"Data Analysts deal with data handling, data modeling, and reporting."
"A model is a single definitive source of information about your data contains the essential fields and behaviors of the data you're storing."
"But in essence, we built our dashboard based on the data model that had tables from Power Query, relationships, and DAX formulas."
"Building ERDs is a great way to conceptualize and visualize before you've actually built your database."
"So far we've modeled in our data with Python classes."
"Every time we add another layer, we get a better model of the training data."
"Each table should represent one real-world object."
"Once you have your models and you've extracted your patterns, one of the ultimate goals is to actually control your fluid."
"We can perfectly model the characteristics... and make a forecast... without deterministically modeling each underlying independent variable."
"That's the hard bit and that's the modeling bit."
"We make our schema, which defines the structure, and then we create a model based on that schema."
"Data modeling is a key activity involving business users and data modelers."
"An extremely important point to remember is that all relations are, by definition, in first normal form."
"The snowflake schema: a result of transactional thinking in modeling."
"This is how we model many-to-many relationships in NoSQL and avoid massive duplication of data."
"They'll be able to make the highest resolution closest to real-time model of all of the drivable space in the world in better than Google."
"Data model must contain necessary data for the end solution."
"OLAP provides an intuitive data model that users who aren't necessarily trained as analysts can quickly understand."
"The agenda is going to be primarily really focused on laying a foundation for what are facts and dimensions, a dimensional model, a star schema."
"Attributes of a good data model: it should be easily understood and consumed."
"The star schema is a way of building a data model that is designed for reporting purposes."
"Everything is going to be easier and better if you have a good data model."
"We can use DBT to have those custom models so that by the time the data is inside the destination, we can run DBT on top of that data and then write custom models."
"Now immediately we now have two columns that are in our dimension customers."
"So we need to understand that a star schema model consists of one central fact table with your values and then surrounding that we have dimension tables that consist of descriptive information."
"Now the next set of information we're going to create and our next dimension table is our regional information."
"Now we have created our second dimension table."
"Now every single one of these records is unique in a sense where it represents a different order."
"Now we have a perfect star schema, which has been set up correctly."
"The third reason and a very popular reason why you would consider having a date table is if you are trying to design Dax time intelligence."
"Meaning that this calculated measure that we developed is going to work within the filter context for whatever filters are being applied through the active relationships in the data model."
"Star and snowflake schemas are some of the most common iterations of an actual dimensional model in practice."
"Data modeling is the heart of our Power BI dashboards and reports. If your data modeling is not correct, you will get entirely wrong results."
"Always remember, data model is the essential and the first step of going or creating your reports."
"Star schema has a fact table at the center, joined to all the dimension tables. It is the most popular and most used schema."
"Snowflake schema is where the dimension tables are further connected to other dimension tables. It reduces redundancy and breaks down large dimension tables."
"And building a model from data like this is a counterpart to deriving a model through first principles."
"For now, I want to leave you with the four principles he defined: 1. Model the data, the whole data, and nothing but the data"
"Dimension table is nothing but it will have a primary key and attributes."
"Whenever you have the choice between anything else and a star schema, in my personal experience, a star schema always wins. Enjoy star schemas."
"Symbolic regression is a machine learning algorithm where you basically learn to model your data set by searching the space of analytic expressions."
"So I'm able to represent and store and work with and model a book inside my program."
"How you model your data depends entirely on how you are actually accessing and using the data. No one-size-fits-all approach."
"Data-oriented programming models real-world data as immutable Java data."
"Json values can be precisely modeled using record classes."
"Model your data as a star schema."
"This is basically a simple example of data modeling."
"My goal over the next 45 minutes is to help you get a better understanding of what it means to model and partition data on Cosmos DB."
"...you need to really understand the algorithm and model very well."
"In a time series split, you want to make sure that none of the information about the future is fed into your model."
"Our model is better with GloVe than without it."
"Did you know that Visual Studio is able to display such nice data models?"
"It's a comprehensive guide to data modeling with DynamoDB."
"Every item needs to be uniquely identified by that primary key."
"If you have a more complex application, you're often gonna opt for using a composite primary key."
"We'll be working on item collections a lot when you model with Dynamo."
"So get pumped, have fun, and model some data!"
"Welcome everybody and thank you for taking the time out of your day to come see this session on data modeling with Amazon DynamoDB."
"We're going to talk about how to design a nice, clean, efficient data model with DynamoDB."
"A good model should be intuitive, insightful, and self-explanatory."
"There's three types of relationship, one-to-one, one-to-many, many-to-many."
"This data model is where you essentially define the data that's going to be shown and used within your app."
"Starting with the data model can really help understand what you need to do."
"You're able to in a single notebook do some experiments, do some modeling, and train a model that does predictions."
"ARIMA models... despite being old traditional, they have excellent performance especially on small data sets."
"Algebraic data types are insanely powerful; they're so powerful that you can create a precise data model of almost anything you can imagine."
"This is going to be the ultimate data modeling tool for Power BI and Analysis Services Tableau."
"The design phase is where we will create data models."
"Entity relationship diagrams are graphical ways of describing the relationships among the various entities."
"Entity modeling is a standard modeling methodology that analysts typically go through in order to capture your needs for the business data that that system is going to capture."
"Compression is a cool way of thinking about how we should best model our data."
"Well today, you are in luck because I'm going to show you the new AWS Amplify UI admin interface which makes it really simple to create your data models."
"There is two way of data modeling we can do it one is Task schema other one is the snowflake schema."
