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

Pickl AI

NOVEMBER 4, 2024

Mathematical Foundations In addition to programming concepts, a solid grasp of basic mathematical principles is essential for success in Data Science. Mathematics is critical in Data Analysis and algorithm development, allowing you to derive meaningful insights from data.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 3, 2024

ODSC Bootcamp Primer: Data Wrangling with SQL Course January 25th @ 2PM EST This SQL coding course teaches students the basics of Structured Query Language, which is a standard programming language used for managing and manipulating data and an essential tool in AI.

Data Wrangling 52

Data Wrangling Machine Learning Machine Learning SQL 52

ODSC - Open Data Science

SEPTEMBER 1, 2023

SQL Primer Thursday, September 7th, 2023, 2 PM EST This SQL coding course teaches students the basics of Structured Query Language, which is a standard programming language used for managing and manipulating data and an essential tool in learning AI. You will learn how to design and write SQL code to solve real-world problems.

Data Wrangling 52

Data Wrangling Machine Learning Machine Learning Data Science 52

ODSC - Open Data Science

MARCH 2, 2023

Programming/coding skills are to data scientists as plumbing tools are to professional plumbers. Without the ability to utilize data, create models, visualizations, algorithms, or anything else, you’re left without a story. SQL Databases might sound scary, but honestly, they’re not all that bad. Learning is learning.

Data Science 52

Data Science SQL Deep Learning Deep Learning 52

Pickl AI

AUGUST 31, 2023

Data Profiling refers to the process of analysing and examining data for creating valuable summaries of it. The process of data profiling helps in understanding the structure, content and interrelationships of data. Identify potential foreign key relationships between tables in a relational database.

Data Profiling 52

Data Profiling ETL Data Quality Data Wrangling 52

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

ODSC - Open Data Science

SEPTEMBER 20, 2023

Register now while tickets are 50% off. Prices go up Friday! Keynotes and Talks: Neural Networks Make Stuff up. What Should We do About it?

Data Wrangling 52

Data Wrangling Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities. Businesses need to analyse data as it streams in to make timely decisions. This diversity requires flexible data processing and storage solutions.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

MAY 17, 2023

Steps to Become a Data Scientist If you want to pursue a Data Science course after 10th, you need to ensure that you are aware the steps that can help you become a Data Scientist. For instance, calculus can help with optimising ML algorithms. Using Python libraries like pandas can help you better in the process.

Data Scientist 52

Data Scientist Data Science Data Wrangling Python 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 21, 2023

The Data Science Roadmap: Navigating Your Path to Success Step 1: Learning About Programming or Software Engineering A strong foundation in programming languages like Python , R, or Java is essential. Step 2: Acquiring Statistical Proficiency A Data Scientist’s toolkit is incomplete without a solid understanding of statistics.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

JULY 18, 2023

By transitioning from computer science to data science, you can tap into a broader range of job opportunities and potentially increase your earning potential. Leveraging existing skills: Computer science provides a strong foundation in programming, algorithms, and problem-solving, which are highly valuable in data science.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

learn data science

SEPTEMBER 3, 2020

This is the data at the source step (the first step in the right hand side) before any data wrangling. This is to improve the data loading performance. you can easily switch between similar algorithms while preserving the column selection. Analytics Analytics Switch With v6.1, That’s all for the Exploratory v6.1

SQL 52

SQL Data Wrangling Data Science Analytics 52

ODSC - Open Data Science

JANUARY 29, 2024

Data Science Knowing the ins and outs of data science encompasses the ability to handle, analyze, and interpret data, which is required for training models and understanding their outputs. Knowledge in these areas enables prompt engineers to understand the mechanics of language models and how to apply them effectively.

Data Science 52

Data Science Machine Learning Machine Learning Natural Language Processing 52

Iguazio

OCTOBER 20, 2024

Capabilities include session loading, query refinement, history saving, guardrails like subject classification and a toxicity filter, connection to monitoring, the ability to iterate and retrain the model, external database connections, and more. They also had access to a database with client data and a database with product data.

AI 96

AI AI Database Data Wrangling 96