"We've discovered is or at least highlighted is that you very very often don't need much data at all."
"Every data scientist working with deep learning needs to recognize they have an incredibly high leverage tool."
"The point of machine learning systems is that we want to know how our algorithm will perform on unseen data after training."
"Data science is a field that attempts to find patterns and draw insights from data."
"This test set is used to check how generalizable the final model is."
"The smaller the loss, the better our model is performing."
"Classification means predicting discrete classes."
"Regression is trying to predict continuous values."
"The best way to learn data science is to do data science."
"There is a huge gap...the demand for data scientists is currently huge and the supply is very low."
"What's the point of creating a model if you can't use it?"
"In general, doing data science is kind of like building houses. You're gonna need some tools, like hammer and nails, but just knowing the tools is not good enough."
"Data is the language of scientists and makes the unexplainable explainable."
"Your data science portfolio and resume is your opportunity to represent the best of yourself."
"The breadth of data science actually extends far beyond simply predictive modeling and machine learning."
"Calculus pops up in several places in data science and machine learning."
"It's hard to hire for good data scientists because you could screen for intelligence...it's hard to screen for laziness."
"Data science is great...it really helps you in your soft skills and definitely makes you grow as a person."
"Data science is such a growing field and every company now wants to use it."
"There are sometimes 30 times more job postings than there are people to fill them."
"If you're not motivated to learn... data science may not be the best career for you."
"Learning data science is a journey, not a destination."
"The best way to learn data science is by doing data science."
"Data is organized by algorithms and models... those algorithms are not created by or understood by the people they're telling stories about."
"Biological knowledge combined with computing power multiplied by data equals the ability to hack humans."
"The best way to get better at data science is to do data science."
"You're basically like a data janitor a lot, and that's a really important step because if you're generating a model that's very flawed, dirty, or has too many missing values, the model's not going to work very well."
"One thing that's a great test of a data scientist is if they don't know how to do something, they can figure out how to do it."
"I think you need to take some time to understand how these algorithms work because if you don't understand how they work, you could get burned."
"We need to have a real conversation about the racism and sexism behind electability."
"Data scientist was the number one job two years in a row."
"The fundamental role of a data scientist is to understand the product in and out better than anyone else."
"The XG Boost algorithm is one of the most popular algorithms on Kaggle when it comes to winners."
"It is Dave's belief that you do not need a PhD in statistics or machine learning to learn data science and apply it to your daily work to derive business value."
"Machine learning is all about optimizing for an objective function... we are minimizing a loss or error function."
"Data science integrates scientific techniques, algorithms, and systems to extract information and insights from organized and unstructured data."
"Classification: identifying which category an object belongs to."
"And this is just me being like a data scientist asking questions about my data just using black and white SQL queries."
"Understanding the basics is the prerequisite to innovation in data science."
"We don't need a data scientist for every way that we're going to apply a generative AI solution to a problem. The data science part may actually be a small component of the overall project investment."
"With the right data and the right model, artificial intelligence can solve many problems."
"I was an intelligence analyst, I did charts, maps, graphs, and was, uh, did data science essentially."
"Data visualization makes data-driven arguments easier to understand."
"Ensemble approaches are really powerful techniques for solving problems and generally a best practice."
"Your chance of a collision is much much much higher than right because your search space is extremely small."
"Data science is a combination of AI, machine learning, statistical mathematics, business analytics, business software development, and computer science."
"If my interviewer has data science background I'm gonna analyze user behavior and see how we can turn inactive users into active users."
"You should learn SQL because all of the interviews I got related to data science had a SQL question."
"I think the next wave of research is going to be with less data not just less human not just with less label data but also with a lot of weak supervision and where you can increase the learning rate."
"Data labeling and simulations aren't exactly super sexy but this is one of the most important and overlooked things."
"Teledoc and Livongo are both data science organizations that are coming together and using big data to provide a total healthcare ecosystem."
"Data science is a quite multi-disciplinary path."
"Data scientists are the ones that are going to take that data and they're going to create new ways of thinking about problems."
"A good data scientist needs a diverse set of skills, including a strong sense of ethics."
