Sisense

MARCH 20, 2019

First, let’s start with basic language skills. Python is very easy to learn, and one of the most powerful programming languages. What our data engineers like about this course is that it is geared towards the data scientists and covers practical issues for statistical computing. Python and R. Database Knowledge.

Big Data 72

Big Data Machine Learning Data Warehouse Unstructured Data 72

Data Science 101

MAY 6, 2019

The big problem that we were solving at Mercedes is basically: how do you build machine learning into an environment where there’s limited-to-no data connectivity? There’s this data set that I send out to people that’s from the UCI Machine Learning Repository. And if you’re not practicing, you can’t develop that intuition.

Data Science 98

Data Science Modeling Machine Learning Testing 98

FineReport

DECEMBER 19, 2019

I know novices are very concerned about the learning process of data analysis. You may be full of doubts and yearnings for SQL, Python, R, etc. There are so many things to learn, which one should I learn? How do I learn, and to what extent? Second, learn SQL and understand the concept of database.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. In-context learning LLMs are trained with point-in-time data and have no inherent ability to access fresh data at inference time. The following diagram illustrates a basic in-context learning workflow.

Data Lake 87

Data Lake Unstructured Data Management Modeling 87

O'Reilly on Data

SEPTEMBER 15, 2021

Learning new skills and improving old ones were the most common reasons for training, though hireability and job security were also factors. We didn’t use the data from these respondents; in practice, discarding this data had no effect on the results. Compensation Basics. Salaries by Programming Language.

Machine Learning 363

Machine Learning Statistics Reporting Consulting 363

Sisense

APRIL 15, 2021

Augmented analytics (according to Gartner, which would know), uses technologies “such as machine learning [ML] and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.” Tools of the AI trade.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

FineReport

DECEMBER 15, 2021

Here I provide some practical suggestions to help you create a well-oiled strategic dashboard. Here, I list some most basic visual elements you can use to build a dashboard. Basic charts , including line charts, pie charts, radar charts, rose charts and so on. Basic Charts. Identify the target audience. Carousel KPI card.

Dashboards 52

Dashboards KPI Metrics Insurance 52

FineReport

DECEMBER 15, 2021

Here I provide some practical suggestions to help you create a well-oiled strategic dashboard. Here, I list some most basic visual elements you can use to build a dashboard. Basic charts , including line charts, pie charts, radar charts, rose charts and so on. Basic Charts. Identify the target audience. Carousel KPI card.

Dashboards 52

Dashboards KPI Metrics Insurance 52

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

Alation

OCTOBER 7, 2021

Spatial data isn’t special but thinking spatially – now that’s a skill everyone should learn. It’s just data — they argued — and any data person, with a few basics under their belt, should be empowered to work with it. Spatial data skills are ones anyone can and should learn to be an effective 21st century data scientist.

Data Lake 98

Data Lake Visualization Data Science Digital Transformation 98

DataRobot

SEPTEMBER 17, 2021

So they create business or “decision rules” that act on the output of their predictive models to add business best practices and standardize decision-making across the organization. We see them used in machine learning use cases in every industry. Want to Learn More? Here are some other use cases examples.

Forecasting 52

Forecasting Predictive Modeling Machine Learning Cost-Benefit 52

Smart Data Collective

JUNE 4, 2021

Basic knowledge of statistics is essential for data science. it is overwhelming to learn data science concepts and a general-purpose language like python at the same time. Basics of Machine Learning. Machine learning is the science of building models automatically. In supervised learning, a variable is predicted.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

Insight

AUGUST 12, 2019

Let’s say you’ve learned the fundamentals and are ready to start your job hunt. There is a lot here to chew on, so don’t expect to be able to learn everything all at once. Certain activities lend themselves to daily practice, like CodeSignal problems or OverTheWire challenges. Learn vim by simply using the vimtutor command.

Software 65

Software Data-driven Testing Interactive 65

AWS Big Data

AUGUST 3, 2023

Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. Run the following SQL using Athena. To learn more about authoring dashboards in QuickSight, check out the QuickSight Author Workshop.

Data Lake 76

Data Lake Visualization Dashboards Insurance 76

Cloudera

SEPTEMBER 28, 2020

ACID transactions, ANSI 2016 SQL SupportMajor Performance improvements. This customer’s workloads leverage batch processing of data from 100+ backend database sources like Oracle, SQL Server, and traditional Mainframes using Syncsort. Data Science and machine learning workloads using CDSW. New Features CDH to CDP. Cluster Type.

Testing 131

Testing Metadata Risk Data Science 131

datapine

OCTOBER 10, 2019

How To Create A Dashboard – 6 Best Practices. Now we will focus on 6 essential tips and best practices to follow when creating dashboards, starting with defining your audience. In the rest of this post, you’ll learn how to do just that. Follow These Essential Design Basics. Select The Right Chart Type For Your Data.

Dashboards 146

Dashboards Visualization Sales Metrics 146

Smart Data Collective

MAY 20, 2021

Often, it is a large investment, and you will need to dedicate a lot of time and energy to learning new skills or developing existing ones. Business Analytics Basics. It shows that you have a genuine passion and interest in the subject and that you are willing to devote extra time and energy to learning more about it.

Business Analytics 108

Business Analytics Analytics Cost-Benefit Data-driven 108

Data Insight

APRIL 15, 2019

Start small, create a basic data mart, then encourage your analyst to deliver speed over perfection.” What works is to foster a culture that encourages the delivery of analytics projects quickly and to incorporate learnings into future projects. Then learn from your mistakes, and continually improve.

