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Data Science is quite different from other analytical capabilities that tend to harness the true potential of data.

Big data has become a major component in the tech world today thanks to the actionable insights and results businesses can achieve with such multi-purpose data. However, the creation of such

Data Science and Data Analysis

While data analysis emphasizes on correlative analysis to predict relationships between data sets or known variables to discover how a particular event can occur in the future, data science emphasizes on providing strategic actionable insights into the world where people don’t know what they don’t know.  

Data Science and Data Mining

Data mining is a subset of data science that refers to the process of collecting data and searching it for patterns in data. The main goal is to design algorithms that extract insights from large unstructured data sets and validate the findings by applying identified patterns to novel subsets of data. On the other hand, data science is dependent on data mining. Rather data mining can be considered the first step of data science.  

Data Science and Machine Learning

For data science, the major refining tool is

Data Science and Statistics

Statistics, an important part of data analytics, is a branch of mathematics for providing theoretical and practical support to data mining, business intelligence, and data analysis tools. While when we talk about Statistics for data science the same questions are answered using similar statistical techniques on huge unstructured data sets by using high computational resources. In a nutshell, data science neatly is woven to other subdomains like statistical learning, computational statistics, statistical computing, Bayesian statistics, and ensemble models.  

Data Science and Artificial Intelligence

Big data has become a major component in the tech world today thanks to the actionable insights and results businesses can achieve with such multi-purpose data. However, the creation of such large datasets also calls for better understanding and having the proper tools on hand to parse through them to uncover the right information. To better comprehend big data, the fields of data science and analytics have gone from largely being relegated to academia, to instead becoming integral elements of Business Intelligence and big data analytics tools. However, it can be confusing to differentiate between analytical capabilities and data science. Despite the two being interconnected, they provide different results and pursue different approaches. Here is how data science is different from the other five analytical approaches While data analysis emphasizes on correlative analysis to predict relationships between data sets or known variables to discover how a particular event can occur in the future, data science emphasizes on providing strategic actionable insights into the world where people don’t know what they don’t chúng tôi mining is a subset of data science that refers to the process of collecting data and searching it for patterns in data. The main goal is to design algorithms that extract insights from large unstructured data sets and validate the findings by applying identified patterns to novel subsets of data. On the other hand, data science is dependent on data mining. Rather data mining can be considered the first step of data chúng tôi data science, the major refining tool is machine learning which is a mixture of statistics, computer science, and mathematics. Whereas, data science is a broader discipline that materializes around machine learning concepts which include interaction with existing systems like production databases, data acquisition, and data cleaning.Statistics, an important part of data analytics, is a branch of mathematics for providing theoretical and practical support to data mining, business intelligence, and data analysis tools. While when we talk about Statistics for data science the same questions are answered using similar statistical techniques on huge unstructured data sets by using high computational resources. In a nutshell, data science neatly is woven to other subdomains like statistical learning, computational statistics, statistical computing, Bayesian statistics, and ensemble models.Artificial Intelligence spans various knowledge domains like robotics , cognitive science, natural language processing, human-computer interaction, pattern recognition, etc. However, to note, AI is a core part of data science and very well intersects with pattern recognition and the design of intelligent systems that perform various tasks.

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What Is The Difference Between Data Science And Machine Learning?

Introduction  Data Science vs Machine Learning

AspectData Science Machine Learning DefinitionA multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.A subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that allow computer systems to learn and make predictions or decisions without being explicitly programmed.ScopeBroader scope, encompassing various stages of the data lifecycle, including data collection, cleaning, analysis, visualization, and interpretation.Narrower focus on developing algorithms and models that enable machines to learn from data and make predictions or decisions.GoalExtract insights, patterns, and knowledge from data to solve complex problems and make data-driven decisions.Develop models and algorithms that enable machines to learn from data and improve performance on specific tasks automatically.TechniquesIncorporates various techniques and tools, including statistics, data mining, data visualization, machine learning, and deep learning.Primarily focused on the application of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.ApplicationsData science is applied in various domains, such as healthcare, finance, marketing, social sciences, and more.Machine learning finds applications in recommendation systems, natural language processing, computer vision, fraud detection, autonomous vehicles, and many other areas.

