Data Science vs AI: Understanding the Key Differences
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Data science and artificial intelligence are umbrella terms for analyzing and using digital data with the help of different methods and techniques. Today, organizations are collecting information from other sources online as well as from physical systems. Data or information regarding any aspect of life can be obtained.
Data science and AI has a narrow distinction where data science usually combines statistical tools and methods to generate meaningful data. Artificial intelligence allows you to take one step further and use the derived data to solve cognitive problems commonly associated with human intelligence. It involves learning, pattern recognition, and human expressions with complex algorithms.
However, in the world of technology, data science and AI have frequently emerged in a debate, sparking curiosity about their similarity and distinctions. This discussion aims to illuminate each field and its unique applications by clearly understanding its roles and responsibilities. Let’s have a look at this brief guide to understand data science vs AI.
What is Data science?
Data science integrates technologies, methods, and algorithms to optimize raw data and derive meaningful insights. It involves an understanding of both structured and unstructured data. With data science, you can leverage multiple fields through statistical analysis, machine learning, visualization, and data mining.
Data scientists and analysts study data science to identify the vast dataset’s trends, patterns, and relationships. You can get extensive knowledge of data science with CCSLA’s course on DP-100T01-A: Designing and Implementing a Data Science Solution on Azure.
The major objective is to scan raw data and translate it into actionable insights to make informed decisions. Data science combines statistics, computer science, mathematics, and domain expertise to tackle complex programming languages like R, SQL, Python, and others.
You can learn Python with the Python Foundations eLearning program of CCSLA. This helps build and manipulate data with predictive models and allows you to perform statistical analysis. Data science broadly uses predictive analytics, disease diagnosis, and personalized treatment plans based on medical records and patient data. This finds its applications all across industries and sectors.
Pros and Cons of Data Science
Some advantages of data science are:
- It allows you to extract valuable insights from data and make decision-making processes convenient.
- Increases efficiency and profitability
- Identifies patterns and trends, detects anomalies, and optimizes the operations of different industries like healthcare, finance, and retail
To understand data science vs. AI, let’s understand the cons of data science:
- It involves a large data set, collects and cleans them to derive information. It is a time-consuming process.
- There are also less skilled data scientists; thus, finding the right talent becomes challenging.
What is Artificial intelligence?
Artificial intelligence (AI) is a machine function that imitates human intelligence and allows it to carry out activities that necessitate human cognitive processes. It aims to develop devices capable of grasping context like humans, solving problems automatically, sensing the surroundings, and modifying behavior independently.
AI involves various technologies like machine learning, natural language processing (NLP), robotics, expert systems, and computer vision. You should try out CCSLA’s Exploring AI & Machine Learning for the Enterprise (TTML5500), to have a better understanding of concepts.
Robotics is another major area of AI that focuses on building intelligent machines. It can perform physical tasks like human beings. The AI-powered robots can easily navigate through their surroundings and manipulate the objects.
AI has been a revolutionary element in the 21st century. Its applications in various industries make it exclusive to humans, driving innovation and productivity across domains. AI has advanced and is available on every mobile device, including laptops. This makes AI a part of our daily life. Though AI emerges from the scope of data science, it brings new ways of doing things and makes things innovative.
Pros and Cons of AI
Some benefits of AI are:
- Automates repetitive and time-consuming tasks
- It improves efficiency and reduces human errors.
- Facilitates personalized recommendations and insights
- It can potentially transform industries like healthcare, finance, manufacturing, etc.
Some limitations of AI are as follows:
- Requires large chunks of data to train models
- There are specific ethical implications, like job displacements and privacy concerns.
- The development of AI is expensive and time-consuming.
Ultimate Comparison of Data Science vs AI
After learning about both these technologies, you may wonder which is better: data science vs. AI. To understand this, let’s have a quick comparison between them.
