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.

Exploratory Data Analysis 370

Exploratory Data Analysis EDA Data Analysis Data Analysis 370

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

Pickl AI

MAY 30, 2024

Additionally, you will work closely with cross-functional teams, translating complex data insights into actionable recommendations that can significantly impact business strategies and drive overall success. Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52