What is Data Science? 

Source: DevOps School

What is Machine Learning? 

Computers can now learn without being explicitly programmed, thanks to the field of study known as machine learning. Machine learning uses algorithms to process data without human intervention and become trained to make predictions. The set of instructions, the data, or the observations are the inputs for machine learning. The use of machine learning is widespread among businesses like Facebook, Google, etc. 

Data Scientist vs Machine Learning Engineer

While data scientists focus on extracting insights from data to drive business decisions, machine learning engineers are responsible for developing the algorithms and programs that enable machines to learn and improve autonomously. Understanding the distinctions between these roles is crucial for anyone considering a career in the field.

Data ScientistMachine Learning EngineerExpertiseSpecializes in transforming raw data into valuable insightsFocuses on developing algorithms and programs for machine learningSkillsProficient in data mining, machine learning, and statisticsProficient in algorithmic codingApplicationsUsed in various sectors such as e-commerce, healthcare, and moreDevelops systems like self-driving cars and personalized newsfeedsFocusAnalyzing data and deriving business insightsEnabling machines to exhibit independent behaviorRoleTransforms data into actionable intelligenceDevelops algorithms for machines to learn and improve

What are the Similarities Between Data Science and Machine Learning?

When we talk about Data Science vs Machine Learning, Data Science and Machine Learning are closely related fields with several similarities. Here are some key similarities between Data Science and Machine Learning:

1. Data-driven approach: Data Science and Machine Learning are centered around using data to gain insights and make informed decisions. They rely on analyzing and interpreting large volumes of data to extract meaningful patterns and knowledge.

2. Common goal: The ultimate goal of both Data Science and Machine Learning is to derive valuable insights and predictions from data. They aim to solve complex problems, make accurate predictions, and uncover hidden patterns or relationships in data.

3. Statistical foundation: Both fields rely on statistical techniques and methods to analyze and model data. Probability theory, hypothesis testing, regression analysis, and other statistical tools are commonly used in Data Science and Machine Learning.

4. Feature engineering: In both Data Science and Machine Learning, feature engineering plays a crucial role. It involves selecting, transforming, and creating relevant features from the raw data to improve the performance and accuracy of models. Data scientists and machine learning practitioners often spend significant time on this step.

5. Data preprocessing: Data preprocessing is essential in both Data Science and Machine Learning. It involves cleaning and transforming raw data, handling missing values, dealing with outliers, and standardizing or normalizing data. Proper data preprocessing helps to improve the quality and reliability of models.

Where is Machine Learning Used in Data Science?

In Data Science vs Machine Learning, the skills required for ML Engineer vs Data Scientist are quite similar. 

Skills Required to Become Data Scientist

Exceptional Python, R, SAS, or Scala programming skills

SQL database coding expertise

Familiarity with machine learning algorithms

Knowledge of statistics at a deep level

Skills in data cleaning, mining, and visualization

Knowledge of how to use big data tools like Hadoop.

Skills Needed for the Machine Learning Engineer

Working knowledge of machine learning algorithms

Processing natural language

Python or R programming skills are required

Understanding of probability and statistics

Understanding of data interpretation and modeling.

Source: AltexSoft

Data Science vs Machine Learning – Career Options

There are many career options available for Data Science vs Machine Learning.

Careers in Data Science

Data scientists: They create better judgments for businesses by using data to comprehend and explain the phenomena surrounding them.

Data analysts: Data analysts collect, purge, and analyze data sets to assist in resolving business issues.

Data Architect: Build systems that gather, handle, and transform unstructured data into knowledge for data scientists and business analysts.

Business intelligence analyst: To build databases and execute solutions to store and manage data, a data architect reviews and analyzes an organization’s data infrastructure.

