Data Science vs Artificial Intelligence

Data Science and Artificial Intelligence are often confused due to the interchangeable use of these terms. Data is the common element between them, with AI and Machine Learning falling under Data Science. It’s important to understand the subtle differences between them as well as the eligibility requirements for a Data Science course in order to better understand their applications and concepts.

What is Data Science?

Data Science is a field of study that uses large amounts of data to find meaningful patterns, gain fresh insights, and make better decisions. It assists companies in finding new opportunities, increasing efficiency and performance, improving growth strategies, and gaining an advantage against competitors.

Data Science allows businesses to make smarter decisions by analyzing data such as customer behavior, market trends, and competition. It can help optimize their operations, like supply chain management, inventory segregation and management, and demand forecasting.

What is Artificial Intelligence?

AI is a field of computer science that focuses on creating programs and systems that mimic human behaviour. It utilizes two main approaches, Machine Learning and Deep Learning, which enable computers to make decisions or categorize data. Combining these technologies with Artificial Intelligence creates intelligent systems that can be divided into two categories.

• Basic AI, also known as Narrow AI, is a type of artificial intelligence that is mainly trained to perform specific tasks. Examples of this type of AI can be found in everyday life – Facebook’s recommended newsfeed, Amazon’s suggested purchases, Apple Siri and Amazon Alexa are all applications of weak AI. Even email spam filters use algorithms to classify spam emails and move them out of our inboxes – another example of weak AI in action.
• Advanced AI is a combination of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI, also known as general AI, is an AI that can think and make decisions like humans. ASI, or superintelligence, is a level of intelligence that surpasses the capabilities of the human brain. Currently, Strong AI has only been theorized about – researchers are endeavouring to create an AI that works like a human mind.

What is the distinction between Data Science and Artificial Intelligence?

Data Science is a broad field which includes Artificial Intelligence, but AI is only one part of Data Science. Therefore, Data Science encompasses Artificial Intelligence, but not the other way around. To gain more knowledge about these topics, continue reading!

Data Science vs Artificial Intelligence

Data Science

• Data science involves ingesting data, analyzing it, visualizing the results, and communicating any insights that are found.
• Possess logical reasoning, programming, database management, and strong presentation skills which enable me to effectively communicate insights.
• Aim is to uncover patterns in data to gain useful insights.
• Using findings & insights obtained, make decisions based on them.
• Data Science lifecycles can use types of data as input, including structured, unstructured, & semi-structured data.
• Data science pipeline typically involves fixed set of steps from collecting data to sharing insights. Process requires high level of scientific processing.
• We can use data science to create complex models that rely on statistical analysis to uncover facts about data.
• Statistical methods and data analysis are the foundation of the techniques used.
• Popular tools used for data science include SQL for data migration, Python libraries such as pandas, NumPy, plotly for data exploration and statistics, and visualization tools such as Power BI and Tableau.
• Data Mining, Data Wrangling, Data Exploration and Visualization, and Programming are all essential skills for this field.
• Data Science uses popular tools like SQL, R, and Python.
• Common uses of machine learning include recognizing patterns, detecting anomalies, classifying data, making predictions, and analyzing sentiment.
• Models created here help make better decisions by providing insights.
• The uses of exploratory data analysis include deploying predictive models, and identifying patterns and trends in the data.
• Data Science can be used to increase sales by predicting future demand, managing campaigns more effectively through customer segmentation, and detecting and preventing fraud.

Artificial Intelligence

• Predictive models are used to predict future events & trends.
• Solid understanding of mathematics, statistics, programming, machine learning, and deep learning.
• Aim of AI is to make computers & machines able to think & act intelligently.
• The goal of AI is to create software that can take in data, think logically about it, and explain the results.
• Machine Learning and Deep Learning are forms of Artificial Intelligence that can use visual, textual, and numerical data to train algorithms.
• The goal of this process is to create a model object that can be used with models to produce results. These models are very complicated.
• AI can enable human understanding and cognition up to a certain level.
• Machine Learning and Deep Learning are widely utilized concepts.
• Tools are used to run complicated algorithms for machine learning and deep learning.
• Having a good understanding of Machine Learning and Deep Learning algorithms, as well as mathematics and statistics, is essential for working with Artificial Intelligence.
• Popular tools used in machine learning include Scikit learn, TensorFlow, and Cafe
• AI can be used for speech recognition, computer vision, and creating recommendation engines.
• AI models are designed to replicate human thinking, and they are much more complex than other types of models.
• AI can be used to automate repetitive tasks and to make predictions and forecasts based on historical and current data.
• AI is being used in advanced technologies like speech recognition devices (e.g. Amazon Alexa and Siri) and recommendation engines (e.g. Netflix watch suggestions and Amazon product catalog).

