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

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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

Becoming Human

MAY 15, 2023

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.

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. It’s the test bed for experiments where a developer runs multiple experiments and tries different model architectures, try to find out appropriate loss functions, and experiments with hyperparameters of models.

ML 59

ML ML Algorithm Machine Learning 59