Source: ZaranTech

Careers in Machine Learning

Machine learning engineer: Engineers specializing in machine learning conduct research, develop, and design the AI that powers machine learning and maintains or enhances AI systems.

AI engineer: Building the infrastructure for the development and implementation of AI.

Cloud engineer: Builds and maintains cloud infrastructure as a cloud engineer.

Computational linguist: Develop and design computers that address how human language functions as a computational linguist.

Human-centered AI systems designer: Design, create, and implement AI systems that can learn from and adapt to humans to enhance systems and society.

Source: LinkedIn


Data Science and Machine Learning are closely related yet distinct fields. While they share common skills and concepts, understanding the nuances between them is vital for individuals pursuing careers in these domains and organizations aiming to leverage their benefits effectively. To delve deeper into the comparison of Data Science vs Machine Learning and enhance your understanding, consider joining Analytics Vidhya’s Blackbelt Plus Program.

The program offers valuable resources such as weekly mentorship calls, enabling students to engage with experienced mentors who provide guidance on their data science journey. Moreover, participants get the opportunity to work on industry projects under the guidance of experts. The program takes a personalized approach by offering tailored recommendations based on each student’s unique needs and goals. Sign-up today to know more.

Frequently Asked Questions

Q1. What is the main difference between Data Science and Machine Learning?

A. The main difference lies in their scope and focus. Data Science is a broader field that encompasses various techniques for extracting insights from data, including but not limited to Machine Learning. On the other hand, Machine Learning is a specific subset of Data Science that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions.

Q2. Are the skills required for Data Science and Machine Learning the same?

A. While there is some overlap in the skills required, there are also distinct differences. Data Scientists need strong statistical knowledge, programming skills, data manipulation skills, and domain expertise. In addition to these skills, Machine Learning Engineers require expertise in implementing and optimizing machine learning algorithms and models.

Q3. What is the role of a Data Scientist?

A. The role of a Data Scientist involves collecting and analyzing data, extracting insights, building statistical models, developing data-driven strategies, and communicating findings to stakeholders. They use various tools and techniques, including Machine Learning, to uncover patterns and make data-driven decisions.

Q4. What is the role of a Machine Learning Engineer?

A. Machine Learning Engineers focus on developing and implementing machine learning algorithms and models. They work on tasks such as data preprocessing, feature engineering, model selection, training and tuning models, and deploying them in production systems. They collaborate with Data Scientists and Software Engineers to integrate machine learning solutions into applications.


Removing The Shackles On Ai Is The Future Of Data Science

AI is finally living up to the hype that has surrounded it for decades. While AI is not (yet) the saviour of humanity, it has progressed from concept to reality, and practical applications are improving our environment.

However, much like Clark Kent, many of AI’s astounding exploits are veiled, and its impacts can only be seen when you look past the ordinary mask. Consider BNP Paribas Cardif, a large insurance corporation with operations in more than 30 countries. Every year, the organisation handles around 20 million client calls. They can evaluate the content of calls using speech-to-text technology and natural language processing to satisfy specific business purposes such as controlling sales quality, understanding what customers are saying and what they need, getting a sentiment barometer, and more.”

Consider AES, a leading producer of renewable energy in the United States and around the world. Renewable energy necessitates far more instruments for management and monitoring than traditional energy. AES’ next-level operational effectiveness is driven by data science and AI, which provide data-driven insights that supplement the actions and decisions of performance engineers. This guarantees that uptime requirements are met and that clients receive renewable energy as promptly, efficiently, and cost-effectively as feasible. AES, like Superman, is doing its part to save the planet.

These are only a few of the many AI applications that are already in use. They stand out because, until now, the potential of AI has been constrained by three major constraints:

  Compute Power

Traditionally, organizations lacked the computing power required to fuel AI models and keep them operational. Companies have been left wondering if they should rely only on cloud environments for the resources they require, or if they should split their computing investments between cloud and on-premise resources.

