The Department of Artificial Intelligence and Data Science (AI&DS) will be established with 60 intakes for the academic year 2020-2021. The department is approved by AICTE, New Delhi, and affiliated with Visvesvaraya Institute of Technology, Belagavi.
Artificial intelligence and data science are scientific methods, processes, and diagrams drawn from various disciplines, such as statistics, cognitive science, computing, and information science, to extract knowledge from structured and unstructured data. This is a new field of research that is related to technology. This knowledge is used in making various intelligent decisions in business applications. AI&DS focuses on collecting, classifying, strategizing, analyzing, and interpreting data. It is a specialized division that deals with the development of data-driven solutions, data visualization tools, and techniques to analyze big data. It also covers machine learning and deep learning model-building concepts to solve a variety of computational and real-world problems.
In today’s digital world, terms like artificial intelligence and data science are often used interchangeably, but they are not the same thing.
Although both are branches of computer science, there are many differences between them. If you are interested in a career in technology, you can look into different aspects of data analysis to see which area interests you most.
This article will help you in the decision-making process. Explain the difference between artificial intelligence and data science. We will also discuss the salaries in these fields, the skills required, and how to start a career in artificial intelligence or big data.
What is artificial intelligence?
Artificial intelligence is essentially a branch of computer science that aims to replicate or simulate human intelligence within machines and systems. It is a multidisciplinary science with many perspectives, and advances in machine learning and deep learning are transforming many areas of the IT industry around the world. Machine learning and deep learning are commonly mentioned together with artificial intelligence, which is considered a subfield of artificial intelligence. These streams essentially involve algorithms that make predictions or classifications by creating expert systems based on input data.
There are two types of artificial intelligence.
- Weak AI (also known as narrow AI) is a form of AI that is primarily trained and focused on performing only specific tasks. Much of the AI around us today is applications of weak AI, such as Facebook’s recommended news feed, Amazon’s recommended purchases, Apple Siri, and Amazon Alexa, the technology that answers users’ voice questions. Email spam filters that you enable or use in your mailbox are also examples of weak AI that use algorithms to classify spam emails and move them to other folders.
- Strong AI has two components: artificial general intelligence (AGI) and artificial superintelligence (ASI). AGI (General AI) is a form of AI in which machines theoretically rival human intelligence, allowing them to be self-aware and conscious, allowing them to solve problems, learn, and predict the future. Creates the ability to make strategies for ASI (superintelligence), which is considered to exceed human intelligence and the capabilities of the human brain. Strong AI is still purely theoretical and there are currently no examples of its use in practice. Researchers are still studying its development; their goal is to create intelligent machines that are indistinguishable from the human brain.
Learn more about artificial intelligence
What is Data Science?
Simply put, data science is generally the vast study of processing large amounts of data to identify patterns (visible and invisible), draw conclusions, and derive meaningful information and insights. This information will help you make informed decisions and plan your strategy accordingly. The insights generated through this process of data science enable companies to identify new opportunities, increase the efficiency and effectiveness of their operations, improve their current strategies to expand their portfolio, and strengthen their market position.
