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. You can even connect directly to 20+ data sources to work with data within minutes.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Analytics Vidhya

JULY 8, 2023

Introduction In today’s digital era, the power of data is undeniable, and those who possess the skills to harness its potential are leading the charge in shaping the future of technology.

Data Scientist 291

Data Scientist Data Science Analytics Analytics 291

Dataconomy

AUGUST 16, 2023

” The answer: they craft predictive models that illuminate the future ( Image credit ) Data collection and cleaning : Data scientists kick off their journey by embarking on a digital excavation, unearthing raw data from the digital landscape. At the heart of the question, “What does a data scientist do?”

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

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. Big Data Processing: Apache Hadoop, Apache Spark, etc. Read more to know.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

APRIL 3, 2025

It involves handling missing values, correcting errors, removing duplicates, standardizing formats, and structuring data for analysis. Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) EDA: Calculate overall churn rate.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

JUNE 7, 2023

Analysing Netflix Movies and TV Shows One of the most enticing real-world Data Science projects Github can include the project focusing to analyse Netflix movies and TV shows. Using Netflix user data, you need to undertake Data Analysis for running workflows like EDA, Data Visualisation and interpretation.

Data Science 52

Data Science Deep Learning Deep Learning Clustering 52

Becoming Human

MAY 15, 2023

Data Analysis also helps you to prepare your data for predictive modeling, and it is also a specific field in Data Science. There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPI s (Key Performance Indicators) for making any further calls.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

SEPTEMBER 4, 2024

See More: Seaborn vs Matplotlib: A Comprehensive Comparison for Data Visualisation. Use Cases of corr() in Data Analysis The corr() method in Pandas is a valuable tool for data analysts, offering insights into how different variables relate.

Data Analysis 52

Data Analysis Data Analysis Exploratory Data Analysis Data Analyst 52

Pickl AI

SEPTEMBER 23, 2024

PCA is the go-to method when your primary goal is data compression without losing much information, especially when dealing with high-dimensional datasets. PCA is also commonly used in exploratory Data Analysis (EDA) when the aim is to detect patterns and relationships between variables before building more complex models.

Data Analysis 52

Data Analysis Data Analysis Exploratory Data Analysis EDA 52

Pickl AI

JULY 20, 2023

Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA). Extract valuable insights and patterns from the dataset using data visualization libraries like Matplotlib or Seaborn.

Analytics 40

Analytics Analytics Big Data Big Data 40