  Centralized Data

Data has traditionally been collected, processed, and stored in a centralised location, sometimes referred to as a data warehouse, in order to create a single source of truth for businesses to work from.

Maintaining a single data store simplifies regulation, monitoring, and iteration. Companies now have the option of investing in on-premises or cloud computation capability, and there has been a recent push to provide flexibility in data warehousing by decentralizing data.

Data localization regulations can make aggregating data from a spread organization unfeasible. And a fast-growing array of edge use cases for data models is undermining the concept of unique data warehouses.

  Training Data

A lack of good data has been a major impediment to the spread of AI. While we are theoretically surrounded by data, gathering and keeping it may be time-consuming, laborious, and costly. There is also the matter of bias. When designing and deploying AI models, they must be balanced and free of bias to ensure that they generate valuable insights while causing no harm. However, data, like the real world, has bias. And if you want to scale your usage of models, you’ll need a lot of data.

To address these issues, businesses are turning to synthetic data. In fact, synthetic data is skyrocketing. According to Gartner, by 2024, 60% of data for AI applications would be synthetic. The nature of the data (actual or synthetic) is unimportant to data scientists. What matters is the data’s quality. Synthetic data eliminates the possibility of prejudice. It’s also simple to scale and less expensive to obtain. Businesses can also receive pre-tagged data with synthetic data, which drastically reduces the amount of time and resources required to build and generate the feedstock to develop your models.

How To Write A Resume For Data Science Role

Composing a resume for data science job applications is seldom a great task, however, it is a fundamental malevolence. Most organizations require a resume so as to apply to any of their open jobs and a resume is frequently the first layer of the procedure in moving beyond the “Gatekeeper” which is the recruiter or hiring manager. Writing your very own concise rundown experiences seems like a simple task, yet many battle with it. Here are a few hints about how to write a reasonable and concise resume that will get the attention of a recruiter/hiring manager. Employer demand for

Keep it Brief

The principal thing you should make progress toward in writing a resume is to keep it short. A decent resume should just be one page long, except if you have 15+ long years of applicable experience for the job you’re applying to. That being said, there are recruiters out there who will hurl any resume longer than one page. Recruiters/hiring managers get a LOT of resumes each day and they, as a rule, have around 30 seconds to look over somebody’s resume and settle on a choice. Along these lines, in spite of the fact that you may have many data science projects you’d prefer to highlight, you should prioritize. You need to boil your experience down to the most significant, applicable focuses so it is easy to filter.  

Do your Research

Companies care less about you needing a career in data science than they do about you needing a profession with them. Before you begin throwing together a data science resume, ensure you realize who you’re sending the resume to. All things considered, your resume won’t be fiercely extraordinary for every application you record, however, it should be to some degree unique. A custom-fitted resume isolates candidates who simply need any job from the individuals who want this job. The job description is the most significant snippet of data to remember. Your resume should exhibit that you fill the expected set of responsibilities: in experience, in abilities, in area, and so forth. So you found a position at an organization and you know nothing about it? The best spot to begin is the “About” page or the page that gives an overview of the organization, its mission, values, and so on. When you’ve wrapped up the organization site, extend your hunt. Great external resources for realizing what you should know to incorporate chúng tôi LinkedIn, and different news sources that may have published articles and public statements related to the organization.  

Choose a Template

While each resume will consistently incorporate data like past work experience, skills, contact information, and so on., you should have a resume that is one of a kind to you. That begins with the visual look of the resume and there is a wide range of approaches to achieve a special design. You can make your own resume from scratch, however, it might be simpler, to begin with, creative resume templates from free destinations, for example, Creddle, Canva, VisualCV, CVMKR, SlashCV, or even a Google Doc resume format. Remember that the kind of resume layout you pick is additionally significant. In case you’re applying to organizations with an increasingly traditional feel (the Dells, HPs, and IBMs of the world), try to focus on a progressively great, stifled style of resume. In case you’re focusing on an organization with more a startup vibe (Google, Facebook, Pinterest, and so on.), you can pick a template or make a resume with somewhat more style, maybe with certain graphics and unique coloring. In any case, keep it simple. Once more, a hiring manager may just take 30 seconds to examine this report and make a decision, so if all else fails, keep things short.  

