Think of the Iron Man Movie – Tony Stark studying all the information about his suit, how fast it can fly, how strong the wind is, and what information is needed to drive the suit. He looks at all the data and figures out what is the best course. Tony Stark is like Data Science, using all the information analyzing past data and then giving recommendations as to what can be done.
Now think of Jarvis, who can make independent decisions using this data, control the suit and help Tony in course correcting in real-time. This is Artificial Intelligence helping the person without having to rely on human intervention.
In simple terms, Tony will be the brains (Data Science) and Jarvis will be an interpreter who makes all the decisions (Artificial Intelligence)
Data Science makes use of statistical tools, methods, and technology to decode a set of data. Artificial Intelligence or AI uses the same set of data to solve cognitive problems akin to human intelligence. Learning, pattern recognition, and human-like expression are some aspects of it.
What is Data Science?
Studying large data sets to extract valuable information to help small and large businesses develop important strategies is what Data Science is all about. Given its multidisciplinary nature, Data Science means interpreting data from a variety of fields like Mathematics, Statistics, Business, Computer Science, etc.
Data Scientists i.e. the professionals who perform these tasks generally collect, analyze, and interpret such data. They then predict patterns and trends before deriving insights to create a model that is good for business-related decisions.
How Data Science Works? Key Techniques
The entire functioning of Data Science can be explained in a 7-step process. It starts with a business problem.
A Data Scientist must need to understand the problem a business is facing before undertaking the task to solve it. Asking relevant questions and then researching the same is the beginning of any solution-oriented method involving Data Science. Now, the next step would involve gathering data from multiple sources – Web Servers, Logs, Databases, APIs, Online Repositories, etc.
After gathering the right amount of data, it needs refining. This is done in a two-step method: Data Cleaning, which is filtering the right data from the irrelevant ones. This includes misspellings, inconsistency, and missing values as such. The other one is Transforming or Modifying the data as per the mapping rules. Tools like Talend and Informatica help in this stage.
Next comes Exploratory Data Analysis, which is required for further data refining. This is the prior step to choosing a variable that will create a model. Therefore, this step becomes very important. From then onwards, Data Science operation enters into its core mode.
Data Modelling is the following step, where machine learning techniques like KNN, Decision Tree, Naive Bayes, etc., are applied to predict the correct model for the data set. R, Python, and SaaS are platforms that can be used to model the data. Eventually, the data scientist has to visualize the solution and present it to the client. This requires much patience and clarity in the problem-solution approach. It can be done with tools like Qlikview and Tableau.
Finally, the model is deployed and maintained. This completes the entirety of the Data Science process.
Some of the other key techniques that a Data Scientist employs are Big Data, Data Engineering, Descriptive Statistics, Neural Networks, etc.
What are the Career Paths in Data Science?
After pursuing an MBA in Data Science Management, a student can opt for multiple job designations that vary from entry-level to top-level. Here are some of the jobs available in Data Science:
- Data Analyst
- Data Engineer
- Junior Data Scientist
- Data Scientist
- Machine Learning Engineer
- Data Product Manager
- Senior Data Scientist
- Data Science Consultant
What is Artificial Intelligence?
Artificial Intelligence is typically a method that uses computer programs and online methods to perform tasks that generally require human intelligence. It is achieving an extra level of solution-fetching process, which machines couldn’t do previously since only the human brain could interpret certain things. Artificial Intelligence has changed that.
Just like Data Science, Artificial Intelligence also studies data sets, both small and large, to gather insights, interpret, and then provide a proper solution to a business problem. However, it does it at a far deeper and more intricate level. It solves cognitive problems to bring human-level intelligence to the issue.
Core AI Concepts
AI is accomplished by studying human brain patterns and analyzing the cognitive process. Some of the core concepts of AI include:
1. Natural Language Processing (NLP)
This allows computers to understand, interpret, and generate human language. ChatGPT is an example of NLP.
2. Reinforcement Learning
In this, machines try to learn human language and behavior through a trial and error method.
3. Convolutional Neural Networks (CNNs)
These neural networks are used for image recognition and processing.
4. AI Bias
A core concept in AI, it is where AI produces unfair outcomes and decisions.
5. Supervised Learning
In this method, machines learn from examples and use labeled data to teach algorithms about predictions.
6. Ethical Considerations
Data privacy, bias, and transparency become some of the crucial factors when AI gets too pervasive.
Knowledge Learning, Machine Learning, Robotics, and Machine Perception are some of the other examples of AI Core Concepts.
What are the Real-World AI Examples?
As AI spread far and wide into the world, it found its application in multiple fields. From healthcare to marketing to Education, here are a few real-life examples where AI found its calling:
1. Agriculture
Farmers and scientists are using AI to monitor crops, keep pests at bay, and predict the yields. This has become a very important use of AI in the AgTech world since it reduced the shortages and crop losses by a significant amount.
2. Retail
Retail brands use AI for personnel inventory management, targeted marketing, and even for customer chatbots. It has made the jobs of those working at a retail outlet far easier.
