Data Science Dojo

JULY 3, 2024

Here’s a list of key skills that are typically covered in a good data science bootcamp: Programming Languages : Python : Widely used for its simplicity and extensive libraries for data analysis and machine learning. R : Often used for statistical analysis and data visualization.

Data Science 150

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ODSC - Open Data Science

APRIL 3, 2023

We looked at over 25,000 job descriptions, and these are the data analytics platforms, tools, and skills that employers are looking for in 2023. Excel is the second most sought-after tool in our chart as you’ll see below as it’s still an industry standard for data management and analytics.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

SEPTEMBER 10, 2024

By integrating AI capabilities, Excel can now automate Data Analysis, generate insights, and even create visualisations with minimal human intervention. AI-powered features in Excel enable users to make data-driven decisions more efficiently, saving time and effort while uncovering valuable insights hidden within large datasets.

Power BI 52

Power BI Data Analysis Data Analysis AI 52

ODSC - Open Data Science

JANUARY 18, 2024

Like with any professional shift, it’s always good practice to take inventory of your existing data science strengths. Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. Stay on top of data engineering trends. Learn more about the cloud.

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Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. Explain the Extract, Transform, Load (ETL) process.

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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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. This delay makes Hive less suitable for real-time or interactive data analysis. Why Do We Need Hadoop Hive?

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Hadoop SQL Big Data Big Data 52

Pickl AI

NOVEMBER 4, 2024

It enables reporting and Data Analysis and provides a historical data record that can be used for decision-making. Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

DECEMBER 16, 2024

Its core components include: Lakehouse : Offers robust data storage and processing capabilities. Data Factory : Simplifies the creation of ETL pipelines to integrate data from diverse sources. Developed by Microsoft, it is designed to simplify Data Analysis for users at all levels, from beginners to advanced analysts.

Power BI 52

Power BI Analytics Analytics Machine Learning 52

Pickl AI

JULY 25, 2024

Talend A data integration platform that offers a suite of tools for data ingestion, transformation, and management. AWS Glue A fully managed ETL service that makes it easy to prepare and load data for analytics. It automates the process of data discovery, transformation, and loading.

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Apache Kafka Data Lakes Data Warehouse Data Quality 52

DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. Multimodal embeddings help combine unstructured data from various sources in data warehouses and ETL pipelines.

AI 52

AI AI Data Lakes Database 52

phData

SEPTEMBER 19, 2023

Thankfully, there are tools available to help with metadata management, such as AWS Glue, Azure Data Catalog, or Alation, that can automate much of the process. What are the Best Data Modeling Methodologies and Processes? Data lakes are meant to be flexible for new incoming data, whether structured or unstructured.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

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. Aside neptune.ai

SQL 52

SQL Database Data Scientist Python 52

The MLOps Blog

MAY 31, 2023

There can be multiple sources of data at the same time, which can be available in different forms like image, text, and tabular form. One might want to utilize an off-the-shelf ML Ops Platform to maintain different versions of data. How to set up a data processing platform? are present in the data.

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ML ML Algorithm Machine Learning 59

DagsHub

OCTOBER 23, 2024

They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

ODSC - Open Data Science

FEBRUARY 6, 2025

Data Science & AINews DeepSeek R1 Now Available on Azure AI Foundry and GitHub, Expanding AI Accessibility for Developers Microsofts Azure AI Foundry has added DeepSeek R1 to its growing portfolio of over 1,800 AI models at a time with AI shakeups. Register by Friday for 50%off!

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AI AI Azure ETL 52

Pickl AI

MARCH 10, 2025

In this blog, well explore the 5 key components of Power BI , their features, and how they can help you make data-driven decisions. Key Takeaways User-Friendly Interface: Simplifies data analysis for non-technical users. Key Features Data Import: Connects to multiple data sources like Excel, SQL Server, or cloud services.

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Power BI Business Intelligence Business Intelligence Analytics 52

Dataconomy

MARCH 7, 2025

Sales teams can forecast trends, optimize lead scoring, and enhance customer engagement all while reducing manual data analysis. From customer service chatbots to data-driven decision-making , Watson enables businesses to extract insights from large-scale datasets with precision.

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