Organize and Prioritize

To begin, settle on the data you’ll incorporate, utilizing headings and subheadings. A resume could be sorted out along these lines, for instance: Name

Phone number



Education & Certificates





Issuing Body

Dates Valid











Responsibilities Skills & Knowledge


Technical Skills

Soft Skills

Work Experience

You can name this area “Experience” or “Professional Experience.” Your latest work experience should be recorded on top, with the preceding job beneath that, etc in sequential order. How far back you go as far as experience is subject to a couple of things. Commonly you wouldn’t have any desire to go back further than five years. In any case, if you have significant work experience that goes back farther than that, you might need to incorporate that experience also. Remember that while you don’t have to list all of your experience, you need to be certain that whatever you list looks consistent. Gaps of longer than six months you would say segment are a significant warning for recruiters and hiring managers. If you have such a gap, you without a doubt need to clarify it on your resume. For instance, if you took two years off to bring up children somewhere in between 2023 and 2023, you despite everything need to include those dates on your resume and express that you were a stay-at-home parent during that period. If your work experience isn’t pertinent to the job you’re applying for, at that point you just need to incorporate an organization name, your job title, and the dates worked. You don’t have to occupy space with all the details of an irrelevant job.  

Skills and Accomplishments

Skills are a significant segment in a data science resume, as there are numerous mind boggling tools and programmatic languages that businesses expect of their candidates. A few people essentially list skills, others list skills with an evaluation of their familiarity, while others list abilities and a depiction of where and how they utilized them. Since we’re searching for curtness and on the grounds that the objective of the data scientist resume is to land an interview, not the job (yet), we’ll just rundown skills here. Additional skills may incorporate insights on SAS (and other analytical devices) or data mining and processing. Keep this segment short, as it’s even more a “tell” than a “show” segment, which is the reason it shows up toward the bottom of the resume. It’s generally significant for companies who are skimming.  

Wrap it Up

When you’re done including all the relevant content to your resume, the last significant activity is a spelling and grammar check. A tremendous warning for recruiters and hiring managers is having grammatical or spelling blunders on your resume. These can be difficult to get yourself, so have a confidant companion (or a couple) do a peer audit of your resume and give you criticism. They may get little blunders that you missed!

How To Develop A Career In Ai And Data Science

Also see: Top 15 Data Warehouse Tools

It’s safe to assume that job openings – lucrative job openings – for data scientists and artificial intelligence professional will continue to grow at a rapid clip in the year ahead. To assist those who seek such a career, this webcast will address the following:

What skills are today’s data science students/professional most eager to learn? How does this match with market needs?

What are some potential career paths for students/professionals who are developing their data science and AI skills?

How can job candidates better prepare for interviews for positions as data scientists?

Why is it important to get business and computer science students collaborating on machine learning algorithms?

To provide insight about a career in AI and data science, I’ll spokewith Ted Kwartler, VP of Trusted AI at DataRobot. Kwartler is also a Harvard Adjunct Professor, where he teaches the “Data Mining for Business Course.”



“Absolutely, Harvard said it’s the sexiest Job of the Decade. There’s an absolute shortage of data scientists, we recognize that here, and my students do as well. And you see a lot of institutions trying to fill that need, train people, and people are attracted to it because the salaries are so good, absolutely.

“I have a friend at a major tech firm through an acquisition, he’s making not a million dollars, but definitely 1% of 1% money, absolutely impressive money. What you see for people who are aspiring to it, they’re trying to get on to that career trajectory. And so they have questions about, “How do I get into that and then build a career from it?” You’re not gonna start at those types of salaries, but that’s the promise land, right?”


“You see a lot of students being very excited about the model building itself, and particularly they wanna use deep neural networks, which is used for image recognition for instance.

