Analytics Vidhya

JUNE 27, 2020

Overview There are a plethora of data science tools out there – which one should you pick up? Here’s a list of over 20. The post 22 Widely Used Data Science and Machine Learning Tools in 2020 appeared first on Analytics Vidhya.

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Smart Data Collective

MAY 20, 2019

For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. Basic Business Intelligence Experience is a Must. Communication happens to be a critical soft skill of business intelligence. The successful analysts of today and tomorrow must have a solid foundation in business intelligence too.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

Cost-Efficiency By leveraging cost-effective storage solutions like the Hadoop Distributed File System (HDFS) or cloud-based storage, data lakes can handle large-scale data without incurring prohibitive costs. Processing: Relational databases are optimized for transactional processing and structured queries using SQL.

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Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

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Data Science Blog

MAY 15, 2023

Data Warehousing ist seit den 1980er Jahren die wichtigste Lösung für die Speicherung und Verarbeitung von Daten für Business Intelligence und Analysen. Es ist so konzipiert, dass es mit einer Vielzahl von Speichersystemen wie dem Hadoop Distributed File System (HDFS), Amazon S3 und Azure Blob Storage zusammenarbeitet.

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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. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

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Pickl AI

FEBRUARY 28, 2023

Effectively, Data Analysts use other tools like SQL, R or Python, Excel, etc., Accordingly, the main job of Data Analysts is to help businesses make data-driven decisions and improve their business performance. At length, use Hadoop, Spark, and tools like Pig and Hive to develop big data infrastructures.

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Pickl AI

AUGUST 1, 2023

Data Engineering is crucial for data-driven organizations as it lays the foundation for effective data analysis, business intelligence, machine learning, and other data-driven applications. Acquire essential skills to efficiently preprocess data before it enters the data pipeline.

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Pickl AI

NOVEMBER 15, 2023

It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. Transactional databases, containing operational data generated by day-to-day business activities, feed into the Data Warehouse for analytical processing. It often serves as a source for Data Warehouses.

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Data Lakes Data Warehouse Database ETL 52

Dataconomy

JULY 25, 2023

Some common positions include data analyst, machine learning engineer, data engineer, and business intelligence analyst. Impactful work: Data scientists are crucial in shaping business strategies, driving innovation, and solving complex problems. If you have the following, especially for you, it can be excellent!

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Pickl AI

JUNE 19, 2024

Look for opportunities in business intelligence, market research, or any role that involves data analysis and interpretation. Databases and SQL Data doesn’t exist in a vacuum. Understanding relational databases and the Structured Query Language (SQL) is paramount.

Data Science 52

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IBM Journey to AI blog

JANUARY 18, 2023

Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. Data platform architecture has an interesting history. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lakes 52

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DagsHub

AUGUST 23, 2024

The framework is designed to help organizations ensure high-quality data, particularly within the context of data warehousing and business intelligence environments. Other Apache Griffin is an open-source data quality solution for big data environments, particularly within the Hadoop and Spark ecosystems.

Data Quality 52

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Pickl AI

APRIL 2, 2024

There are three main types, each serving a distinct purpose: Descriptive Analytics (Business Intelligence): This focuses on understanding what happened. SQL (Structured Query Language): Language for managing and querying relational databases. Hadoop/Spark: Frameworks for distributed storage and processing of big data.

Data Science 52

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Pickl AI

MARCH 19, 2025

Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Python, SQL, and Apache Spark are essential for data engineering workflows. SQL Structured Query Language ( SQL ) is a fundamental skill for data engineers.

Data Engineering 52

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Pickl AI

FEBRUARY 23, 2025

Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Key Features : Scalability : Hadoop can handle petabytes of data by adding more nodes to the cluster. Use Cases : Yahoo!

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Dataconomy

MARCH 19, 2025

Predictive modeling and machine learning: Familiarity with programming languages like Python, R, and SQL. Personal attributes Curiosity, critical thinking, and strong business acumen are vital personal attributes that significantly enhance the effectiveness of data scientists.

Data Science 91

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