Data Modeling, Data Models and Exploratory Data Analysis

Empower your career – Discover the 10 essential skills to excel as a data scientist in 2023

Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. To perform exploratory data analysis effectively, data scientists must have a strong understanding of math and statistics.

Data Scientist

Data Scientist Exploratory Data Analysis Data Science Data Visualization

Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science

Data Science Exploratory Data Analysis Machine Learning Machine Learning

LLMOps demystified: Why it’s crucial and best practices for 2023

Data Science Dojo

AUGUST 28, 2023

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

Exploratory Data Analysis

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning

Webinars

What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

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Introducing the Next Generation of Text AI for AI Cloud Platform

DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Explanations around data, models , and blueprints are extensive throughout the platform so you’ll always understand your results.

AI

AI AI Exploratory Data Analysis Clustering

Why Python is Essential for Data Analysis

Pickl AI

AUGUST 27, 2024

Python’s flexibility extends to its ability to handle a wide range of tasks, from quick scripting to complex data modelling. This versatility makes Python perfect for developers who want to script applications, websites, or perform data-intensive tasks.

Data Analysis

Data Analysis Data Analysis Python Data Analyst

Monitoring Your Time Series Model in Comet

Heartbeat

MARCH 21, 2023

In the context of time series, model monitoring is particularly important as time series data can be highly dynamic because change is definite over time in ways that can impact the accuracy of the model. Comet has another noteworthy feature: it allows us to conduct exploratory data analysis.

Exploratory Data Analysis

Exploratory Data Analysis EDA Machine Learning Machine Learning

Generative AI in Software Development

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. diagram Using ChatGPT to build system diagrams — Part II Generate C4 diagrams using mermaid.js

AI

AI AI Data Analysis Data Analysis

Machine Learning Model Training Mistakes: How to avoid them

Mlearning.ai

FEBRUARY 2, 2023

Common causes of data leakage include using test data in the training process, using data from future time points, and using data that is not connected to the problem at hand. Data Leakage — Not using the appropriate test set — Test set measures the generality of the model.

Machine Learning

Machine Learning Machine Learning ML ML

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

JULY 25, 2023

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality. ETL Tools: Apache NiFi, Talend, etc.

Data Engineering

Data Engineering Data Engineering Data Engineer Data Engineering

Types of Statistical Models in R for Data Scientists

Pickl AI

AUGUST 29, 2023

The process of statistical modelling involves the following steps: Problem Definition: Here, you clearly define the research question first that you want to address using statistical modeling. Data Collection: Based on the question or problem identified, you need to collect data that represents the problem that you are studying.

Data Scientist

Data Scientist Clustering Data Analysis Data Analysis

Introducing our New Book: Implementing MLOps in the Enterprise

Iguazio

DECEMBER 14, 2023

There are 6 high-level steps in every MLOps project The 6 steps are: Initial data gathering (for exploration). Exploratory data analysis (EDA) and modeling. Data and model pipeline development (data preparation, training, evaluation, and so on). Deploy according to various strategies.

ML

ML ML Data Science Data Preparation

Importance of Tableau for Data Science

Pickl AI

JUNE 12, 2023

Tableau is an interactive platform that enables users to analyse and visualise data to gain insights. A Data Scientist requires to be able to visualize quickly the data before creating the model and Tableau is helpful for that.

Tableau

Tableau Data Science Data Scientist Data Analysis

How can Data Scientists use ChatGPT for developing Machine Learning Models

Pickl AI

OCTOBER 17, 2023

We will also explore the opportunities and factors to be taken into account while using ChatGPT for Data Science. Leveraging ChatGPT for Data Science ChatGPT for Data Analysis ChatGPT is a useful tool for Data Scientists. It facilitates exploratory Data Analysis and provides quick insights.

Data Scientist

Data Scientist Machine Learning Machine Learning Data Science

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data.

Machine Learning

Machine Learning Machine Learning ML ML

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data.

Machine Learning

Machine Learning Machine Learning ML ML

Data Scientists in the Age of AI Agents and AutoML

Towards AI

JANUARY 22, 2025

I think a competitive data professional in 2025 must possess a comprehensive understanding of the entire data lifecycle without necessarily needing to be super good at coding per se. You have to understand data, how to extract value from them and how to monitor model performances.

Data Scientist

Data Scientist EDA Exploratory Data Analysis AI

Data Science Current

Empower your career – Discover the 10 essential skills to excel as a data scientist in 2023

Data Science Journey Walkthrough – From Beginner to Expert

Webinars

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LLMOps demystified: Why it’s crucial and best practices for 2023

Webinars

Introducing the Next Generation of Text AI for AI Cloud Platform

Why Python is Essential for Data Analysis

Monitoring Your Time Series Model in Comet

Generative AI in Software Development

Machine Learning Model Training Mistakes: How to avoid them

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Types of Statistical Models in R for Data Scientists

Introducing our New Book: Implementing MLOps in the Enterprise

Importance of Tableau for Data Science

How can Data Scientists use ChatGPT for developing Machine Learning Models

Capital One’s data-centric solutions to banking business challenges

Capital One’s data-centric solutions to banking business challenges

Data Scientists in the Age of AI Agents and AutoML

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