“So they get a lot of heavy math, lots of programming, very heavy into a model building, and that’s the exciting part, that’s how I actually got into it…I was trying to gamble on basketball, to be honest with you, and building a model and spending time doing that. And what was interesting was, when you get into industry that’s not always necessary, you can have a good enough model.

“What I say is, not necessarily big data, a ‘big enough’ data. And what I mean by there’s a disconnect is related to what’s actually driving value. So a data scientist fresh out of academia, or fresh with their certification, they may wanna spend six months building the perfect model and optimizing out to the 6th decimal point, right?

“But if I’m really just trying to do a customer propensity model to offer someone in my call center who calls into a call center some new offer, the business itself doesn’t need that. And so, there’s a disconnect between what is needed practically, and what is getting people excited which is the model building of it.

“When I was at Liberty Mutual, I sat on a Machine Learning Research Team, and it would be very typical that you would spend, let’s say, three to six months getting your data, organizing your data. Another three to six months optimizing your model, then another three months working with IT to get it across the finish line. And so, it’s very troublesome. A lot of the data scientists don’t even like doing the data part, they don’t wanna go and learn about the business and understand the use case.”


“It’s an interesting space because by now many people have seen that Venn diagram, and there’s a unicorn and it’s sitting in the middle, and that’s your data scientist.

“I myself have an MBA, and completely self-taught on it. What we talk about at DataRobot is business analysts that are capable and motivated, they become Citizen Data Scientists, we want to arm them to become data scientists. That can be done through training, that can be done through software. What’s interesting about that career path in particular is, you have time to learn the business, understand the narrative behind putting a model into place, because you’ve started let’s say at that junior-level.

“Another kind of differing career path would be like a heavy mathematician, so this is someone with PhD astrophysics, something like that. They can absolutely make excellent models, the challenge for them is how do they scale in the business side, right?

“Another one would be a software engineer who wants to learn modeling. There’s a growing need for what we call machine-learning engineers, people that can take the code and already know the computer science enough to get it across the finish line, get it into production.

“So those are kind of three that I think are very important career paths, so you have varying ways to get there. And then obviously maybe I’m biased, I’m likely biased, because I have an MBA I think there’s a real need to be data fluent managers, people who…understand how to work with these technical individuals.”

“And you sit down with executives, you start talking about data fluency and you talk about the geospatial data that these cars are giving off, and they connect through APIs, and they’re using deep neural net image recognition. And the executive looks at you and says, ‘What’s an API?’ Then it’s like, ‘Oh! We have to back way up here, right?’ When we can’t even get to the strategy we need fundamental understandings of some of the technology. And so I think there’s definitely an opportunity for managers to up-scale and ensure that their business…

“Let’s think about it this way, if you have 10 data scientists and you’re paying them $100,000 as burden cost, you’re out a million dollars, you wanna ensure that they’re generating value for the business.


“So I think I would say, first and foremost, think about what stage you’re at. Think about what the audience is looking for. That is just an example of the recruiter.

“The next stage that I have observed, is very often you have a series of interviews with people who are doing the job. These types of people are gonna be the ones that want the technical depth.

“It’s a balancing act, because they want to know that you can do the job. That you can bring something to the table that they can learn from. But generally, because people in this profession candidly are fairly arrogant sometimes, you also don’t want to outshine them, right?

“Here at DataRobot I would say data science is a team sport. And what I mean by that is you’re constantly interacting, and asking for best practices. This field is so diverse, it’s such a large umbrella, no one is gonna be expected to know every single nuanced way to approach a problem. And so very often you need to interact with people, asking them their best practices technically. Let alone if you’re trying to then get your model into production, right? So you still need these interpersonal communication skills, and to have some qualitative aspects. And so I think that’s very often… Again, going back to the first question where people index on, “I can build the best model possible.”


“You’ll see a lot of different job titles that if you read the descriptions end up being data-heavy, and put you on that path to data science. So cast a very wide net, don’t limit your options. And in terms of a college degree we are at the point where a college degree definitely helps open doors.