"This is what I am going to explain today in the demo."
"It's really easy to see that in a graph model, much more difficult if you were to try to see that in a relational model."
"We wanted to create two-person nodes as the label, and have named properties on each of them, and the love's relationship in between."
"The context window is important because the amount of data that you can put in the context of the model plays a big role in how useful that model is for your downstream applications."
"Key message: Validation is all about checking if your model succeeds on a new dataset; it protects you from the monster."
"No SQL can provide a data model that better fits the application's need and makes our life easier in the long run."
"With no SQL databases, the data can be modeled as JSON documents, graphs with nodes and edges, key value pairs, or with dynamic columns."
"Graphs are everywhere; once you start thinking in terms of how you can model data as a graph, you start to see graph problems in different places."
"We're going to be creating our data model so that we can use it to populate our user interface."
"CDS is also called a semantically rich data model because it has an annotation power."
"So that is our book type, and then the next type is category."
"Data modeling is one of my favorite topics, maybe the most favorite, and I'm very happy to be here and speak about that."
"These types of models are very powerful and flexible, and one of their most powerful features is that it can logically handle missing data at prediction time."
"We would like to model the hierarchical structure of the data."
"The data model intends to replicate real-world constraints."
"A fundamental requirement of any data model is correctly representing how the data elements within a data model interrelate and connect."
"Common relationships that you'll see in data modeling are one-to-many, many-to-many, and referenced relationships."
"Both the multi-dimensional modeling experience and the tabular modeling experience support one-to-many relationships."
"Many-to-many can be done in tabular model, however, it requires some pretty fancy Dax."
"If you have a complex data model, if you have many many-to-many relationships that you need to resolve, that's one example."
"If you need multi-dimensional only features such as actions, data mining, or write-back, translations, this is when you want to consider a multi-dimensional solution."
"If you don't have a tabular or multi-dimensional solution, start with a tabular solution."
"At the model level, we are often interested in prediction or classification accuracy."
"Event sourcing is a way to model your data that doesn't store the current state but rather a sequence of events that led us to this state."
"This is what we mean by relational databases."
"Graph is the ultimate data model, flexible, very capable."
"I'm going to pick a location to model from, and this one is going to be New York City."
"You can easily create data models, and for these data models, it automatically generates all the CRUD functions."
"Anytime you have dimension tables or lookup tables and fact tables, the actual columnar database and DAX formulas work most efficiently."
"It provides perfect fit to today's data but when the data changes, the performance deteriorates."
"For model modeling data... our dependent variable is categorical and our independent variables can be either continuous variables or categorical variables."
"A structural equation modeling framework is an extremely powerful and flexible multivariate methodology."
"In the end, we want a model that's simple, that fits our data well, makes good predictions without overfitting."
"Bayesian modeling... is to think of it in terms of building generative models that tell you how your data was generated."
"We're going to have graphs where we're going to be able to predict and create predictive models."
"Each watch list can only have one movie, so in that sense we want to generate a one to many relationship."
"Modeling from data, we will introduce you to two key concepts: parameter estimation and system identification."
"The actual data model that we require for the business object is outlined or defined in CDS views."
"The model was capable of creating such absolutely incredible vectors like this one."
"In multi-dimensional models, you can define and build many-to-many relationships directly in the data model."
"The model seems to have captured something salient about these documents through time."
"Machine learning engineers create models that learn from data and turn data into products."
"Our first model is a user model because we need to have a way to identify the users who send messages."
"One of the great strengths of data modeler is that it allows us to document and enhance our projects using several different types of models."
"If you're serious about learning this tool, this book is for you: 'Data Modeler for Database Design Mastery'."
"One of the most powerful features of data modeler is the ability to modify your models using scripts rather than the GUI interface."
"The scripting capabilities are just too useful and too powerful not to take advantage of."
"Unsupervised learning is not just about modeling data and learning representations for other tasks."
"One of the key features of IEC 61850 is the semantic data model."
"The trick is to get small conditional probability tables."
"A model can represent data and logic."
"The bag of words model is extremely powerful and is the de facto standard."
"Create a data model and relationships between various different tables."
"When building a web app, the first thing I like to do personally is model the data."
"Switching models are typically used to describe data that comes from an economic environment that seems to be shifting among different modes of operation."
"We have a way to model data within devices, a way to access the data that you're interested in."
"The logical nodes contain data objects and finally the structure of a data object is defined by the common data class."
"Our data model has no anomalies based on our functional dependencies."
"Now we are ready to create our first data model."
"SQL takes a different approach; before we insert any actual data, we have to agree on the data model that we are going to use."
"An entity relationship diagram is simply a diagram of various entities, the attributes associated with those entities, and the relationships among and between the entities."
"I can actually adjust one value and my entire model is updated."
"We have a model of blue dog for bulldozers with a 71% R squared with a random forest we wrote entirely from scratch. That's pretty cool."
"Using the ER diagram and using the cardinality constraints we can provide a more specific definition of the database."
"Keep it simple, really, just wanted to show how you know if you think of data like a spreadsheet with rows of data very easy to model that data in a database."
"We're automatically annotating the whole data model with synonyms."
"These systems are also good for irregularly sampled data and they can actually model continuous time processes pretty effectively."
"Schema builder... can help you visualize the data model in a useful way."
"By understanding the problem domain, the shape and the structure of the data model should become clear."
"You can build your own little cube inside of Power BI. It's pretty awesome."
"DOM is a way of modeling hierarchical data for instance XML or HTML in a tree structure."