of data professionals hasn’t yet caught up with demand, starting salaries for these positions remain high, especially for those who have an advanced degree in data science or a related field.
Germany: Data scientists in Germany can earn about €5,960 per month. The salary of a data scientist in Germany ranges from €2,740 per month to €9,470 per month. Germany offers the most lucrative salary packages for the role of a data scientist.
United Kingdom (UK): Similar to Europe and the US, various industries in the UK are now hiring skilled professionals to manage, maintain, and analyze large amounts of data. A data scientist in the UK can earn up to £50,000 p.a.
China: China is planning to lead the world in artificial intelligence by the year 2030 by investing in IT industries and making government policies more accommodating. An experienced data scientist in China can earn up to ¥350,000 p.a.
India: India has the fastest-growing industries in several sectors such as healthcare, defense, logistics, and artificial intelligence. Similar to the rest of the world, India too is facing acute challenges in finding skilled data scientists. So, if you have the right skills and experience as a data scientist, you can earn up to ₹1,200,000 p.a.
The field of data science is hugely fulfilling and offers extraordinary potential for future development. Data scientist has been dubbed the most promising vocation in the world because of the already high demand, excellent salary, and multiple benefits. The environment for data science is dynamic and remarkably similar to the actual, globally interconnected world. New inventions have been created and used for a very long time, and the transition is anticipated to continue for the ensuing ten years. Consequently, a rise in data science employment is to be anticipated soon.
Q.1 To pursue a job in data science, is certification required?
Ans. If you are proficient in coding and possess all the other necessary skills for a data science expert, you do not necessarily need a certification in the field. However, if you have a non-technical background, a certification in data science will help you brush up on all the ideas and assist you with implementation, and it will also enable you to take advantage of job opportunities.
Q.2 Is it possible to work in data science without knowing how to code?
Ans. No, if you don’t know how to code, you can’t pursue a job in data science. However, since you can learn to code while you study, a data science course is not dependent on prior coding experience.
Q.3 Which company employs data scientists?
Ans. There are numerous reputable and international companies, including IBM, Wipro, Cloudera, Deloitte, Numerator, Infosys, and many others, that are in need of data scientists.
Q.4 Do data science principles include machine learning?
Ans. Yes, data science uses machine learning and deep learning concepts, and you can learn useful machine learning techniques there.
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