“But you see a lot of success coming out of people that have DataCamp certifications or bootcamp certifications, because the demand is there. So…

“What I tell students who are interested in building these careers, find a passion project. Because what happens is, you’ll be motivated at night to come home, and learn how to web scrape basketball statistics, learn how to download stock data, whatever your passion project is.

“You hit a roadblock, you yourself are going to be empowered and motivated to overcome that roadblock. You’ll spend time on the forums, you’ll spend time on Stack Overflow. And so there’s learning that happens just by doing. And so I don’t know that a college degree in itself really is necessary.

“I may get it wrong, but one of the boot camps maybe General Assembly or something like that, actually helps them with a career fair at the conclusion of an eight-week program. Helps open those doors, helps them get the internship. And you think about the cost of education, even at a place like Harvard, right? Obviously, the graduates from an institution with that brand, they get placed, right? So maybe, depending on just getting your foot in the door, these other certifications can help you.”

11 Superb Data Science Videos Every Data Scientist Must Watch


Presenting 11 data science videos that will enhance and expand your current skillset

We have categorized these videos into three fields – Natural Language Processing (NLP), Generative Models, and Reinforcement Learning

Learn how the concepts in these videos work and build your own data science project!


I love learning and understanding data science concepts through videos. I simply do not have the time to pour through books and pages of text to understand different ideas and topics. Instead, I get a much better overview of concepts via videos and then pick and choose the topics I want to learn more about.

The sheer quality and diversity of topics available on platforms like YouTube never ceases to amaze. I recently learned about the amazing XLNet framework for NLP from a video (which I have mentioned below for your consumption). This helped me grasp the concept so I could explore more about XLNet!

I strongly believe structure is very necessary when we’re learning any concept or topic. I follow that approach each time I write an article as well. That’s why I’ve categorized these videos into their respective domains, primarily Natural Language Processing (NLP), Generative Models and Reinforcement Learning.

So are you ready to dive in and explore the length and breadth of data science through these fascinating videos?

Without any further ado, here are 11 awesome Data Science Videos:

Natural Language Processing (NLP)

XLNet explained

How does Google Duplex work?

Google’s POEMPORTRAITS: Combining Art and AI

Generative Models

Dive into Variational Autoencoders!

Create Facial Animation from Audio

MuseNet Learned to Compose Mozart, Bon Jovi, and More

Reinforcement Learning

Teaching the Computer to drive

Learn how AlphaGo Zero works

Google DeepMind AI learns to walk

AI learns to play 2048


Adobe develops AI to detect Photoshopped Faces

XLNet Explained

XLNet is the hottest framework in NLP right now. You simply must be aware of what it is and how it works if you want to carve out a career in this field. I came across this video recently and wanted to share it with the community as soon as possible.

XLNet is the latest state-of-the-art NLP framework. It has outperformed Google’s BERT on 20 NLP tasks and achieved state-of-the-art results on 18 of them. That is very, very impressive.

Make sure you check out our article covering XLNet and it’s powerful ability here.

The below video provides a clear explanation of the original XLNet research paper. Note: You might need to know a few NLP concepts beforehand to truly grasp the inner workings of XLNet.

How does Google Duplex work?

Remember when Sundar Pichai went on stage and sent the whole world into a frenzy when he unveiled Google Duplex in his keynote at Google I/O 2023? I remember listening in complete awe to the super-realistic calls that the AI made.

It took a bit of time for the data science and NLP community to come up with an explanation as to how Google Duplex actually works. It’s pretty powerful and has the potential to change how we interact with machines.

So the million dollar question – did Google Duplex pass the Turing Test!? You decide after watching this video:

Google’s POEMPORTRAITS: Combining Art and AI

I am an artist and the prospect of combining any art form with Artificial Intelligence is extremely enticing. In a world where there is so much fear around AI, such applications are more than welcome.