Data science efforts from an operational perspective can help organizations optimize various aspects of their business, including supply chain management, inventory segregation and management, and demand forecasting. This allows companies to focus on creating holistic, data-driven business strategies and plans. Analysis of customer behavior, market trends and competition. Essentially, we can conclude that if data science is not applied in business, companies will miss out on all the opportunities in the world and make wrong decisions.Â
What is data science? Everything is here
Let us now look into the differences between artificial intelligence and data science:Â
Key differences between Artificial Intelligence and Data Science
| SI | Parameters | Artificial Intelligence | Data Science |
| 1 | Basics | Implementation of a predictive model to measure or classify future events or trends. | It includes processes such as data ingestion, analysis, visualization and communicating the insights obtained. |
| 2 | Skills | Strong foundation in mathematics and statistics and knowledge of programming, complex machine learning and deep learning algorithms and concepts. Â | Logical reasoning, programming skills, database management skills, and strong presentation skills are needed to convey insights in a meaningful way. |
| 3 | Goals | The research goal of AI is to enable computers and machines to function intelligently. | Recognize hidden patterns in your data to generate meaningful discoveries and insights |
| 4 | Purpose | The purpose of AI is to provide software that is powerful enough to reason about given inputs and explain outputs. | Use findings and insights to make informed decisions |
| 5 | Types of Data | Visual, textual, and numerical data are data formats that can be used to train algorithms in ML or DL within AI. | Various types of data can be used as input for the data science lifecycle. Structured, unstructured and semi-structured data input formats are |
| 6 | Scientific Processing | The process followed here is to focus on creating model objects that can be input into the model to generate outputs. The model is too complex | It generally follows fixed stages of the data science pipeline, covering all stages from data ingestion to insight dissemination. Requires advanced scientific processing |
| 8 | Techniques Used | Machine learning and deep learning concepts are widely used  | Statistical methods and data analysis are the core of the methods used |
| 9 | Tools Used | The tools are used to enable the execution of complex machine learning and deep learning algorithms. Â | Common tools used in DS are: 1. SQL for data migration 2. Python libraries like Pandas, NumPy and Plotly for data exploration and statistics, and visualization tools like Power BI and Tableau. |
| 10 | Examples | AI is being applied to cutting edge technologies such as voice recognition devices like Amazon Alexa and Apple’s Siri Recommendation Engine. Netflix viewing suggestions, Amazon product catalog suggestions  | Data Science Examples: 1. Increase sales by predicting future demand 2. Effective campaign management with customer segmentation 3. Fraud Detection and Prevention |
Differences between Artificial Intelligence and Data Science on the basis of job profile
If you take a look at job postings, you will see that artificial intelligence jobs are growing rapidly due to the widespread use of artificial intelligence in industries like pharmaceuticals and retail. Similarly, positions in data science are growing, highlighting the growing need for professionals who can leverage data to make informed decisions. Let’s take a look at these roles:
Role of artificial intelligence
1. AI or Machine Learning Engineer
The role of a machine learning engineer involves developing machine learning and deep learning models and retraining systems. It also involves the creation of algorithms based on statistical modeling that can be further used as scalable solutions. ML engineers focus on designing self-running software that can operate the entire process. ML engineers work closely with data scientists across the entire data science pipeline.
ML engineers need programming experience in Python and R, as well as strong data modeling and data architecture skills. You should also have knowledge of ML frameworks like TensorFlow and Keras.
2. Researcher
The ideal candidate for this position will have recognition in one or more of the following research areas: applied mathematics, computational statistics, artificial intelligence, machine learning, deep learning, graphical models, computer recognition, natural language processing, and data representation. I am an expert.
In addition to programming literacy, it is essential to know how to write in several different languages and have a solid understanding of data structures and basic algorithms. AI programming relies heavily on the use of probability, statistics, calculus, and other complex concepts, making it a bastion of mathematical and statistical skills.
3. Robot Scientist
Robotics engineers create prototypes, build and test machines, and update the software that manages them. Additionally, we are researching the most economical and safe ways to build robotic systems. You also need to have in-depth knowledge of flexible automation and computer systems and the ability to optimize cost and efficiency.
Similarly, mathematical and statistical skills and in-depth knowledge of algorithms are required here. Programming high-level robotic systems requires highly complex and specialized AI and ML techniques.
Data Science Job Roles Â
Let’s start with data science. First, let’s quickly understand by listing the different roles found in data science.
1. Data Analyst
Data analysis involves the process of cleaning, analyzing, interpreting, and communicating the results and insights through appropriate contextualization and an appropriate set of tools. A data analyst is a professional who can perform all the tasks mentioned in the process of data analysis. This role can also be defined as a person with the knowledge and skills to generate conclusions and insights from the available raw data.
