Data Science and AI are the buzzwords these days. While Data Science employs AI in its processes, and AI and Data Science are interlinked, there is a world of differences between the two. Before we delve into the differences between Data Science and Artificial Intelligence, let us understand what each of them is all about.
What is Data Science?
The word suggests that Data Science is nothing more than the science behind the data. Data Science means acquiring meaningful information from a huge amount of data using various machine learning algorithms. It has brought in the fourth industrial revolution due to the tremendous increase of data and the rising necessity for companies to rely on data to generate better goods, services, and solutions. We live in a data-driven culture where data is essential for businesses and individuals alike.
The Data Science Life Cycle
The Data Science life cycle includes the following fundamental steps for any project.
1. Problem Identification
It is critical to comprehend the problem statement and ask the correct questions to the client to comprehend the data and gain useful insights from it fully. Data Science is often used to solve five broad categories of questions:
- What do you mean by "how much" or "how many"? (regression)
- Which category does the problem belong to? (classification)
- Which group does the data belong to? (clustering)
- Is this strange? (Detection of anomalies)
- Which of the two options should be chosen? (recommendation)
It would help if you also outlined the fundamental goal of your project by identifying the factors that must be forecasted.
2. Data Gathering
The next important step is to gather the required data. It can be a public dataset, private dataset, or real-time dataset. It is crucial to gather all the data required for problem-solving.
3. Data Preprocessing
This stage can also be known as the data cleaning stage. Due to the high rise in the use of social media platforms, a huge amount of data is generated. Which has opened the door for AI versus Big Data. So data is being cleaned and selected as per the problem statement.
4. Data Exploration
This data is explored for its feasibility with the problem statement.
5. Feature Selection
Some columns or data work as a special feature that helps predict the result with high accuracy.
Finally, the prediction is made for the target variable using given data.
7. Data Visualizations
At this stage, various plots are being generated to visualize the results.
How to Become Data Scientist?
Statistics, mathematics, and programming are only a few of the fundamental subjects of Data Science. As a result, to discern trends and patterns in data, a data scientist must be an expert in them. Data Science has a steep learning curve due to the high skill requirements. A data scientist must also have certain skills. Data Science involves several techniques and procedures that include data extraction, modification, visualization, and maintenance to predict future occurrences.
A good understanding of machine learning methods is also required of a Data Scientist. Artificial Intelligence is the term for these machine learning algorithms, which we shall go over in more detail later in this post. Industries need data scientists to assist them in making data-driven choices. They assist industries in evaluating their performance and making required changes to improve it. By studying client behavior, they also assist the product development team tailor items that appeal to customers. For more details regarding a good Data Science course, you can refer to the online Data Science course.
What is Artificial Intelligence?
Artificial Intelligence itself suggests that intelligence is artificially provided. We human beings have intelligence, but we need to provide artificial intelligence to have our computers or machines to be intelligent, just like human beings. Artificial Intelligence refers to the intelligence that machines possess. It is based on innate intelligence, found in both animals and humans.
Algorithms are used in Artificial Intelligence to conduct independent behaviors. These self-directed behaviors are comparable to those that have been effective in the past. Many traditional Artificial Intelligence systems, such as pathfinding algorithms like A*, were given clear goals. However, modern AI algorithms such as deep learning recognize patterns and identify the objects hidden in the data. In addition, artificial intelligence employs several software engineering techniques to find answers to existing issues.
How to Become Artificial Intelligence Engineer?
Business analytics experts are becoming data scientists, collaborating alongside traditional data scientists to create machine learning models that give insight and suggestions for future choices. Future software aficionados will gain greatly from pursuing a job in artificial intelligence in the coming years. Artificial Intelligence Engineers are in high demand in AI-centric firms, staffing it with individuals who can do a mix of data engineering, Data Science, and software development activities.
Artificial Intelligence Engineers, unlike data engineers, do not write code to create scalable data pipelines and seldom compete in Kaggle tournaments. Because they have a strong full-stack application development background and experience incorporating machine learning algorithms, any organization's top software engineers/programmers are best positioned to transform into highly successful and knowledgeable Artificial Intelligence engineers. With their mix of programming, solid math and statistics basics, and Data Science abilities enhanced by picking machine learning as their chosen elective, computer science first-year students can satisfy some needs for AI engineers. For more details regarding good AI courses, you can refer to Knowledgehut python with Data Science.
