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.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis EDA 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. We will also be sharing code snippets so you can try out different analysis techniques yourself. EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization.

Exploratory Data Analysis 370

Exploratory Data Analysis EDA Data Analysis Data Analysis 370

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

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. However, there are a few fundamental principles that remain the same throughout.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

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.

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

As an example for catalogue data, it’s important to check if the set of mandatory fields like product title, primary image, nutritional values, etc. are present in the data. So, we need to build a verification layer that runs based on a set of rules to verify and validate data before preparing it for model training.

ML 59

ML ML Algorithm Machine Learning 59