ODSC - Open Data Science

FEBRUARY 2, 2023

The role of a data scientist is in demand and 2023 will be no exception. To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Data Science Of course, a data scientist should know data science!

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

JUNE 27, 2023

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. Check out the Kubeflow documentation.

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Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

JANUARY 7, 2025

Heres what we noticed from analyzing this data, highlighting whats remained the same over the years, and what additions help make the modern data scientist in2025. Data Science Of course, a data scientist should know data science! Joking aside, this does infer particular skills.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. You can watch it on demand here.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

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ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

MARCH 8, 2023

With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

ML 124

ML ML AWS Python 124

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DagsHub

DECEMBER 11, 2023

DagsHub DagsHub is a centralized Github-based platform that allows Machine Learning and Data Science teams to build, manage and collaborate on their projects. In addition to versioning code, teams can also version data, models, experiments and more. It does not support the ‘dvc repro’ command to reproduce its data pipeline.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. The good news is that there are many skills that data scientists already have that are transferable to data engineering.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

DECEMBER 5, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and of course, plenty related to large language models and generative AI. You can see our photos from the event here , and be sure to follow our YouTube for virtual highlights from the conference as well.

Data Science 98

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 98

The MLOps Blog

SEPTEMBER 7, 2023

Data Scientists and ML Engineers typically write lots and lots of code. These combinations of Python code and SQL play a crucial role but can be challenging to keep them robust for their entire lifetime. By adopting these patterns, data scientists can dedicate more attention to analyzing the model’s impact and performance.

Machine Learning 52

Machine Learning Machine Learning ETL ML 52

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

O'Reilly Media

SEPTEMBER 15, 2021

When we looked at the most popular programming languages for data and AI practitioners, we didn’t see any surprises: Python was dominant (61%), followed by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). But we believe that this data shows something significant. Salaries by Programming Language.

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AI AI Azure AWS 145

ODSC - Open Data Science

AUGUST 23, 2023

That’s a problem when you’re trying to work with that data in pandas because you have to pull the dataset into the memory of your machine, which can be slow, expensive, and lead to fatal out-of-memory issues. Ponder solves this problem by translating your pandas code to SQL that can be understood by your data warehouse.

Data Warehouse 52

Data Warehouse Data Science Database SQL 52

Alation

APRIL 4, 2023

Integrating helpful metadata into user workflows gives all people, from data scientists to analysts , the context they need to use data more effectively. The Benefits and Challenges of the Modern Data Stack Why are such integrations needed? This empowers users to judge data’s quality and fitness for purpose quickly.

DataOps 52

DataOps Data Engineering Data Engineer Data Engineering 52

phData

JANUARY 31, 2024

Applying Machine Learning with Snowpark Now that we have our data from the Snowflake Marketplace, it’s time to leverage Snowpark to apply machine learning. Python has long been the favorite programming language of data scientists. What was once a SQL-based data warehousing tool is now so much more.

Machine Learning 52

Machine Learning Machine Learning Python Data Scientist 52

The MLOps Blog

JANUARY 23, 2023

When it comes to data complexity, it is for sure that in machine learning, we are dealing with much more complex data. First of all, machine learning engineers and data scientists often use data from different data vendors. Some data sets are being corrected by data entry specialists and manual inspectors.

ML 52

ML ML Data Lakes Machine Learning 52

phData

MARCH 1, 2024

Sample CSV files (download files here ) Step 1: Load Sample CSV Files Into the Internal Stage Location Open the SQL worksheet and create a stage if it doesn’t exist. From the homepage: Data > Databases > Select your database/schema and select stages. Go back to the SQL worksheet and verify if the files exist.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DataSeries

AUGUST 15, 2024

Data Engineering Career: Unleashing The True Potential of Data Problem-Solving Skills Data Engineers are required to possess strong analytical and problem-solving skills to navigate complex data challenges. Practice coding with the help of languages that are used in data engineering like Python, SQL, Scala, or Java.

Data Engineering 40

Data Engineering Data Engineer Data Engineering Data Engineering 40

ODSC - Open Data Science

APRIL 26, 2023

Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. This is why you’ll often find that there are jobs in AI specific to an industry, or desired outcome when it comes to data.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

AI 74

AI AI Machine Learning Machine Learning 74

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Snorkel AI

JANUARY 24, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications. Data can then be labeled programmatically using a data-centric AI workflow in Snorkel Flow to quickly generate high-quality training sets over complex, highly variable data.

AI 52

AI AI ML ML 52

Snorkel AI

JANUARY 24, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications. Data can then be labeled programmatically using a data-centric AI workflow in Snorkel Flow to quickly generate high-quality training sets over complex, highly variable data.

AI 52

AI AI ML ML 52

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. A DataFrame is like a query that must be evaluated to retrieve data. An action causes the DataFrame to be evaluated and sends the corresponding SQL statement to the server for execution.

Python 52

Python ML ML SQL 52

Precisely

AUGUST 15, 2024

Key Players in AI Development Enterprises increasingly rely on AI to automate and enhance their data engineering workflows, making data more ready for building, training, and deploying AI applications. This involves various professionals.

AI 64

AI AI Data Quality Data Engineering 64

Alation

MARCH 31, 2022

New BI toolsets, such as BusinessObjects and Cognos, started to emerge; these allowed ad hoc queries to be composed without the need to write SQL. (I The data-haves feared granting access to the have-not masses, and BI use still tended to lie with super users whom the IT department trusted.

Business Intelligence 52

Business Intelligence Business Intelligence Data Warehouse Data Scientist 52

phData

JULY 18, 2024

However, creating a computer vision AI requires data scientists to train models for months before they can give results, right? AI can be trained to determine even the most subtle defects in products while being available 24 hours a day, seven days a week.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Data Engineering 52

Mlearning.ai

JULY 10, 2023

In case of complex data pipelines, a combination of Materialized Views, Stored Procedures, and Scheduled Queries could be a better choice than to solely rely on Scheduled Queries by itself. To create a Scheduled Query, the initial step is to ensure your SQL is accurately entered in the Query Editor.

SQL 52

SQL Database Database Administration Data Lakes 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Data warehousing is a vital constituent of any business intelligence operation. Simplify and Win Experienced data engineers value simplicity.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Alation

JANUARY 17, 2023

A legacy data stack usually refers to the traditional relational database management system (RDBMS), which uses a structured query language (SQL) to store and process data. While an RDBMS can still be used in a modern data stack, it is not as common because it is not as well-suited for managing big data.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Heartbeat

JANUARY 5, 2024

Introduction to LangChain for Including AI from Large Language Models (LLMs) Inside Data Applications and Data Pipelines This article will provide an overview of LangChain, the problems it addresses, its use cases, and some of its limitations. Python : Great for including AI in Python-based software or data pipelines.

AI 52

AI AI Data Pipeline Deep Learning 52