Analytics Vidhya

JANUARY 13, 2023

Introduction Data science is a rapidly growing field that is changing the way organizations understand and make decisions based on their data. As a result, companies are increasingly looking to hire data scientists to help them make sense of their data and drive business outcomes.

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Towards AI

NOVEMBER 6, 2024

Whether you’re a data scientist aiming to deepen your expertise in NLP or a machine learning engineer interested in domain-specific model fine-tuning, this tutorial will equip you with the tools and insights you need to get started.

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Analytics Vidhya

FEBRUARY 4, 2023

Introduction As a data scientist, you have the power to revolutionize the real estate industry by developing models that can accurately predict house prices. Get ready to learn about data collection and analysis, model selection, and […] The post How to Build a Real Estate Price Prediction Model?

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Data Science Dojo

OCTOBER 2, 2023

This means that you can use natural language prompts to perform advanced data analysis tasks, generate visualizations, and train machine learning models without the need for complex coding knowledge. Zapier The  Zapier  plugin allows you to connect ChatGPT with other cloud-based applications, automating workflows and integrating data.

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Towards AI

NOVEMBER 1, 2023

Image by storyset on Freepik A good data scientist knows their data inside out. To build a good model, you have to be truly connected to the data. Last Updated on November 2, 2023 by Editorial Team Author(s): Ryan Ueda Teo Originally published on Towards AI. Why is having an effective framework so important?

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Pickl AI

DECEMBER 10, 2023

Discover the power of Python libraries for (partial) automation of Exploratory Data Analysis (EDA). These tools empower both seasoned Data Scientists and beginners to explore datasets efficiently, extracting meaningful insights without the usual time constraints. What are auto EDA libraires?

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Heartbeat

JANUARY 24, 2024

The importance of EDA in the machine learning world is well known to its users. Making visualizations is one of the finest ways for data scientists to explain data analysis to people outside the business. Exploratory data analysis can help you comprehend your data better, which can aid in future data preprocessing.

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Towards AI

APRIL 7, 2024

This article seeks to also explain fundamental topics in data science such as EDA automation, pipelines, ROC-AUC curve (how results will be evaluated), and Principal Component Analysis in a simple way. One important stage of any data analysis/science project is EDA. Exploratory Data Analysis is a pre-study.

Machine Learning 124

Machine Learning Machine Learning Exploratory Data Analysis EDA 124

Data Science Dojo

AUGUST 28, 2023

Similar to traditional Machine Learning Ops (MLOps), LLMOps necessitates a collaborative effort involving data scientists, DevOps engineers, and IT professionals. Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production.

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Smart Data Collective

MARCH 9, 2021

Mito was specifically designed with all three of our EDA desires in mind! Our philosophy is that data analysis should be as easy as Excel and Alteryx, but with the power and ownership structure of Python and Pandas. Because Mito is at its core a spreadsheet, your data is default visible and interactive. So, what is Mito?

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Towards AI

FEBRUARY 3, 2024

As a data scientist, we will explore the entire data set to understand each characteristic and identify any patterns existing if any in it. This process is called Exploratory Data Analysis(EDA). Step III: Data organization and Feature Engineering This is a crucial step to get accurate results.

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Machine Learning Machine Learning Exploratory Data Analysis Data Scientist 122

ODSC - Open Data Science

FEBRUARY 24, 2023

These tools will help make your initial data exploration process easy. ydata-profiling GitHub | Website The primary goal of ydata-profiling is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution.

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Pickl AI

FEBRUARY 27, 2023

Knowing them and adopting the right way to overcome these will help you become a proficient data scientist. 10 Mistakes That a Data Analyst May Make Failing to Define the Problem Identifying the problem area is significant. However, many data scientist fail to focus on this aspect.

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Towards AI

JUNE 24, 2023

Collaboration] How can multiple data scientists collaborate in real-time on the same dataset? Explore with SageMaker Data Wrangler [✓] [Automation] Does the existing platform helps the data scientist to quickly analyze, visualize the data and automatically detect common issues? Source: Image by the author.

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Pickl AI

NOVEMBER 4, 2024

Its robust ecosystem of libraries and frameworks tailored for Data Science, such as NumPy, Pandas, and Scikit-learn, contributes significantly to its popularity. Moreover, Python’s straightforward syntax allows Data Scientists to focus on problem-solving rather than grappling with complex code.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

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Mlearning.ai

MAY 23, 2023

Answering one of the most common questions I get asked as a Senior Data Scientist — What skills and educational background are necessary to become a data scientist? Photo by Eunice Lituañas on Unsplash To become a data scientist, a combination of technical skills and educational background is typically required.

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Pickl AI

MAY 30, 2024

It combines elements of statistics, mathematics, computer science, and domain expertise to extract meaningful patterns from large volumes of data. Role of Data Scientists in Modern Industries Data Scientists drive innovation and competitiveness across industries in today’s fast-paced digital world.

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Pickl AI

DECEMBER 3, 2024

Introduction Data preprocessing is a critical step in the Machine Learning pipeline, transforming raw data into a clean and usable format. With the explosion of data in recent years, it has become essential for data scientists and Machine Learning practitioners to understand and effectively apply preprocessing techniques.

