IBM Journey to AI blog

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

IBM Journey to AI blog

DECEMBER 7, 2023

Here’s an overview of the key characteristics: AI-powered analytics : Integration of AI and machine learning capabilities into OLAP engines will enable real-time insights, predictive analytics and anomaly detection, providing businesses with actionable insights to drive informed decisions.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

Tableau

NOVEMBER 24, 2019

Tableau connects to Amazon Redshift, Amazon RDS, Amazon Athena, and Amazon EMR—with additional connectivity and offerings announced at AWS re:Invent 2021: Amazon S3 Connector: Leveraging Tableau’s Hyper in-memory data engine technology, Tableau has the ability to read Parquet or CSV files in place—without extract creation.

Tableau 52

Tableau AWS Analytics Analytics 52

Pickl AI

OCTOBER 10, 2024

Inconsistent or unstructured data can lead to faulty insights, so transformation helps standardise data, ensuring it aligns with the requirements of Analytics, Machine Learning , or Business Intelligence tools. This makes drawing actionable insights, spotting patterns, and making data-driven decisions easier.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

DECEMBER 16, 2024

With an estimated market share of 30.03% , Microsoft Fabric is a preferred choice for businesses seeking efficient and scalable data solutions. Definition and Core Components Microsoft Fabric is a unified solution integrating various data services into a single ecosystem. What is Power BI?

Power BI 52

Power BI Analytics Analytics Machine Learning 52

IBM Journey to AI blog

OCTOBER 20, 2023

AI technology is quickly proving to be a critical component of business intelligence within organizations across industries. These factors are also important in identifying the AI platform that can be most effectively integrated to align with your business objectives. trillion in value.

AI 90

AI AI Machine Learning Machine Learning 90

Pickl AI

JANUARY 16, 2024

Statistical Analysis Firm grasp of statistical methods for accurate data interpretation. Programming Languages Competency in languages like Python and R for data manipulation. Machine Learning Understanding the fundamentals to leverage predictive analytics.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Analytics 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. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks.

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

DECEMBER 4, 2023

Implementing interoperable data platforms. Resource Allocation Improvement Optimises staff and resource allocation Balancing workload and resource availability Implementing predictive analytics for resource planning. 9,43,649 Business acumen, Data Visualisation tools (e.g., 12,00000 Programming (e.g.,

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

Data Science Blog

JULY 23, 2023

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. The integration of these technologies helps companies harness data for growth and efficiency.

Data Science 246

Data Science Azure Power BI Business Intelligence 246

IBM Journey to AI blog

JUNE 8, 2023

Data is the lifeblood of successful organizations. Beyond the traditional data roles—data engineers, analysts, architects—decision-makers across an organization need flexible, self-service access to data-driven insights accelerated by artificial intelligence (AI).

Business Intelligence 78

Business Intelligence Business Intelligence AI AI 78

The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Dataconomy

MARCH 19, 2025

Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics. Data engineering lays the groundwork by managing data infrastructure, while data preparation focuses on cleaning and processing data for analysis.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91