The skills you definitely need here are a good foundation in programming languages like SQL, SAS, Python, and R.A.
2. Data Engineer
Professionals with data engineering and programming expertise work to collect and transform raw data and create systems that can be used by businesses. Additionally, these systems and datasets are maintained so that they are accessible and easily available for further use. We are also looking at implementing methods to improve data readability and quality, as well as developing and testing architectures that enable data extraction and transformation.
Requires technical expertise in concepts such as data mining, data models, and segmentation, as well as a solid understanding of SQL and database operations.
3. Data Scientist
Basically, you can think of a data scientist as someone who understands business challenges and can provide solutions that can be implemented. Data scientists are generally responsible for all tasks that are part of the data science pipeline, providing findings and insights to the business in the most effective way.
Skills like data mining, data warehousing, mathematics and statistics, and data visualization tools enable storytelling.
4. Business Analyst
Business analysts are experts in working closely with stakeholders to set goals, develop best practices for data collection, and evaluate current processes to discover areas of improvement to achieve desired results. It involves the definition of specifications and analysis requirements that establish the basis for further processes in the life cycle. Business analysts act as a bridge between the business and offshore teams of data analysts and data scientists.
Relevant skills to elicit requirements and draw business-relevant conclusions from data through data visualization tools such as Power BI, Tableau, etc.
Differences Artificial Intelligence and Data Science: Salary and Career PathsÂ
Following the same pattern, we’ll talk about data science career paths and salary ranges, then move on to the details of artificial intelligence.
Artificial Intelligence Career Path and Salary
Artificial Intelligence is a profession that is still in its infancy, but it is rapidly gaining recognition as a field with the potential to change the face of society. Here you will find information and details about the process you will perform as an AI Engineer and the compensation each role offers.
1. Introduction by an ML/AI Engineer
Because artificial intelligence is deeply tied to machine learning and deep learning algorithms, entry-level ML/AI engineers often design and build models in teams. Professionals in the machine learning engineering field require advanced education and technical experience to advance their careers.
Gaining experience is essential to success as a machine learning engineer. Working on theoretical models of the real world helps engineers gain experience and develop practical capabilities. Like other areas of science and technology, there is a lot of trial and error going on in this field too.
Entry Level ML/AI Engineer Salary in India: 4 to 6 LPA
2. Increase your rank
By working consistently and acquiring the necessary skill sets, ML/AI engineers can easily advance to junior and senior ML/AI engineers. These senior-level engineers master deep learning and complex machine-learning algorithms.
My approach to becoming a senior machine learning engineer is to continuously strive to learn new technologies and AI-related capabilities. For machine learning engineers, promotion to a senior level has many benefits, including increased respect and the exciting work expected of them.
Senior ML/AI Engineer Salary in India: 15-20 LPA
3. Senior ML/AI Engineer. Where should I go next?
As a senior-level Machine Learning Engineer, you can build a long and successful career in your current location, helping to create and implement intelligent systems that impact the daily lives of millions of people and shape the future.
Experts say the field is expanding and changing, so there are good reasons for senior ML/AI engineers to stay in their current positions. Millions of machine learning engineer jobs are expected to be created over the next 10 to 15 years as automation is seen as a way for organizations across all industries to enhance their operations.
Senior ML/AI engineers can advance to become project leaders or advance to the position of head of AI within their organization.
Project Leader/Chief Divisional Engineer Salary in India: 25 LPA
Data Science career path
Currently, one of the most lucrative careers in this field is Data Science. This position is in high demand across many industries, and there are many vacancies available. Companies are hiring data scientists in large numbers. 70% of job advertisements in the analytics ecosystem are for data scientists with less than 5 years of professional experience. Let’s take a look at the career paths of data scientists, data engineers, and business analysts.
1. Data Scientist
This is the most sought-after role in our industry by both recruiters and job seekers. The careers of data scientists and data analysts are similar in many ways but differ based on their purpose.
