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Data Science vs AI: Difference Between Data Science and Artificial Intelligence

Data Science vs AI: Difference Between Data Science and Artificial Intelligence

Updated on 19 February, 2024

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Table of Contents

What is Data Science?
Hierarchy of Needs in Data Science
What is Artificial Intelligence?
Data Science vs Artificial Intelligence: Difference Between Data Science and Artificial Intelligence
Salaries for Artificial Intelligence Engineers and Data Scientists
How they are Similar?
Conclusion

In today’s rapidly evolving technological landscape, Artificial Intelligence (AI) and Data Science stand out as two of the most sought-after fields. Often, people mistakenly consider them the same, but they serve distinct purposes. AI, a component within Data Science, is utilized to enhance and automate data operations. As an expert in these domains, it is crucial to differentiate between them. The conversation around “data science vs artificial intelligence” is not just academic; it has practical implications for professionals looking to specialize. AI focuses on creating systems capable of intelligent behavior, whereas Data Science involves analyzing and interpreting complex data to make informed decisions. A detailed analysis of “data science vs artificial intelligence” reveals each field’s unique value and application, offering a clear understanding for professionals aiming to navigate these exciting career paths.

What is Data Science?

Data science is the pillar of the industry in today’s world. It is something without which a business cannot perform. It is the foundation of a successful profit-earning organization. Data science helps in collecting and analyzing the data. Based on this analysis, firms take their important financial decisions, helping them to increase their sales, prevent losses and increase their profit margin.

There has been significant growth in need of the data processing to the industries after the explosion of massive data collected by them through various means of the internet like a laptop, smartphone, tablet, desktop, etc. The companies are now relying on data to make any decisions related to almost everything about the organization. These decisions are used to make better services and products, enhancement and modification, elimination and addition of different things, etc.

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Data Science has thus brought forth a massive revolution in almost all industries. Modern societies are all data-driven, and that’s why Data science has become a crucial part of the contemporary world.

There are many subfields in Data Science such as Programming, Mathematics, and Statistics. A Data scientist should be very proficient in understanding the patterns and trends of the data. One should possess this skill of understanding to become a good Data Scientist. There are many procedures and steps in Data Science which are:

Extraction of Data: The Data has to be extracted by the Data Scientist from Big Data which is the first step in the processing of data. Data extracted should be able to give insight into a specific problem which will be later used by the leadership, management or other decision making authorities in the organization.
Manipulation: A Data Scientist should be able to manipulate the data by applying specific filters. Using filters, one should be able to get the desired level of data filtration, which is going to be analyzed further for decision-making.
Visualization: The Data Scientist shall create a display of data that can be easily understood. The Data can be represented in the form of Tables, Diagrams, Charts, Graphs and many more. When the Data is visualized, then it is straightforward to understand which is the best form of anything to understand.
Maintenance: The data extracted has to be maintained for future purposes as well so that it can be used again in future decision-making to predict various things in the businesses.

Read: Career in data science and its future growth

The ways in which data science helps in the growth of a firm are as follows –

It helps to analyze the risk of the firm and protects the firm and also the customers from any future losses. It predicts if there are any fraud activities going on.
It analyzes the trends and purchasing patterns of the customers. It then gives recommendations to the customers regarding their preferred choice of product that they would like to purchase. It also gives offers and discounts on their preferred products. It also helps in strengthening relations with the customer besides increasing the sales of the company.
Banks use data science tools to analyze the creditworthiness of their customers. With the help of the past payment record, the firm decides whether to give further loans to the customer or not.
It also helps to analyze the hidden data which is not easily understandable.

Hierarchy of Needs in Data Science

As now we already know that Artificial Intelligence is a part of Data Science, Now we will discuss the six different hierarchy of needs in Data Science:

First Need: Artificial Intelligence and Deep Learning
Second Need: A/B Testing, Experimentation and Simple ML Algorithms
Third Need: Analytics, Metrics, Segments, Aggregates, Features and Training Data
Fourth Need: Cleaning, Anomaly Detection, and Prep
Fifth Need: Reliable Data Flow, Infrastructure, Data Pipelines, ETL, Structured and Unstructured Data Storage
Sixth Need: Instrumentation, Logging, Sensors, External Data and User Generated Content

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What is Artificial Intelligence?