Artificial Intelligence Life Cycle
Key Differences Between Artificial Intelligence & Data Science
|Data Science||Artificial Intelligence|
|Meaning ||Pre-processing, Data analysis, visualization, and prediction are all part of the Data Science process.||It refers to the use of a predictive model to anticipate future occurrences.|
|Skills ||Statistical techniques||Machine Learning or Deep Learning algorithms|
|Purpose ||To find hidden patterns in the data||Making autonomous data models|
|Processing ||Does not require a high level of scientific processing||Requires a high level of scientific processing|
|Tools||Python ,R, SAAS, Tableau , etc||Kaffee, Scikit-learn, Tensorflow, keras, etc.|
|Average Annual salary||Around 9 Lakh INR (The year 2021) Source |
- Entry-level around 6 Lakhs INR
- Mid-Level around 12 Lakhs INR
- Senior Level around 25 Lakhs INR source
To sum up, with the points, Data Science mainly requires skills like statistics knowledge, basic machine learning algorithm knowledge, and hands-on Python and R language, while Artificial intelligence deals with machine learning and deep learning where the knowledge of neural networks and various machine learning algorithms knowledge is a must. Thus, Data Science without machine learning is useless, and AI without data is just like a body without a heart.
Top 10 Key Differences Between Data Science and AI
- Data Science includes data-processing, pattern finding, visualization of data, and prediction. On the other hand, Artificial intelligence uses a predictive model to forestall future occurrences.
- Data Science uses various statistical tools, whereas AI uses computer algorithms for implementation.
- The tools utilized in Data Science are far more extensive than those used in AI. Data Science entails several stages for evaluating data and extracting insights.
- Basim motive of Data Science is to find the hidden patterns in the data, whereas the motive of AI is to provide the data model autonomy.
- We employ statistical insights to develop models with Data Science. Instead, AI creates models that imitate human intellect and understanding.
- Compared to AI, Data Science does not require a high level of scientific processing.
- The graphical representation is used in Data Science (Data visualization), but artificial intelligence algorithms and network node representations are used in artificial intelligence.
- Data Science is concerned with data mining and manipulation, whereas artificial intelligence is concerned with robotic control.
- Job Positions in the Artificial Intelligence profession offer a diverse range of big data career possibilities in major firms with excellent compensation. The following are a handful of these roles:
- Data Analyst
- Scientist in Robotics
- Engineer in Machine Learning
- Engineer for Big Data
- Developer of software
- Developer of Business Intelligence
- Professor of Artificial Intelligence
Job Roles in Data Science include the following roles listed from the highest-paying occupations accessible in 2021 include:
- Data Analyst
- Engineer, Data
- Architect of Data
- Analyst of Data
10. Python, R, Julia, C/C++, JAVA, Scala, etc., are the core languages used for Data Science, while Prolog, LISP, Haskel Wolfram, and many more are used for Artificial Intelligence. Python is used in both AI as well as Data Science.
Frequently Asked Questions (FAQs)
1. Are Machine Learning and Data Science the same?
No, they both are not the same. Data Science is used to find hidden patterns in data, while machine learning is used to predict or classify the data. However, it is for sure that without machine learning, only to use Data Science is worthless, and for machines, learning data works the same as the heart as in the human body.
2. Which is better, Machine Learning or Data Science?
It completely depends on the application. If you want to know the patterns hidden in data, you should go with Data Science. If you want to predict or classify something from the data, then you should go with machine learning.
3. Does Machine Learning need Data Science?
Because Machine Learning and Data Science are so intertwined, a fundamental understanding of both is essential to specialize in one of the two fields. To begin with Machine Learning, however, understanding data analysis is necessary for data science. Understanding and cleaning data before creating ML algorithms necessitates learning computer languages such as R, Python, and Java. Tutorials on these programming languages and basic data analysis and Data Science ideas are included in most Machine Learning courses.