Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

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 Engineer Data Engineering Data Engineering 52

Smart Data Collective

APRIL 14, 2021

If you occasionally run business stands in fairs, congresses and exhibitions, business stands designers can incorporate business intelligence to aid in better business and client data collection. Business intelligence tools can include data warehousing, data visualizations, dashboards, and reporting.

Business Intelligence 133

Business Intelligence Business Intelligence Tableau SQL 133

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 85

Data Science Analytics Analytics Data Scientist 85

ODSC - Open Data Science

APRIL 6, 2023

Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language , so important to learn? Let’s start with the first clause often learned by new SQL users, the WHERE clause.

SQL 98

SQL Data Scientist Database Data Analysis 98

Pickl AI

AUGUST 28, 2023

Key Features of Power BI: Power BI is a powerful business intelligence tool developed by Microsoft that enables users to visualize and analyze data from various sources. It offers a wide range of features that make it a popular choice for data professionals, analysts, and organizations. Choose your data source (e.g.,

Power BI 52

Power BI Data Analysis Data Analysis Data Visualization 52

Dataconomy

MAY 2, 2023

What do machine learning engineers do: They implement and train machine learning models Data modeling One of the primary tasks in machine learning is to analyze unstructured data models, which requires a solid foundation in data modeling. R is especially popular in academia and research.

ML 110

ML ML Machine Learning Machine Learning 110

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 60

Machine Learning Machine Learning Data Science Big Data 60

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

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 133

Tableau Analytics Analytics Machine Learning 133

Mlearning.ai

FEBRUARY 13, 2023

With the use of keys, relational databases can easily define relationships between data elements, making them ideal for structured data like customer information, financial transactions, and product inventory. Some of the most popular relational databases include Oracle, MySQL, and Microsoft SQL Server. Popular relational DBs 2.

Database 52

Database Data Modeling Data Models SQL 52

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science 105

Data Science Exploratory Data Analysis Machine Learning Machine Learning 105

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Pickl AI

JANUARY 16, 2024

Join me in understanding the pivotal role of Data Analysts , where learning is not just an option but a necessity for success. Key takeaways Develop proficiency in Data Visualization, Statistical Analysis, Programming Languages (Python, R), Machine Learning, and Database Management.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Analytics 52

Mlearning.ai

APRIL 29, 2023

This case study as we’ve mentioned above is all about calculating metrics, and growth and helping the business analyze their data in a smart way to better forecast and plan for their future developments! Submission Suggestions 8-Week SQL Challenge: Data Bank was originally published in MLearning.ai BECOME a WRITER at MLearning.ai

SQL 52

SQL Power BI Cloud Data Data Analysis 52

Pickl AI

DECEMBER 4, 2023

Technologies, tools, and methodologies Imagine Data Intelligence as a toolbox filled with gadgets for every analytical need. From powerful analytics software to Machine Learning algorithms, these tools transform data into actionable intelligence. 6,20000 Analytical skills, proficiency in Data Analysis tools (e.g.,

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

phData

MAY 25, 2023

Two of the platforms that we see emerging as a popular combination of data warehousing and business intelligence are the Snowflake Data Cloud and Power BI. Debuting in 2015, Power BI has undergone meaningful updates that have made it a leader not just in data visualization, but in the business intelligence space as well.

Power BI 84

Power BI Analytics Analytics Azure 84

phData

MARCH 28, 2024

Use Cases and Examples Tables are best for data presentation, analysis and reporting, and data modeling. Presentation: Commonly used to present structured data in a spreadsheet-like format; each row represents a record, and each column represents a different attribute or measure associated with that record.

Data Modeling 52

Data Modeling Data Models SQL Data Visualization 52

Cambridge Intelligence

OCTOBER 25, 2023

When you design your data model, you’ll probably begin by sketching out your data in a graph format – representing entities as nodes and relationships as links. Working in a graph database means you can take that whiteboard model and apply it directly to your schema with relatively few adaptations. age > 50 AND p2.gender

Database 64

Database Data Modeling Data Models Algorithm 64

phData

JULY 17, 2023

Data Ingestion) Fivetran Transformation Once various data sources are available in the data warehouse, data transformation is needed to create usable datasets. Data modeling, data cleanup, etc. We can also create advanced data science models with this data using AI/ Machine Learning.

Data Warehouse 52

Data Warehouse Data Pipeline Cloud Data ETL 52

Cambridge Intelligence

OCTOBER 19, 2023

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. Many of our customers successfully build fast and scalable graph visualization applications without a graph database. With data stored as a graph, it makes sense to query it as a graph too.

Database 59

Database Azure SQL AWS 59

Pickl AI

JUNE 12, 2023

Tableau is an interactive platform that enables users to analyse and visualise data to gain insights. Consequently, if your results, scores, etc are stored in an SQL Database, Tableau can be able to quickly visualise easily your model metrics. With SQL queries Tableau helps in integrating with them effectively.

Tableau 52

Tableau Data Science Data Scientist Data Analysis 52

AWS Machine Learning Blog

AUGUST 21, 2023

Data professionals such as data scientists want to use the power of Apache Spark , Hive , and Presto running on Amazon EMR for fast data preparation; however, the learning curve is steep. Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model.

AWS 83

AWS Data Lakes Clustering Data Preparation 83

phData

JULY 18, 2023

Support for Numerous Data Sources: Fivetran supports over 200 data sources, including popular databases, applications, and cloud platforms like Salesforce, Google Analytics, SQL Server, Snowflake, and many more. Additionally, unsupported data sources can be integrated using Fivetran’s cloud function connectors.

Data Warehouse 52

Data Warehouse SQL Cloud Data Analytics 52

Tableau

DECEMBER 1, 2021

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Relationships in Tableau 2020.2 (May Beginning in Tableau 2020.2,

Tableau 144

Tableau ML ML Database 144

Tableau

DECEMBER 1, 2021

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Relationships in Tableau 2020.2 (May Beginning in Tableau 2020.2,

Tableau 98

Tableau ML ML Database 98