Artificial Intelligence is a field where algorithms are used to perform automatic actions. Its models are based on the natural intelligence of humans and animals. Similar patterns of the past are recognized, and related operations are performed automatically when the patterns are repeated. Artificial Intelligence basically impersonates the human brain and performs the activities that any human can perform but in a more efficient way and is less time–consuming.

It utilizes the principles of software engineering and computational algorithms for the development of solutions to a problem. Using Artificial intelligence, people can develop automatic systems that provide cost savings and several other benefits to companies. Large organizations are heavily dependant on Artificial Intelligence, including tech giants like Facebook, Amazon, and Google.

The uses of AI are as follows –

Artificial Intelligence helps to build a system that thinks and works like a human brain.
Artificial Intelligence is used in the field of agriculture as well to study soil conditions.
It is used in automobile industries where AI is used to predict any danger or problem in a vehicle.
AI is used by online shopping sites to keep a track of the products purchased by the customer and suggest similar products.
Video gaming industry, Education and navigating industries are some of the sectors that use AI extensively.

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Data Science vs Artificial Intelligence: Difference Between Data Science and Artificial Intelligence

AI and data science are blessings to the industries. Although, at times amid AI vs data science, it is assumed that both concepts are the same, in reality, it is not so. The difference between AI and data science are as follows –

Definition: Data science is a process of collection and analysis of data. AI is a process where only future patterns and trends have to be analyzed.
Scope: Artificial Intelligence is only limited to the implementation of ML algorithms, whereas Data Science involves various underlying operations of data.
Type of Data: Artificial Intelligence contains the kind of data that are standardized in the form of vectors and embeddings but, on the other hand, Data Science will have many different kinds of data such as structured, semi-structured and unstructured type of data
Tools: The Tools used in Artificial Intelligence are Mahout, Shogun, TensorFlow, PyTorch, Kaffe, Scikit-learn and the tools that are used in Data Science are Keras, SPSS, SAS, Python, R, etc
Applications: Artificial Intelligence applications are used in many sectors like the Healthcare industry, transport industry, robotics industries, automation industries, and manufacturing industries. On the other hand, Data Science applications are used in the field of Internet Search Engines like Google, Yahoo, Bing, Marketing Field, Banking, Advertising Field and many more.
Process: In the process of Artificial Intelligence (AI), Future events are forecasted using the predictive model. But Data Science involves the process of prediction, visualization, analysis, and pre-processing of data. Thus with respect to the process, in data science vs artificial intelligence, AI involves a lot of high-level, complex processing compared to data science.
Techniques: Artificial Intelligence will use algorithms in computers to solve the problem, whereas Data Science will involve many different methods of statistics and mathematics. Thus with respect to techniques in artificial intelligence vs data science, we can say that data science involves the use of data analytics tools. Artificial Intelligence involves the use of machine learning techniques. Machine learning is a part of Artificial Intelligence. Machine learning provides the tools that are required to understand the data. Machine learning has three approaches. They have supervised machine learning, unsupervised machine learning, and reinforcement machine learning.
Purpose: The primary purpose of Artificial Intelligence is to automate the process and bring autonomy to the model of data. But the primary goal of Data Science is to find the patterns that are hidden in the data. These both have their own set of purposes and goals which are different from each other.
Different Models: In Artificial Intelligence, Models are built which are expected to be similar to the understanding and cognition of humans. In Data Science, Models are constructed to produce insights that are statistical for decision-making.
Degree of Scientific Processing: Artificial Intelligence will use a very high degree of scientific processing when compared with Data science which uses less scientific processing.
Skills Required: When talking about AI vs data science, a data scientist must have knowledge of data analytics tools like R, Excel, Python, Stata, etc. For AI, being an expert in algorithm design is necessary.

