Dataconomy

AUGUST 16, 2023

Today’s question is, “What does a data scientist do.” ” Step into the realm of data science, where numbers dance like fireflies and patterns emerge from the chaos of information. In this blog post, we’re embarking on a thrilling expedition to demystify the enigmatic role of data scientists.

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

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?

Data Scientist 306

Data Scientist Analytics Analytics EDA 306

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.

Exploratory Data Analysis 52

Exploratory Data Analysis EDA Data Analysis Data Analysis 52

ODSC - Open Data Science

FEBRUARY 24, 2023

While machine learning frameworks and platforms like PyTorch, TensorFlow, and scikit-learn can perform data exploration well, it’s not their primary intent. There are also plenty of data visualization libraries available that can handle exploration like Plotly, matplotlib, D3, Apache ECharts, Bokeh, etc.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

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

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

Data Engineering Data Engineer Data Engineering Data Engineering 52

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.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

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.

Python 52

Python ML ML Exploratory Data Analysis 52

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.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

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.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

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. This report took the data set provided in the challenge, as well as external data feeds and alternative sources.

Cross Validation 59

Cross Validation Predictive Analytics Exploratory Data Analysis EDA 59

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.

Data Analysis 59

Data Analysis Data Analysis EDA Exploratory Data Analysis 59

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. Key Features Simplicity and Speed: Focused on quick and easy data visualization capabilities.

Python 52

Python Exploratory Data Analysis Data Analysis Data Analysis 52

Mlearning.ai

FEBRUARY 28, 2023

As a data scientist at Cars4U, I had to come up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing. These are common Python libraries used for data analysis and visualization.

Python 52

Python EDA Exploratory Data Analysis Data Analysis 52

Heartbeat

FEBRUARY 6, 2024

From the above EDA, it is clear that the room's temperature, light, and CO2 levels are good occupancy indicators. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis Machine Learning 52