Today's organizations are inundated with data. Even ten years ago, a gigabyte of data seemed large. However, some large organizations currently manage more than a zettabyte. To give you an idea of how much data that is, if your typical laptop or desktop computer has a 1TB hard drive, a zettabyte is equal to one billion of those hard drives. How can organizations even hope to derive any business value from this amount of data? He must be able to analyze them and identify the needles of valuable knowledge in the almost endless haystack. This is where the combination of data science, machine learning, and artificial intelligence has become remarkably useful; you don't need a zettabyte of data to make these three things relevant.
While data science vs machine learning vs artificial intelligence (AI) is creating revolutions across industries, there is still a significant amount of doubt over these concepts. To learn more, check out What is a Data Science Course? The three are interrelated, but have different scopes, use different approaches, and achieve different results depending on your industry.
Organizations are constantly looking for data science and data analytics professionals, so it goes without saying that the best time to explore and utilize these fields is now. And here you can start! In this article, we will talk about the sensational debate of data science vs machine learning vs AI. Now let's look at the difference between data science and machine learning, and artificial intelligence with a comparison chart and then look at each topic separately.
Data Science vs Artificial Intelligence vs Machine Learning [Comparison Table]
|Criteria||Data Science||Machine Learning||AI|
|FOCUS||Extract meaning from structured and unstructured data to take decisions and decide further business strategy||Enable computers to perform complex intellectual tasks like humans, including decision-making, problem-solving, perception, and understanding human communication.||Provide a way for systems to process the data, learn from it and use the insights to improve day by day|
|What is required/What do they use?||Data science deals with structured and unstructured data||Machine Learning uses statistical models. ||Artificial Intelligence uses logic and decision trees.|
|What it achieves||Helps us find out the hidden pattern in data||Predict the future based on certain criteria||An autonomous system that can run without human intervention|
|Example of Tools||R, Python, Excel are the tools used in data science.||R, Python, Excel, and sci-kit learn are the tools used in Machine learning||Tensor flow, sci-kit-learn, and WEKA are the tools used in AI|
|Application||Its applications are advertising, marketing, Healthcare, etc.||Its applications are Weather Prediction, Stock market Prediction, etc||Its application are chatbots, autonomous systems, and robots|
Now let’s look at each field in detail, we will start with data science
Difference Between Data Science and Machine Learning and Artificial Intelligence (AI)
A) Data Science
Data science is a field of study that combines industry expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. To perform tasks that normally require human intelligence, data scientists apply machine learning algorithms to numbers, text, images, video, audio, and more to create artificial intelligence (AI) systems. These systems, in turn, generate insights that analysts and business users can translate into tangible business value.
But why is data science so important?
Data is not useful until it is transformed into valuable information.
- Data Science involves mining large datasets containing structured and unstructured data and identifying hidden patterns to gain actionable insights.
- The importance of Data Science lies in its myriad uses, from everyday activities such as asking Siri or Alexa for recommendations to more complex applications such as controlling a self-driving car.
- It also enables businesses to measure, track and record performance metrics to facilitate enterprise-wide decision-making. Companies can analyze trends to make critical decisions to engage customers, increase company performance, and increase profitability better.
- Data Science models use historic data, and they can predict future actions. Thus, companies can devise a way to reap the best business results.
- Data Science helps organizations identify and refine target audiences by combining existing data with other data points to generate actionable insights.
2. Prerequisites & Skills
There are certain skills you should acquire to have a strong career in this amazing field
- Inclination towards data: Know how much you are inclined towards data. Do you generally work with Excel sheets, tables, and unstructured data? Are you comfortable handling data? Since you will be handling data for the longer part of your career as a data scientist, your interest in data is of prime importance. Rather than skill, this is the first prerequisite to starting a career in data science.
- Programming knowledge: You should have a basic understanding of the various aspects related to programming, conditional status statements, loops, functions, etc. If you know what terms such as client service, deployment, databases, API mean and program codes interest you, you may enjoy data science.
- Statistics: How comfortable are you with statistics? Does the concept of distribution Probability and Bayes theorem strike a chord with you, or would you rather stay away from another hypothesis testing Chi-square? This is a major determinant of whether a youth enjoys Data Science as a career choice.
- Knowledge of the business domain: It is all right if you are just starting off with data science and you don't know much about business. however, the greater understanding you have as to how a business operates, the better it is for you in your pursuit of Data Science.