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Towards AI

JUNE 5, 2023

.✓ The goal should be to automate all aspects, from data acquisition and processing to training, deployment, and monitoring. Collaboration✓ The system should promote collaboration between data scientists, engineers, and the operation team.✓ A significant amount of data scientist time goes into this activity of data exploration.

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Heartbeat

MARCH 21, 2023

We will carry out some EDA on our dataset, and then we will log the visualizations onto the Comet experimentation website or platform. Time Series Models Time series models are a type of statistical model that are used to analyze and make predictions about data that is collected over time. Without further ado, let’s begin.

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Dataconomy

JULY 28, 2023

Data preprocessing ensures the removal of incorrect, incomplete, and inaccurate data from datasets, leading to the creation of accurate and useful datasets for analysis ( Image Credit ) Data completeness One of the primary requirements for data preprocessing is ensuring that the dataset is complete, with minimal missing values.

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Heartbeat

FEBRUARY 13, 2023

It is designed to make it easy to track and monitor experiments and conduct exploratory data analysis (EDA) using popular Python visualization frameworks. Introducing Kangas A powerful software application for working with large amounts of multimedia data. We pay our contributors, and we don’t sell ads.

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Ocean Protocol

MAY 24, 2024

The challenge required a detailed analysis of Google Trends data, integration of additional data sources, and the application of advanced ML methods to predict market behaviors. Data scientists across various expertise levels engaged in this challenge to determine Google Trends’ impact on cryptocurrency valuations.

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Mlearning.ai

MARCH 22, 2023

Principal Component Analysis(PCA) is an essential algorithm in a data scientist's toolkit. This makes it particularly useful for analyzing large datasets with many variables, where it can be difficult to visualize and interpret the data. . BECOME a WRITER at MLearning.ai

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AWS Machine Learning Blog

JULY 31, 2023

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. We explain the metrics and show techniques to deal with data to obtain better model performance. We use the model preview functionality to perform an initial EDA.

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Ocean Protocol

SEPTEMBER 9, 2024

They will work with lap-by-lap data to assess how pit stop timing, tire selection, and stint management influence race performance. By conducting exploratory data analysis (EDA), they will identify relationships between these variables and generate insights on how strategy impacts race outcomes.

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Kaggle

JULY 29, 2020

With sports (and everything else) cancelled, this data scientist decided to take on COVID-19 | A Winner’s Interview with David Mezzetti When his hobbies went on hiatus, Kaggler David Mezzetti made fighting COVID-19 his mission. The early days of the effort were spent on EDA and exchanging ideas with other members of the community.

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Pickl AI

OCTOBER 14, 2024

Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships. The primary purpose of EDA is to explore the data without any preconceived notions or hypotheses.

Data Analysis 52

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Heartbeat

SEPTEMBER 28, 2023

To address this challenge, data scientists harness the power of machine learning to predict customer churn and develop strategies for customer retention. Continuous Experiment Tracking with Comet ML Comet ML is a versatile tool that helps data scientists optimize machine learning experiments.

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Ocean Protocol

MARCH 11, 2024

METAR, Miami International Airport (KMIA) on March 9, 2024, at 15:00 UTC In the recently concluded data challenge hosted on Desights.ai , participants used exploratory data analysis (EDA) and advanced artificial intelligence (AI) techniques to enhance aviation weather forecasting accuracy.

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Ocean Protocol

SEPTEMBER 29, 2023

Fantasy Football is a popular pastime for a large amount of the world, we gathered data around the past 6 seasons of player performance data to see what our community of data scientists could create.

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AWS Machine Learning Blog

APRIL 6, 2023

We observed during the exploratory data analysis (EDA) that as we move from micro-level sales (product level) to macro-level sales (BL level), missing values become less significant. However, the maximum length of historical sales data (maximum length of 140 months) still posed significant challenges in terms of model accuracy.

Machine Learning 103

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Heartbeat

JANUARY 30, 2023

But they need a lot of labeled training data, and the dataset could be biased. In order to accomplish this, we will perform some EDA on the Disneyland dataset, and then we will view the visualization on the Comet experimentation website or platform. In this article, we’ll learn how to link Comet with Disneyland Sentiment Analysis.

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Pickl AI

FEBRUARY 4, 2025

It simplifies the creation of complex visualisations, making it a go-to tool for Data Scientists and analysts. Seaborn integrates seamlessly with Pandas data structures, allowing users to create plots directly from DataFrame objects. DataFrame and Series Support: Directly visualise data from Pandas DataFrames and Series.

Python 52

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Ocean Protocol

NOVEMBER 21, 2023

Overview This data challenge leaped into the fascinating world of automobile reviews with the “AutoInsight Challenge.” Here data scientists could explore, analyze, and uncover the data’s myriad stories and insights directly from Doug’s scoring metrics.

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Becoming Human

MAY 15, 2023

Note : Now, Start joining Data Science communities on social media platforms. These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey.

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