"The goal of supervised learning is always to build a model that generalizes to out-of-sample data."
"I found myself cleaning and then extracting data in order to then build machine learning models in things like Python."
"A data science certificate program can give you a reasonable taste of what data science is."
"Employers are pretty apathetic to seeing a data science certificate on your resume."
"Is this option right for you? If you have no background in data science and you're looking to get some taste of what it's like."
"There are plenty of online free resources that can give you a comparable understanding of data science."
"For those of you who are trying to get into data science, I would say start with statistics."
"The reason you're learning to become a data scientist is to get a job."
"Data centric approaches are becoming quite important."
"Whenever you use large language models or pre-created neural networks, you can encode a lot of biases..."
"Causal inference tries to go a step beyond predictive modeling and help stakeholders understand why something's happening."
"Data science: the sexiest job of the 21st century."
"Communicating model results or insights to stakeholders."
"Try to take it to a field that you like, try to get some data from some things that you like because you're gonna have this, you know, you're learning database modeling."
"Data science is the sexiest job of the 21st century."
"There is a way without going to school while still having a job to break into data science."
"I think data science is really poised to grow in the next decade."
"numpy is super important library, it's kind of the base for a lot of the other major data science libraries in Python"
"Data analysis yields numbers and visualizations."
"Data science: the sexiest job of the 21st century, and possibly even the 22nd."
"Data science is not just about quick fixes; it's about finding lasting fulfillment and making a difference."
"Data science skills are essential for sustainable national competitiveness."
"Being a data scientist means understanding both business needs and technology services."
"These systems can capture the true causal structure of the data."
"Once you learn these components of data science—statistics, domain expertise, data engineering, visualization, and machine learning—you can pursue roles like data analyst, engineer, or scientist, opening doors to dream companies."
"Join our internship to dive deeper into data science concepts and gain hands-on experience with projects like credit score classification and stress prediction models."
"With data science skills, you can land lucrative roles like AI engineer, data scientist, or business intelligence manager, with salaries ranging from 8 to 15 lakhs per annum."
"Cleansing and formatting data is a huge step that needs to be done properly."
"Even if many parts of the data pipeline and workflow are automated, you still need someone to translate that business problem you're trying to solve."
"Data scientists are increasingly required to understand domain knowledge and business context."
"Data scientists make on average about $117,000 per year."
"Most of your work is not going to be training a model... a lot of it is going to have to do with data collecting, data processing, data and then testing and evaluating your model."
"Data science for everyone: you do not need a PhD in statistics to learn data science."
"Clustering is perhaps the most popular technique when it comes to classification."
"It's not about the domain but more about kind of how the data looks like and so this could end up totally being its own research area."
"Started pursuing a business major, unaware of data science."
"Data science is all about dealing with different data structures. You definitely need algorithms and data structures."
"Kaggle is a data science platform; it's part of Google."
"Machine learning and data science, it is its own realm of possibilities."
"AI is artificial intelligence... Machine learning is a subset of AI... And data science is a field that attempts to find patterns and draw insights from data."
"All the samples in our data set should be independent. They should not rely on one another, they should not affect one another."
"You now know about feature extraction, data scaling, classification, and evaluation."
"We are poisoned by correlations and we don't know how to build machines that can understand causation."
"By the end of this video, you will have the blueprint to become a data scientist in 2024."
"The two most important skills we need as data scientists are maths and programming."
"We are the National Institute for data science and AI, named after Alan Turing, who is very famous for being part of the team that cracked the Enigma code."
"...the most foundational step in any kind of model building process is deciding what it actually is you want to model..."
"Notebooks are a key tool for data scientists, used to explore data through code, typically through python or R."
"These are the essential concepts every software developer or data scientist must know."
"Data science is a direct expansion of the need to comprehend the counterintuitive."
"In order to be successful as a data scientist, you need to have the basic fundamentals: coding is a tool to apply statistics and machine learning."
"What differentiates a good data scientist from a great data scientist is having a solid business understanding and product understanding."
"Getting practice on these interview questions is very important for data scientist interviews."
"Learning only API is not important, you need to understand the fundamentals, how it works underneath, then only you can become a great data scientist or anal engineer."