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Salaries for Artificial Intelligence Engineers and Data Scientists

In my professional journey, I’ve noticed a growing interest in the salaries for Artificial Intelligence Engineers and Data Scientists, reflecting the high demand and pivotal role these positions play in today’s technology-driven landscape. The debate around “data science vs artificial intelligence” extends beyond their applications and into compensation, where both fields offer lucrative opportunities. Artificial Intelligence Engineers who design and implement AI models often command higher salaries due to the specialized skills and expertise required in creating intelligent systems. Data Scientists, on the other hand, analyze and interpret complex data to inform strategic decisions, a role that is equally critical and well-compensated. The salary differences reflect the varying levels of specialization and the distinct impact each role has on leveraging data and AI within organizations. For professionals considering a career in either field, understanding the nuances in compensation can guide your path toward these rewarding careers.

How they are Similar?

Data Science and Artificial Intelligence (AI) are similar in their shared objective of extracting value from data to drive decision-making and innovation. Both fields intersect at the point where they leverage algorithms, computational techniques, and insights to solve complex problems. As a data scientist, I utilize AI to enhance predictive modeling and automate data analysis processes, underscoring the symbiotic relationship between these disciplines. Data Science provides the framework for analyzing data, while AI applies this analysis to automate tasks and mimic human intelligence. This convergence is pivotal in advancing technologies that rely on data-driven insights to improve efficiency, accuracy, and outcomes across various sectors. Understanding the complementary nature of Data Science and AI is crucial for professionals aiming to harness the full potential of data in solving real-world challenges.

Conclusion

In my professional experience, I have noticed the changing landscape of “data science vs AI” and how they both have distinct yet complementary effects on the market. The vast potential of Artificial Intelligence is yet to be fully explored. AI transforms product development by automating tasks and improving product functionality, introducing innovation and autonomy to operations.

Data science transforms data for visualization and analysis, playing a crucial role in informing strategic business decisions and offering substantial benefits to companies. This association between Data Science and AI is pivotal, as Data Science provides insights and AI applies them to innovate and automate, driving forward modern business strategies. Understanding the interplay of “data science vs AI” is essential for professionals navigating these dynamic fields effectively.

There are many companies based on Artificial Intelligence that offer pure AI job positions such as NLP Scientist, Machine Learning Engineer, and Deep Learning Scientist. Various operations on data are performed using the Data Science algorithms implemented in languages like Python and R. Key decisions today are taken based on the Data that is processed by Data scientists. Thus, Data science has to play a vital role in any organization.

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Frequently Asked Questions (FAQs)

1. Why do we need to keep our databases up to date?

The goal of database maintenance is to keep the database clean and well-organized in order to prevent it from becoming unusable. Simply backing up the data so that another copy is available in the case of a disaster is one of the most important parts of database management.

2. What are AI's primary objectives?

Artificial planning aids agents in determining the best course of action to take to attain their objectives. Reasoning, knowledge representation, planning, learning, natural language processing, vision, and the capacity to move and control things are all traditional AI research aims. The artificial intelligence-assisted process of building robots that can read and comprehend human languages is known as natural learning processing.

3. What role does data visualization play in the development of AI projects?

Data visualization helps us comprehend what data means by putting it in a visual context, such as maps or graphs. This makes the data easier for the human mind to comprehend, making it easier to see trends, patterns, and outliers in large data sets. Data visualization is an important assessment criterion for deep learning since the ultimate goal of artificial intelligence is to create a machine that can grasp and respond to data even better than a person. Data visualization has shown to be important in AI development since it may help both AI engineers and others concerned about AI adoption understand and explain these systems.

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These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent. Here are the key areas I would change to make hiring processes more competitive: Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids. Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal. Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks. Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t. Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts. Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback. Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis Redesigned Interview Process In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills. Read between the resume lines Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy. Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?

by upGrad

14 May'16

5.69K+

UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself

by Omkar Pradhan

09 Oct'16

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Apoorva Shankar

07 Dec'16