Data Science Dojo

JULY 3, 2024

Data Visualization : Techniques and tools to create visual representations of data to communicate insights effectively. Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. While knowing Python, R, and SQL are expected, you’ll need to go beyond that.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job. Knowing some SQL is also essential.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

Pickl AI

APRIL 21, 2025

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

phData

NOVEMBER 4, 2024

With all this packaged into a well-governed platform, Snowflake continues to set the standard for data warehousing and beyond. Snowflake supports data sharing and collaboration across organizations without the need for complex data pipelines.

Machine Learning 69

Machine Learning Machine Learning Data Science ML 69

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

The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

Data Pipeline 59

Data Pipeline ETL Data Quality SQL 59

AWS Machine Learning Blog

MARCH 8, 2023

Sagemaker provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common ML algorithms that are optimized to run efficiently against extremely large data in a distributed environment.

ML 130

ML ML AWS Python 130

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

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

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

Database 157

Database AWS ETL SQL 157

Women in Big Data

NOVEMBER 27, 2024

Its PostgreSQL foundation ensures compatibility with most SQL clients. While it shares similarities with PostgreSQL, there are key differences that must be considered during application development. Strengths : High performance with SQL support, easy integration with other AWS services, and strong security features.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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. In this article, let’s understand an explanation of how to enhance problem-solving skills as a data engineer.

Data Engineering 40

Data Engineering Data Engineering Data Engineering Data Engineer 40

Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

The MLOps Blog

SEPTEMBER 7, 2023

These combinations of Python code and SQL play a crucial role but can be challenging to keep them robust for their entire lifetime. Directives and architectural tricks for robust data pipelines Gain insights into an extensive array of directives and architectural strategies tailored for the development of highly dependable data pipelines.

Machine Learning 52

Machine Learning Machine Learning ETL ML 52

ODSC - Open Data Science

JANUARY 7, 2025

Just as a writer needs to know core skills like sentence structure and grammar, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and soon. While knowing Python, R, and SQL is expected, youll need to go beyond that.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

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

Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Algorithm Development: Crafting algorithms to solve complex business problems and optimise processes. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52

AI AI Data Analysis Data Analysis 52

phData

JANUARY 31, 2024

Python has long been the favorite programming language of data scientists. Historically, Python was only supported via a connector, so making predictions on our energy data using an algorithm created in Python would require moving data out of our Snowflake environment.

Machine Learning 52

Machine Learning Machine Learning Python Data Scientist 52

phData

JUNE 26, 2024

This is a perfect use case for machine learning algorithms that predict metrics such as sales and product demand based on historical and environmental factors. Cleaning and preparing the data Raw data typically shouldn’t be used in machine learning models as it’ll throw off the prediction.

Machine Learning 52

Machine Learning Machine Learning Data Engineer Data Engineering 52

Pickl AI

DECEMBER 9, 2024

Hive is a data warehouse tool built on Hadoop that enables SQL-like querying to analyse large datasets. What is the Difference Between Structured and Unstructured Data? Structured data is organised in tabular formats like databases, while unstructured data, such as images or videos, lacks a predefined format.

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

ETL 52

ETL Data Warehouse Data Quality Data Governance 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. Companies can build Snowflake databases expeditiously and use them for ad-hoc analysis by making SQL queries.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 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

ODSC - Open Data Science

APRIL 26, 2023

Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Alation

APRIL 14, 2022

Data pipeline orchestration. Support for languages and SQL. Moving/integrating data in the cloud/data exploration and quality assessment. An inference algorithm that informs the analyst with a ranked set of suggestions about the transformation. Collaboration and governance. Low-code, no-code operation.

Data Governance 59

Data Governance ML ML Cloud Data 59

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

ODSC - Open Data Science

DECEMBER 9, 2024

Yet despite these rich capabilities, challenges stillarise The Fragmentation Challenge With so many modular open-source libraries and frameworks now available, effectively stitching together coherent data science application workflows poses a frequent headache for practitioners.

Data Science 40

Data Science Python Machine Learning Machine Learning 40

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

APRIL 21, 2024

MLOps helps these organizations to continuously monitor the systems for accuracy and fairness, with automated processes for model retraining and deployment as new data becomes available. You can consider this stage as the most code-intensive stage of the entire ML pipeline. It is designed to leverage hardware acceleration (e.g.,

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

MARCH 21, 2023

I have worked with customers where R and SQL were the first-class languages of their data science community. With language models and NLP , you’d likely need your data component to also cater for unstructured text and speech data and extract real-time insights and summaries from them.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

phData

OCTOBER 28, 2024

However, if the tool supposes an option where we can write our custom programming code to implement features that cannot be achieved using the drag-and-drop components, it broadens the horizon of what we can do with our data pipelines. JV_STAGING_TBL} Here is what the outline of the pipeline looks like. Contact phData Today!

Python 52

Python ETL AWS Database 52

phData

SEPTEMBER 27, 2024

Some modern CDPs are starting to incorporate these concepts, allowing for more flexible and evolving customer data models. It also requires a shift in how we query our customer data. Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52