"...everything should be made as simple as possible to describe the data but not simpler."
"Data science is like a great field it's lucrative it's really interesting the problems are always different the projects are always different never feels like the same job so it's really hard to get bored."
"Understanding and having a base working knowledge of those tools to load data, pull it into a data frame and then use it within other libraries will be really important."
"Spatial data science treats location, distance, and spatial interactions as a core aspect of the data."
"As a data scientist, the first thing I want to do is get a description. What am I looking at?"
"Pandas is the most popular and widely used Python library for data science along with NumPy and Matplotlib."
"Splitting data into training and test sets is crucial for training the system."
"Understanding pre-processing data is key in data science."
"You need to understand the value proposition, data sources, production task, and feature engineering before diving into ML."
"Feature engineering comes into this place to extract more information from available data sources."
"Machine learning is not just about building model; it's about a lot more than that."
"Pandas, there is this data frame object that data scientists use on a day-to-day basis. It is the most widely used tool for any data scientist."
"To drain stuff you need a data set."
"The best data scientist is not someone who can accurately write SQL code, but knowing what is a high level query to ask."
"Delta architecture can serve multiple personas: data engineers, ML engineers, and data scientists."
"80% of enterprise data is unstructured... applying structure to unstructured sources is common in data science."
"All these components are really great because they make data science workflows I think much easier to productionize."
"The data science leader has to be bold but also has to align the business objectives of the data science operations with the board."
"Good enough is good enough; it's like something from science fiction, except it isn't science fiction, it's data science."
"Ideas are very important and data science is a new domain."
"I think startups today have a lot of leverage doing data science and AI as long as they have access to that data."
"What's happening guys, my name is Nicholas Chernott and in this video, we're going to take a look at the exact process that's going to allow you to learn data science ridiculously fast."
"Now the cool thing about this is this is the exact same process that I use to go from being an accountant to a data scientist at a large tech company."
"Now you're probably wondering what's the first thing I should do to kick this data science journey off? Well, the first and probably one of the best steps that you can take is to begin to learn Python."
"We're the National Institute for data science and AI, collaborating with universities, businesses, and public sector organizations to apply research to real-world problems."
"Clean data is data that's complete, correct, and relevant to the problem you're trying to solve."
"Dirty data is incomplete, incorrect, or irrelevant to the problem you're trying to solve."
"We're therefore making major investments in data science and AI approaches which really are essential to fully harness the potential of modern biology."
"Data science is the driver for how we approach and practice medicine."
"Processes on data gathering and quickly whittling down to what you want is improved with AI technology."
"I joined as a data scientist and so far I've been here about like two and a half three months and what I realized biggest things with the projects I've worked on again like I said the most complicated machine learning model isn't always like the best use of time and resources."
"SQL is really really really important in the tech world honestly like data science involves a lot of sql."
"We've done quite a lot, but now is the fun part. After all the data cleaning and manipulation, we can actually create our machine learning model."
"Data science and analytics professionals are continuing to grow in demand."
"Embark on a hands-on data science and machine learning project where we are going to find what are the drivers of Californian house prices."
"This very common in data science when we want to reduce the dimension of the data and when we want to have some sensible numbers and create this cross-section data."
"Data science needs a new engine to answer causal questions."
"Data is a window to reality and data science is a glass that enables us to look through the window."
"Being a mechanical engineer and going into data science... that's the reason. Like, this might happen. Even BMW and Daimler hire data scientists and data analysts from the same stream."
"So, I think the jobs of data scientist or data science jobs, are in trend of the market and is in high demand."
"I'm over the data science group and within this group, we build machine learning models for the overall Navy. Specifically, we work within the financial function and we get essentially customers from all over the Navy who come submit a use case."
"Artificial intelligence, machine learning, and data science... enable machines to have human-level intelligence."
"Data labeling is the process of identifying raw data and adding one or more meaningful and informative labels."
"A linear regression finds the line that best fits the data points and gives a relationship between the two variables."
"Try to learn this ML flow, okay, and try to integrate with your projects. I hope actually it will help you a lot and you can crack any kinds of data science interview if you know these kinds of envelope tools."