2. Data Engineer
In any organization, data engineers are the backbone of all data systems within the organization. In most organizations, data engineers are responsible for building data pipelines and ensuring that data flows properly so that information reaches the right departments. Career advancement in this case will be as follows:
3. Business Analyst
Business analysts are considered the best business analysts, data analysts, and scientists. This role requires a deep understanding of business needs and expectations.
The application used between artificial intelligence and data science
Real-world applications of AI
In today’s world, many real-world applications of AI exist. Here, we have discussed some of them below.
1. Speech Recognition: Also known as automatic speech recognition (ASR) or speech-to-text, it is a feature of natural language processing (NLP) that processes human speech as written. Many mobile devices have voice recognition built into their systems.
2. Computer Vision: In this field, digital images, videos, and other visual forms form the input into AI, allowing the computer to receive meaningful input based on the tasks it needs to perform. Convolutional neural networks power computer vision and find applications in medicine such as photo tagging and radiology image processing.
3. Recommendation Engine: AI algorithms help find various trends within historical consumption data, which helps end users develop efficient strategies to identify cross-selling opportunities.
Machine learning, or deep learning, is the study of developing techniques that use data to improve performance or inform predictions. Data science, on the other hand, is the study of data and how to extract meaning from it. A subset of artificial intelligence is called machine learning. This is the main difference between data science and machine learning.
Real-world applications of data science
As I explored data science, I realized that its applications extended far beyond my regular work. It is also likely to address many of the global issues identified as the Sustainable Development Goals (SDGs) as the world’s most pressing issues, and various government-funded research centers and business schools are working on this. Capture, analyze and use massive amounts of data to help develop products and services that solve fundamental global and humanitarian problems at scale.
The data science lifecycle includes the following stages and is accomplished through a variety of roles, tools, and processes:
- Data collection and ingestion phase: The lifecycle begins by collecting relevant data needed for the process. This data includes any type of data, structured or unstructured. This includes pictures, videos, social media, etc. Data compilation can be in the form of manual data entry processes, scraping from the web, or real-time live streaming data from various sensors located on multiple systems and machines.
- Data storage and pre-processing: Once an organization has established a data matching process, the data should be stored in different storage systems depending on its needs. Data storage can be done either on-premises devices or in the cloud. These are the two most commonly used storage methods in any organization.
Once the storage process has started, the next step is to invest time in cleaning, duplicating, transforming and combining the data using data integration technologies that further facilitate the ETL/ELT process. This step will be necessary as the data moves on to further analysis, but you won’t want to spend too much time on basic checking to clean the data.
- Analyze and generate findings and insights. The main objective of this step is to understand the data and see what it is trying to tell you. You can perform exploratory data analysis to explore your data and identify patterns, distributions, trends, ranges, and biases. A very common method for evaluating data is to perform hypothesis testing on the data. Create a hypothesis and test it against data to confirm its reliability.
This phase also leads to determining the relevance of using different techniques, such as predictive analytics, machine learning, and deep learning, within the modeling. All these processes generate findings and insights that enable informed decision-making.
- Communication: What good are the findings, insights, and recommendations gathered if they are not actually used or acted upon? So, here we will discuss how all these insights can be visualized or presented in another suitable format: creating reports, allowing companies to extract the value derived, and, in turn, making decisions. Helps people.
Typical applications in the field of data science include pattern recognition, anomaly detection, classification, predictive modeling, and sentiment analysis. This data science pipeline and its associated tools and technologies are at the core of all the responsibilities surrounding a data scientist. Job Description.
Conclusion 
We hope this article has helped you understand the basics of data science, artificial intelligence, and their applications. We also looked at the different job profiles found in these fields and how to progress in each field.
While it’s still an open option whether to go into either data science or artificial intelligence, each of these fields is enriching in different ways, including career paths, compensation, and the ability to make a big impact on a lot of people. That it provides an opportunity. Business, medical, and environmental issues. If you are a beginner and want to learn more about data science, choose Python with Data Science.
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