Mlearning.ai

MARCH 5, 2023

Before conducting any formal statistical analysis, it’s important to conduct exploratory data analysis (EDA) to better understand the data and identify any patterns or relationships. EDA is an approach that involves using graphical and numerical methods to summarize and visualize the data. We can use summary statistics to do this.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis EDA 52

Women in Big Data

APRIL 17, 2024

Hypothesis Testing in Action: We learned how to formulate a null hypothesis (no difference exists) and an alternative hypothesis (a difference exists) and use statistical tests to evaluate their validity. EDA involves techniques like: Identifying different types of variables (categorical, numerical).

Exploratory Data Analysis 52

Exploratory Data Analysis EDA Machine Learning Machine Learning 52

Data Science Dojo

JANUARY 22, 2023

In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization. We will also be sharing code snippets so you can try out different analysis techniques yourself. DSD got you covered!

Exploratory Data Analysis 370

Exploratory Data Analysis EDA Data Analysis Data Analysis 370

Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. It forms the basis for many statistical tests and estimators used in hypothesis testing and confidence interval estimation.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2023

Exploratory Data Analysis (EDA) EDA is a crucial step where Data Scientists visually explore and analyze the data to identify patterns, trends, and potential correlations. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Becoming Human

MAY 15, 2023

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

The MLOps Blog

MAY 31, 2023

Exploratory data analysis The purpose of having an EDA layer is to find out any obvious error or outlier in the data. are captured and compared by formulating a hypothesis test to conclude with statistical significance. In this layer, we need to set up a set of visualisations to monitor statistical parameters from the data.

ML 59

ML ML Algorithm Machine Learning 59