"A data scientist need not really know every part of ML ops, but he needs to be involved in the process with the DevOps team so that he can create code and frameworks that are easily deployable by DevOps engineers."
"In some industries where there's only one data scientist doing everything, that's possible. You have a full-stack engineer and a small organization. It is possible, though I don't support that culture."
"I believe that's where the real value of data science comes in."
"Transitioning into data science from mechanical engineering is possible, and I'm living proof."
"When you say you're a data scientist, you can do so many different things."
"You have to define the OEC such that it is causally predictive of the lifetime value of the user."
"Patient data analysis is just one tool out of many in your data science tool belt and while it can be a very useful tool, it's not the be-all end-all of data analytical methods even though it's sometimes presented as that."
"Can we build systems that can work with sparse data?"
"I was going to become a data scientist come hell or high water."
"Time series forecasting is a very common problem that you face as a data scientist where you have historic data and you want to predict into the future."
"So the important part of a big model is that it's big enough to allow you to put the data in."
"I wanted to share this exact technique that I used over three years ago to be able to go from complete beginner to becoming a full-blown data science and eventually landing a six-figure job."
"This is our jupyter notebook file."
"Brilliant is the best way to learn data science, computer science, and maths interactively."
"So now that we have our data set, our data set's loaded into ds. example objects and our llm metric, let's dive into the dspi programming model"
"So what we do is we bundle them up together, so we put the 15 images along with the Stardust data science bowl data that was curated in the Stardust tutorial."
"The overall scale of data science needs a lot of what you just kind of described in different kinds of metrics."
"99% of data science work is extracting and cleaning data, not really analyzing it."
"You can take data science and deep learning from the notebook into the field and solve some real-world problems."
"Probably one of the most useful libraries to have under your belt as a data scientist."
"Presentation is an important part of data science."
"My main role actually is to develop models is to take the data from data prep data cleaning to developing a model out of that and then I'm not that involved in a production eyes inside of it but you do have to kind of keep that in mind when you're developing the models."
"It's very beneficial to have big data processing in your toolkit."
"I'll also open source a pre-trained model that you can download and then you can just fine-tune that on your own data."
"We need to understand the data first before building any machine learning model."
"Exploratory data analysis helps us understand the distribution of data visually."
"In the next nine minutes you're gonna figure out exactly what to expect from every data science interview question that you will ever have."
"This is such a great platform to learn everything you need to know for data science interviews."
"Anything you know in your company or like people have to ask another data scientist how to do this or look up lots of documentation and so on is the type of thing that this will learn and do for you."
"Data scientists are the modern-day alchemist turning raw data into valuable insights."
"The data isn't under our control, but the quality of our model is."
"Data scientists with the right certification can earn up to $115,000 to $246,000 per annum."
"Automl allows both beginner and experienced data scientists to get started with low to no code experimentation."
"Euclidean distance, the cosine distance, and the Manhattan distance make up 99 maybe 0.999 percent of all use cases you're going to deal with."
"...if you know that you want to do something quantitative but don't know exactly how you want to apply those quantitative skills then data science is potentially a very good fit for you."
"Feature reduction becomes important."
"Your input is fixed, your input is the data, and now your job is to find an optimal system, an optimal f, that produces a desired response y."
"Discovering equations from data is very important, something Koopman can help with."
"Koopman analysis fits very nicely in the context of data science with increasing amounts of data."
"We've now fully integrated their data science portfolio into our Oracle cloud infrastructure."
"Data cleaning is a crucial step in the data science pipeline, ensuring that data is accurate, consistent, and ready for analysis."
"Exploratory data analysis is a crucial step in a data science pipeline, allowing us to gain insights into our data and make informed decisions about data preprocessing and modeling."
"Many organizations suffer from the challenges of having siloed functional roles for individuals on their data science teams."
"Managed MLflow helps data scientists manage the complete machine learning lifecycle."
"A data scientist means a lot of different things...you're also a problem solver, a statistician, and a domain expert."
"Reproducibility and the lineage that you need to be able to build."
"Python's large library system makes it the best choice for data science, machine learning, and artificial intelligence projects."