3. Job Roles
To start a career in data science at an entry-level most of the time you will get a job as a data analyst and then slowly you can move up to the ladder and start doing complex operations using statistics and programming. Data science typically is a broader term and many careers fit under its umbrella such as data analyst, Data engineer, Business analyst, data scientist, and so on.
The average data science salary per month in the United States is $8,008, according to PayScale. With less than a year of experience, an entry-level data scientist can make approximately $69,000 per year. Mid-level data scientists with 1 to 4 years of experience may expect to earn about $89,000 per year. Check more about Data Scientist Salary here!
B) Machine Learning
- Machine learning is a branch of the broader field of artificial intelligence that uses statistical models to develop predictions. Often described as a form of predictive modeling or predictive analytics, it has traditionally been defined as the ability of a computer to learn without being explicitly programmed to do so.
- In basic technical terms, machine learning uses algorithms that take in empirical or historical data, analyze it, and generate outputs based on that analysis. In some approaches, algorithms first work with so-called "training data" and then learn, predict, and find ways to improve their performance over time.
- In one approach, ML systems also learn about data by finding similarities between them and making predictions based on that.
- Machine learning is important because it provides businesses with insight into trends in customer behavior and business operating patterns, as well as supports new product development. Many of today's leading companies, such as Facebook, Google and Uber, are making machine learning a central part of their operations.
- Machine learning has become a significant competitive element for many companies.
2. Prerequisite and skills
Below are some of the prerequisites for a career in machine learning
- Statistics: Statistics contains tools that can be used to get some results from the data. There are descriptive statistics are used to transform raw data into meaningful information. Inferential statistics can also be used to extract important information from a sample of data instead of using the full data set.
- Linear algebra: Linear algebra deals with vectors, matrices and linear transformations. It is very important in machine learning because it can be used to transform and perform operations on a dataset.
- Calculus: Calculus is an important area in mathematics and plays an integral role in many machine learning algorithms. Multi-feature datasets are used to build machine learning models because the number of multivariate features plays an important role in building a machine learning model. Integration and differentiation are a must.
- Probability: Probability helps predict the likelihood of events occurring and helps us reason about whether a situation may or may not happen again. For machine learning, probability is fundamental.
- Programming language: To implement the entire process of machine learning, it is essential to know programming languages like R and Python. Both Python and R provide built-in libraries that make implementing machine learning algorithms very easy.
In addition to basic programming knowledge, it is also important that you know how to extract, process and analyze data. This is one of the most important skills required for machine learning.
3. Career Path & Job Role
As a machine learning engineer, you will work on many real-world problems and make a lot of predictions to help businesses get better at what they do by predicting potential answers for them. For example, detecting credit card fraud, predicting rainstorms, building personalized recommendation engines, and so on.
There are many tools you can learn to do predictive analytics, like Pytorch, Knime, Weka, TensorFlow, and collab
The average annual salary of a machine learning engineer is $129,359. Machine learning engineers with less than a year of experience earn around $66,635 per year, clearly one of the highest entry-level salaries.
The average salary of a mid-level engineer is $141,066 per year. If they think it's great, you will find the salary of top engineers (more than 10 years of experience) is inspiring as they earn $154,438 per year. ML engineer salary may vary depending on the city or the experience a person may have.
C) Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence in machines programmed to think like humans and mimic their behavior. The term can also be applied to any machine exhibiting properties related to the human mind, such as: Learning and problem-solving. The ideal property of artificial intelligence is its ability to streamline and execute actions that are most likely to achieve a specific goal. A subset of artificial intelligence is machine learning (ML). It refers to the concept that computer programs can automatically learn and adapt to new data without human assistance.
Artificial intelligence is constantly changing, not only in its definition but also in its scope and capabilities. Decades ago, a simple calculator was considered artificial intelligence. Today, however, AI underpins home automation, with new AI releases almost every week that scientists are trying to improve the user experience. A good foundation is essential to mastering AI
To learn artificial intelligence, you need the following prerequisites:
- Strong in Mathematics and Algorithms
- Physics, Engineering, Robotics
- Some knowledge of computer science, programming languages, and coding
- Python/ C++/ Java (computer languages)
- Good data analysis skills
Artificial Since intelligence is a research field that realizes human intelligent behavior on computers; the ultimate goal is to develop computers that can learn, plan, and solve problems autonomously.