Google’s POEMPORTRAITS AI has been trained on nineteenth-century poetry using NLP techniques. You can contribute and donate a word to generate your own POEMPORTRAIT. Check out how this awesome concept works:

Generative Models Dive into Variational Autoencoders!

Here’s one of our favorite reinforcement learning experts Xander Streenbrugge from his wonderful ArxivInsights channel.

Variational Autoencoders (VAEs) are powerful generative models with diverse applications. You can generate human faces or synthesize your own music or use VAEs for removing noise from images.

I like this video a lot. Xander begins with an introduction to basic autoencoders and then goes into VAEs and disentangled beta-VAEs. Quite technical, but explained beautifully and concisely, in typical Xander style.

Xander is coming back to DataHack Summit this year so you can hear from him and meet him in person!

Create Facial Animation from Audio

I was immediately drawn to the video when I read the title. This is Generative Models at their best! You can not only generate facial animation from audio but also generate different emotions for the same audio. And the facial expressions look incredibly natural.

If you aren’t following Two Minute Papers, you’re missing out. They regularly churn out videos breaking down the latest developments in easy-to-understand fashion. It’s a gem of a channel.

MuseNet Learned to Compose Mozart, Bon Jovi, and More

OpenAI’s MuseNet is a deep neural network that generates musical compositions with different instruments and combines different styles. It uses the same general-purpose unsupervised technology as GPT-2 and the results are amazing.

Never heard of GPT-2? It’s an NLP framework on par with XLNet. Check out how MustNet works here:

Reinforcement Learning Teaching a Computer to Drive

Self-driving cars have always fascinated me. The sheer scale of an autonomous vehicle’s project is staggering. There are so many components, both on the hardware side as well as the data science side, that need to align for this project to work.

This is a perfect video for beginners to learn about Genetic Programming and Reinforcement Learning and how they are used to create powerful applications. Simon’s personality kept me hooked until the very end.

And I am definitely trying the project on my own.

Learn how Google DeepMind’s AlphaGo Zero works

Another great video by Xander. He explains Google DeepMind’s popular paper on AlphaGo Zero.

AlphaGo Zero is a new version of the original AlphaGo program that beat human champion Lee Sedol comprehensively. I recommend reading our article on Monte Carlo Tree Search, the algorithm behind AlphaGo before proceeding to learn about AlphaGo Zero.

AlphaGo Zero uses Reinforcement Learning to beat the world’s leading Go players without using data from human games.

“AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.”

Source: Wikipedia

Google DeepMind’s AI learns to Walk

This video is both hilarious and informative. Exactly the type of video I like when I’m learning new things! It was funny to watch the AI learn to walk. But at the same time, it left me marveling at the power of Reinforcement Learning.

The video discusses 3 papers to try and explain how the AI learned to walk and it is surprisingly simple to understand.

AI learns to play 2048

Have you ever played the 2048 game? It is super addictive once you get the hang of it. I used to easily finish games earlier but not anymore. Being a data science enthusiast, I am going to train my computer to play it with the help of this awesome video.

This is another example of the use of Genetic Programming and Evolutionary Algorithms.

BONUS: Adobe develops AI to detect Photoshopped Faces

Adobe is a market leader in image and video manipulation software. Other companies have tried, but not many have even gotten close to Adobe’s level.

Last month, Adobe announced its research efforts to detect manipulated images. High time someone did that! It will soon be impossible to tell real from fake given how quickly GANs have taken over the world.

Imagine Donald Trump challenging Kim Jong Un to a nuclear war and then claiming that it was a deepfake and shrugging off all responsibility! We need to avoid those situations turning into reality. This video shows how Adobe’s algorithm works and tried to combat fake images:

End Notes

I love knowing about the latest research in data science, machine learning and AI. But I find it hard to read papers. It takes a lot of time and effort – something not every data science professional has. I am sure many of you struggle with the same. Consuming videos is the ideal way to get an overview of these concepts.

You can then pick and choose where your interests lie and try to spin up a project or blog post on it. Trust me, it’s a wonderful way to learn and ingrain new data science concepts.


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