KDnuggets

MARCH 20, 2024

Are you curious about what it takes to become a professional data scientist? By following these guides, you can transform yourself into a skilled data scientist and unlock endless career opportunities. Look no further!

Exploratory Data Analysis 362

Exploratory Data Analysis Data Wrangling Clean Data Data Analysis 362

Dataconomy

MAY 16, 2023

They employ statistical and mathematical techniques to uncover patterns, trends, and relationships within the data. Data scientists possess a deep understanding of statistical modeling, data visualization, and exploratory data analysis to derive actionable insights and drive business decisions.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

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

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. When data science was sexy , notebooks weren’t a thing yet. documentation.

SQL 52

SQL Database Data Scientist Python 52

Pickl AI

JULY 18, 2023

Dealing with large datasets: With the exponential growth of data in various industries, the ability to handle and extract insights from large datasets has become crucial. Data science equips you with the tools and techniques to manage big data, perform exploratory data analysis, and extract meaningful information from complex datasets.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. 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

SEPTEMBER 12, 2024

D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52