3. Career Path
- As artificial intelligence is an increasingly pervasive and growing form of technology, professionals specializing in AI are needed more than ever. Fortunately, there are many different career opportunities in the AI career field. This means you can have different roles and responsibilities depending on your position, experience and interests. Bored with your current role and want to learn more about AI checkout KnowledgeHut's what is Data Science course?
- if you're not already in the field, the first step is to do your research, which includes interviewing current AI professionals and researching reputable colleges and programs. hiring managers will likely require you to have at least a bachelor's degree in math and basic computer technology (but in many cases, a bachelor's degree only qualifies you for entry-level positions).
- Bachelor's degrees in computer science or engineering are a good starting point, but a master's degree in artificial intelligence can provide first-hand experience and knowledge from industry experts that can help you secure a position and set you apart from other candidates.
4. Job Roles
Artificial intelligence specialists are working to bring diversity to the system. An important role of an AI specialist is to add cognitive behavior to machines through various test cases. In the last few years, artificial intelligence is a hot topic not only in India but all over the world. With advances in data science, artificial intelligence is making great leaps. Institutions across India are joining the AI stream.
The role of artificial intelligence varies according to the applications required. An entry level candidate can expect a package of $107,458 per annum. With 2-4 years of experience the salary can range from $137,368 per annum. A senior level specialist with more than 5 years of experience has a median salary in the range of $139,246 per annum.
How Data Science, Machine Learning, and AI are Similar?
Artificial intelligence (AI) is the science of making machines think like humans and make decisions without human intervention. AI can do this using machine learning (ML) algorithms. These algorithms are designed to allow machines to learn from previous data and predict trends. Data Science, on the other hand, is an entirely different and larger subject. It is the complete process of collecting, analyzing, filtering , and using relevant data to find solutions to real-world problems and business challenges. AI and ML are just two of the many techniques used in the data science process. Want to transition your career into data science? Attend a Data Science Bootcamp Online.
AI vs Machine Learning vs Data Science: How They Work Together?
Data is the common denominator of data science, artificial intelligence, and machine learning. All three fields we have discussed are different from each other based on their applications, but they are all connected through DATA.
- Data science, as the name suggests, is the science of collecting, understanding, analyzing and processing data to generate valuable insights that can be used to solve various real-world problems and improve existing business models.
- Today, it is mainly artificial intelligence (AI) and machine learning (ML) tools and technologies that help data scientists to obtain these useful insights, make decisions and use them effectively. Yippee. The most prevalent technology is predictive analytics, where machine learning, artificial intelligence and data science work closely together to produce flawless results that benefit businesses.
Let's understand this with a simple example.
- If you use any of the YouTube, Netflix, Flipkart or Spotify apps, you know that these apps provide recommendations for your favorite videos, movies, products or songs. must. how do you think this will happen? Data scientists in the company
- collect data, such as browsing history, from these apps and feed it to machine learning algorithms that learn each consumer's behavior based on that data. The AI app then makes decisions based on learning ML algorithms and displays the best results to the consumer.
- A data scientist may know that it is considered one of the best jobs of the 21st century. With the exponential growth of big data every day, the scope of this field is huge.
- This exponential growth has created a huge gap between supply and demand for data science professionals worldwide. According to a report by Analytics India Magazine (AIM), there were more than 90,000 job vacancies in the field of analytics and data science in India alone in 2020.
- This alone explains the wide variety of careers in data science. With increasing digitization and widespread adoption of data science, there is an urgent need for experienced data science professionals in the industry.
- Let's talk about artificial intelligence (AI) and machine learning (ML). So, as mentioned above, data science, Machine learning, and Artificial intelligence are somehow connected. In fact, the most important application of AI and ML is data science.
- Even data science relies heavily on AI and ML, with more than half of its applications and focus related to AI and ML. That being said, most data science jobs are for AI and ML professionals. However, AI and ML are not only used in data science, but also in other important and dynamic fields such as robotics and cyber security. All of these fields can be highly advanced and technical, making them highly lucrative jobs.
The field of data science, machine learning, and AI is constantly evolving, with new technologies, trends, research and applications emerging every day. Therefore, a career in these fields guarantees longevity and security. This is because if there are people on earth and data in the world, work will not progress. So, you can be sure. However, companies need qualified and experienced professionals. This means that if you want to pursue a career in any of these fields, you should study these subjects intensively. So, choose a career in this industry based on your interests and what suits you best.