Difference: Data Science & Artificial Intelligence

• Data Science is used to process, analyze, visualize, and predict data. Artificial Intelligence (AI) takes this one step further by using predictive models to make predictions about future events.
• Data Science involved multiple statistical methods, but AI uses computer algorithms.
• Data Science involves more tools than Artificial Intelligence as it requires multiple steps to analyze data & draw conclusions from it.
• Data Science involves uncovering patterns that are not immediately obvious in data, while AI focuses on giving a data model the ability to act independently.
• Data Science uses statistical insights to construct models, while AI builds models that mimic human cognition & understanding.
• Data Science does not involve as much scientific processing as Artificial Intelligence.

The salaries of Data Scientists and Artificial Intelligence Engineers.

Data scientists are in high demand, with salaries to match. The average salary of data scientist is $116,654/year. Even entry-level positions can offer salaries of up to $93,167 annually and experienced professionals can earn as much as $142,131 per year. Companies understand the value of big data and are willing to pay for talented people who can use it to make informed decisions.

The average salary of artificial intelligence engineer in the U.S. is $164,769 per year, with a low of $90,000 and a high of $304,500. This salary is well above the national average and as career opportunities in AI increase, salaries are expected to rise further.

The Tasks Performed in Data Science and Artificial Intelligence Are Different

Data Science Job Roles

Data Analysis

Data Analysis is the practice of using data to gain insights and draw conclusions. It involves cleaning, analyzing, interpreting and communicating findings from raw data. To do so effectively, Data Analysts require expertise in programming languages like SQL, SAS, Python and R.

Data Engineer 

A Data Engineer is a professional who specializes in working with and transforming raw data into usable systems for business purposes. They also maintain these systems and datasets to ensure they are accessible and can be easily used. Additionally, they implement methods to enhance data readability and quality, develop architectures to extract and transform data, have expertise in mining, models, segmentation, and SQL, as well as work with databases.

Data Scientist 

A Data Scientist is someone who can identify business challenges, come up with solutions and make them actionable. They take on all aspects of the data science process to provide businesses with meaningful insights. This requires expertise in areas such as Data Mining, Data Warehousing, Mathematics/Statistics, and Data Visualization tools that help tell a story.

Business Analyst 

A business analyst works with stakeholders to set goals, figure out the best way to collect data, and assess current processes for better results. They define specifications and analyze requirements for further processes. They are like a bridge between the business and data analysts or data scientists as they can gather requirements and draw conclusions from data using tools such as Power BI or Tableau.

Job Roles in Artificial Intelligence

An engineer who works with Artificial Intelligence or Machine Learning.

A Machine Learning Engineer’s job is to create a machine learning and deep learning models, design algorithms based on statistical modeling, build software that works without manual intervention, and collaborate with Data Scientists during the entire data science process. They should have knowledge of programming languages such as Python and R; as well as ML frameworks like TensorFlow and Keras.

Research Scientist

The ideal candidate for this position should have a strong background in applied mathematics, computational statistics, artificial intelligence, machine learning, deep learning, graphical models, computer perception, natural language processing, and data representation. They must be proficient in programming and writing across multiple languages. Additionally, they must have a good understanding of mathematical and statistical concepts such as probability, statistics, and calculus as these are key components of AI programming.

Robotics Scientist

An engineer in robotics designs builds, and tests machines, as well as updates their software. They must also be knowledgeable in flexible automation and computer systems and be able to maximize cost-efficiency. Furthermore, they need a strong understanding of mathematics and statistics as well as algorithms to program complex robotic systems that use AI and ML techniques.

A summary

Data Science and Artificial Intelligence are often used interchangeably, but they are two distinct fields. Artificial Intelligence is a broad area with many unexplored possibilities, while Data Science uses AI techniques to produce predictions and process data for analysis and visualization.

Data Science involves analyzing data to gain meaningful insights, while Artificial Intelligence focuses on making products and systems smarter. We hope our explanation of the differences between Data Science and Artificial Intelligence was clear and helpful.