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

Dataconomy

AUGUST 16, 2023

Today’s question is, “What does a data scientist do.” ” Step into the realm of data science, where numbers dance like fireflies and patterns emerge from the chaos of information. In this blog post, we’re embarking on a thrilling expedition to demystify the enigmatic role of data scientists.

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

Pickl AI

FEBRUARY 27, 2023

Knowing them and adopting the right way to overcome these will help you become a proficient data scientist. 10 Mistakes That a Data Analyst May Make Failing to Define the Problem Identifying the problem area is significant. However, many data scientist fail to focus on this aspect.

Data Analyst 52

Data Analyst Exploratory Data Analysis Data Scientist EDA 52

Pickl AI

MAY 30, 2024

It combines elements of statistics, mathematics, computer science, and domain expertise to extract meaningful patterns from large volumes of data. Role of Data Scientists in Modern Industries Data Scientists drive innovation and competitiveness across industries in today’s fast-paced digital world.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

DECEMBER 3, 2024

Introduction Data preprocessing is a critical step in the Machine Learning pipeline, transforming raw data into a clean and usable format. With the explosion of data in recent years, it has become essential for data scientists and Machine Learning practitioners to understand and effectively apply preprocessing techniques.

Python 52

Python ML ML Exploratory Data Analysis 52

Dataconomy

JULY 28, 2023

Missing data can lead to inaccurate results and biased analyses. Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. What are the best data preprocessing tools of 2023?

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

DECEMBER 16, 2024

Step 3: Data Preprocessing and Exploration Before modeling, it’s essential to preprocess and explore the data thoroughly.This step ensures that you have a clean and well-understood dataset before moving on to modeling. Cleaning Data: Address any missing values or outliers that could skew results.

AI 52

AI AI Machine Learning Machine Learning 52