3. Education
AI has entered schools and classrooms to enhance personalized teaching. It also makes plagiarism detection far more effective, and teachers can use AI to predict student performance and intervene if and when necessary.
4. Security
Law enforcement agencies and cybersecurity firms can use AI for facial recognition, surveillance, and threat detection. This is an imperative use of AI since human safety and data theft is a major concern in today’s world.
5. Space Exploration
Scientists are already using AI for space navigation, satellite imaging, mission planning, and identifying new astronaut phenomena.
6. Entertainment
The Entertainment world, including video games, movies, songs, etc., have all been impacted heavily by AI. Look at the use of CGI in most movies nowadays, video gaming experiences becoming more lifelike, or songs relying on auto-tune to enhance the listening experience. AI has surrounded the Entertainment world.
What are the Career Paths in AI?
AI has opened up many doors for those looking for a job in the technology world. Here are some of the career paths some can choose from:
- AI Marketing Manager
- Data Science Managers
- Data Science Consultants
- Product Management
- AI Scientist
- Risk Analyst
- Multichannel CRM Manager
Data Science vs. Artificial Intelligence: Key Differences
While Data Science and Artificial Intelligence are two technological domains that are cut from the same cloth, there are specific differences between Data Science and Artificial Intelligence. Here’s how Data Science Vs Artificial Intelligence stack up:
- Data Science studies data sets to come up with a solution to a business-related problem. AI does the same but the problems are normally cognitive i.e. those that can or could be processed by human intelligence.
- Data Science uses statistical tools and methods to extract data after studying them, whereas AI uses complex algorithms that mimic human abilities.
- Data Science can be applied anywhere where there is a qualitative data set. The applications of AI are boundless in comparison.
- Data Science has a smaller scope in comparison to AI. Since AI’s reach is boundless, it has found wider scope and applicability than Data Science.
- The methodologies of Data Science and AI are different. While Data Science uses linear regression, logistic regression, anomaly detection, binary classification, etc., AI uses facial recognition, natural language processing, reinforcement learning, knowledge graphs, etc.
MBA in Data Science & AI: Choosing the Right MBA Program
An MBA in Data Science & Artificial Intelligence bridges the gap between non-specialists and experts in this dynamic industry. The course is designed to enable professionals with the necessary skills to become leaders in the fields of Data Science and Artificial Intelligence.
MBA ESG is one of the top colleges for an MBA in Artificial Intelligence & Data Science in India. The program is designed by dedicated industry experts keeping in mind current industry needs and future forecasts. The trifecta of scientific learning, tech knowledge, and managerial projects offered by the program makes it a specialized course required in the digital era.
As a graduate in this field, you’ll be equipped with the skills to collect, process, and analyze large datasets, develop AI-driven models, and apply these insights to optimize operations and create innovative solutions.
An MBA in Data Science equips you with the skills and knowledge to interpret data and make corporate decisions. Artificial Intelligence and Data Science may seem like technical profiles, but in our dynamic world today, their application in everyday business strategy decisions has become rather imperative and need of the hour. The benefits of studying MBA in Data Science & Artificial Intelligence Management are numerous.
From learning cutting edge technology to high demand of professionals to dynamic landscape, an MBA in Data Science and AI has tremendous potential.
Artificial Intelligence Vs Data Science Salary
Salaries in AI and Data Science aren’t much farther apart; subject to experience, education, skills, and job designations.
An AI professional can earn somewhere between INR 6-8 LPA in an entry-level job. With a few years of experience, a professional can earn between INR 12-16 LPA. Senior AI professionals earn somewhere between INR 20-30 LPA and sometimes reach up to INR 40 LPA as well.
A Data Scientist’s starting salary can be somewhere around INR 3-5 LPA. With 4-9 years of experience, they earn somewhere between INR 12-14 LPA. Senior Data Scientist and Lead Data Scientist earn INR 20-35 LPA and INR 24-43 LPA respectively.
Frequently Asked Questions
1. Is Data Science a good career?
Yes. Data Science is a good and thriving career option. With the rise of AI and various technological advancements, Data Science has become a prosperous career option. Professionals can earn up to INR 35-40 LPA with experience.
2. Is Artificial Intelligence a good career?
Yes. Artificial Intelligence is a good career option. The salary can reach up to INR 40 LPA with experience and there is a wide array of jobs in this field such as Machine Learning Engineer, Robotics Engineer, NLP Engineer, etc.
3. Which field has a Broader Scope Data Science vs AI?
In Data Science vs AI, the scope of AI is far more. AI’s reach and applicability are basically boundless.
4. How are AI and Data Science related?
Both AI and Data Science involve studying complex sets of data and then interpreting them and coming up with a model solution. While Data Science does it for any business-related problems, AI solves problems that could otherwise be solved with human intelligence.
5. Can I pursue a career in AI with a Data Science degree?
Yes. You can pursue a career in any of the AI fields after pursuing a degree in Data Science. Alternatively, you can pursue an MBA degree in Data Science and Artificial Intelligence. This way, doors to both career options will open up for you automatically.