| Basis | Data Science | Artificial Intelligence |
|---|---|---|
| Definition | Data science is a process of data mining, data collecting, and data cleaning to derive meaningful information. Such insights are later used in the decision-making process to build something innovative. | AI is concerned with developing intelligent systems and helps implement data and knowledge of machines with the help of algorithms and deep learning techniques. |
| Scope | It has a narrow scope and focuses on analyzing extracted insights, identifying patterns, and making predictions. | AI encases a broader scope to build intelligent systems and algorithms to sense the environment and make autonomous decisions. |
| Objective | Identifies the patterns and conceals the data. | Automation of the process is initiated. |
| Components | Data science includes: * Data collection * Data cleaning * Exploratory data analysis * Modeling and machine learning * Validation and testing Visualization | AI is categorized into: * Narrow AI * General AI * Super intelligent AI |
| Tools used | Python, SAS, R, SQL, SPSS, Keras, Tableau, etc. | TensorFlow, PyTorch, OpenAI, Shogun, Scikit-Learn, Caffe, etc. |
| Graphic | Data science is used when: * Need for exploratory data analysis * Deploying predictive models * To identify patterns and trends in the data | AI can be used for: * Automating repetitive behavior * Predicting or forecasting the future with historical and present data |
| When to use | Data science is used when: * Need for exploratory data analysis * Deploying predictive models * To identify patterns and trends in the data | AI can be used for: * Automating repetitive behavior * Predicting or forecasting the future with historical and present data |
| Techniques | Data science uses data analytics techniques. | AI uses deep learning and machine learning techniques. |
| Scientific processing | Data science has a high degree of scientific processing. | AI offers a high-level and complex type of processing. |
| Job roles | Data analyst Data scientist Data engineer Business analyst | Machine learning engineer Research scientist Robotics scientist |
| Examples | Process optimization Customer trends Financial analysis | Robotics Chatbots Online gaming Voice assistants |
Applicability of Data Science
Data science and artificial intelligence both have their applications in various industries and sectors. These sectors involve finance, healthcare, marketing, and manufacturing areas. The pivotal role of data science in these fields derives better decision-making and innovative processes. Here are some extensive applications that highlight the primary distinction between data science vs AI:
Finance
Business analytics extensively uses data science to facilitate and analyze market trends, customer behavior, and sales forecasts. In the finance sector, its implementation is fruitful as it allows risk assessment, fraud detection, and algorithmic trading. It helps in developing models to predict market trends, identify fraudulent activities, and curate investment decisions through social media interactions, website traffic, and data mining.
Artificial intelligence is extensively used in algorithmic trading, risk management, and fraud detection. Trading algorithms created through AI can predict market trends and pinpoint the opportunities for traders to trade. Better AI infrastructure scrutinizes the transaction data to detect anomalies or suspicious activities. Therefore, preventing fraudulent transactions becomes easy and protects financial institutions.
Healthcare
Data science has a transformative potential to utilize predictive analytics, disease diagnosis, and personalized treatment plans in the healthcare sector. Electronic health records (EHRs), genomic data, and medical imaging data can allow you to develop a predictive model to identify high-risk patients and recommend customized treatment regimens with improved patient outcomes.
AI advances medical imaging analysis, predictive analysis, and personalized medicines in healthcare. X-rays, MRIs, and CT scans assist radiologists in detecting abnormalities and diagnosing diseases. Once AI is implemented in hospitals, its operations and patient outcomes will improve.
Marketing
Marketing data science enables target advertising, sentimental analysis, customer segmentations, demographic-based segregation, online behavior, and purchase history. By analyzing these, data scientists can tailor marketing campaigns to specific customer segments and preferences.
AI reshapes marketing strategies with better AI-driven recommendations regarding customer preferences, purchase history, online behavior, and customer segmentations.
Manufacturing
Data science in the manufacturing sector helps maintain quality checks, supply chain optimization, and predictive maintenance. The sensor data from production equipment can predict equipment failures before they happen. This minimizes the defects and optimizes supply chain logistics to reduce cost and improve efficiency.
AI in manufacturing processes optimizes the quality control and supply chain. It analyzes sensor data from production equipment to predict failure and reduce maintenance costs.
Career Path: Data Science vs AI
One of the most promising career options in the technology sector is data science. The position is in high demand and has numerous openings. To begin your career in data science, a relevant graduation degree or even a certification course like Snowflake Data Science Training is essential.
Some job outlooks in data science are data engineers, data scientists, and data analysts. The career progression of data scientists and analysts were similar in many ways but would differ in applications. The business analyst needs a strong understanding of business needs and requirements. However, data engineers are in charge of constructing data pipelines and thus have different career paths.
Though AI is a developing profession, it is swiftly gearing up its scope and network to transform the face of society. Some of the prominent job titles of AI are machine learning engineer, senior ML engineer, research scientist, and robotics scientist. If you wish to join AI, build a relevant experience and educational background so that you can grasp the concept well.
Furthermore, you can learn from certification courses like Introduction to Artificial Intelligence (AI) and Machine Learning (ML) from CCSLA. This will provide you with extensive knowledge and will give you hands-on experience on practical applications. To get started with ML engineering, you must be familiar with the ML and deep learning algorithms to build models. With experience, you will be promoted to a senior position and offered a handsome salary. Knowledge and experience with relevant tools are necessary; you can build these skills over time.
Bottom Line
While data science focuses on extracting insights from data, AI aims to build intelligent systems that perform tasks like human intelligence. Data science is an interdisciplinary field, and AI is mainly a computer science field that heavily relies on machine learning and algorithms. Deciding between data science vs AI for your career path, analyze your strengths and weaknesses in these fields. Understand where your passion and strength lie and choose the one that suits your preferences.
Furthermore, if you are experienced and are currently working in these fields yet wish to boost your career to a new height, you can improve your expertise with advanced certification courses and excel in your field.
If you wish to grow your career in any of these fields, don’t miss a chance to ride the wave of data revolution with CCSLA. Dive into the future of technology and explore the Data Analytics & Engineering Bootcamp that CCSLA offers to give you hands-on experience and provide job placement assistance. With cutting-edge curriculum and certified trainers, earn your data analytics and engineering certificate in 12 weeks.