Analytics 40

Analytics Metrics Software Reporting 40

AWS Big Data

FEBRUARY 24, 2023

As a best practice of a data lake design, data should be immutable once stored. On the Athena console, create an Athena workgroup named demoworkgroup for SQL queries. To learn more about AWS integrations, refer to Iceberg AWS integrations. Choose Athena engine version 3 for Query engine version.

Data Lake 79

Data Lake Metadata Testing Data Warehouse 79

AWS Big Data

APRIL 3, 2023

With Amazon Redshift, you can query data across your data warehouse, operational data stores, and data lake using standard SQL. To learn about new options for database scripting, refer to Accelerate your data warehouse migration to Amazon Redshift – Part 4. The converted scripts run on Amazon Redshift with little to no changes.

Data Warehouse 95

Data Warehouse Testing Data Lake Data-driven 95

Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. This month, let’s explore learning data science. What are some good learning materials available to the public?

Data Science 56

Data Science Machine Learning Reporting Visualization 56

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. The Altron team created an AWS Glue crawler and configured it to run against Azure SQL to discover its tables. Basic formatting and readability of the data is standardized here.

Optimization 87

Optimization B2B Data Quality Sales 87

CIO Business Intelligence

JANUARY 17, 2023

There was a huge improvement in standardization and the basics of running things,” he says. You just inherit all these best practices that we were inventing or reinventing ourselves.” And a particular gripe of his was that you have to build your own documentation best practices. That was very fast.”

IT 126

IT Data Processing Reporting Marketing 126

datapine

FEBRUARY 4, 2020

But first, we will start with a basic definition and some tips on creating these kinds of reports. If you get your practice right and utilize some effective data driven marketing strategy tips and tricks, a report can be generated with just a few clicks. Let’s dig deeper. Use professional software. 2) Marketing KPI Report.

Reporting 280

Reporting Marketing Advertising Metrics 280

Domino Data Lab

JULY 2, 2019

SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

datapine

FEBRUARY 17, 2023

Here, we will look at these concepts in more detail, explaining their power and potential in a practical context. Boost brand identity: Boosting and reinforcing brand identity is always a valuable practice, no matter if it’s on an internal or an external level. Let’s get started with some definitions!

Analytics 191

Analytics Cost-Benefit Dashboards Business Intelligence 191

Cloudera

NOVEMBER 23, 2021

It leverages the basic features of HDFS and builds upon it to provide scalability by handling a large volume of the read and write requests in real-time. Learn more about Apache HBase. Apache Phoenix is a RDBMS, an ANSI SQL interface. Phoenix provides: SQL and JDBC API support. Learn more about Apache Phoenix.

Metadata 91

Metadata Data-driven Modeling Big Data 91

datapine

SEPTEMBER 16, 2022

Previously, we discussed the top 19 big data books you need to read, followed by our rundown of the world’s top business intelligence books as well as our list of the best SQL books for beginners and intermediates. The author recently published an “expanded follow-up” to her book called “Storytelling With Data: Let’s Practice!”.

Visualization 304

Visualization Dashboards Data-driven Statistics 304

FineReport

JULY 18, 2022

Its typical users are IT staff with basic SQL knowledge. It is developed mainly for business personnel or data analysts with basic business logic and data knowledge to design their own dashboard independently and analyze the data they really need, leading to wiser business decisions for long-term development. From FineReport.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

Smart Data Collective

FEBRUARY 20, 2022

What is the role of machine learning in monitoring database activity? For some vendors, the basic parameter is the number of database servers or cores; for others – it’s the amount of the processed traffic and the number of transactions. DAM deployment best practices. Does DAM need a user behavior analytics (UBA) module?

Behavioral Analytics 118

Behavioral Analytics Risk Machine Learning Interactive 118

Business Over Broadway

JANUARY 20, 2019

Data scientists and machine learning engineers rely on programming languages to help them get insights from data. Principal Component Matrix of 16 Programming Languages – data from Kaggle 2018 Machine Learning and Data Science survey of data professionals. R, SQL, Visual Basic/VBA, SAS/STATA. Click image to enlarge.

Machine Learning 79

Machine Learning Data Science Data-driven Statistics 79

Alation

JANUARY 25, 2022

One nuance is that the data catalog is more than a modern-day data dictionary ; it goes beyond basic dictionary features, like names for technical metadata, data types, keying, and constraints. In a practical sense, a modern data catalog should capture a broad array of metadata that also serves a broader array of consumers.

Metadata 52

Metadata IT Metrics Data-driven 52

datapine

NOVEMBER 11, 2021

billion , paired with the fact that 33% of large-sized businesses will practice decision intelligence by 2023. And not just that, with COVID-19 and remote work now being a permanent business practice, the need for more intuitive platforms that will facilitate teamwork has become critical. d) Trend analysis. d) Custom design.

Business Intelligence 213

Business Intelligence Dashboards Interactive Visualization 213

Peter James Thomas

MAY 3, 2019

The flip side is that making the necessary investments to provide even basic information has been at the heart of the successful business turnarounds that I have been involved in. Best practice has evolved in this area. Instead best practice now encompasses two repositories: the first Operational, the second Analytical.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

Cloudera

NOVEMBER 2, 2020

As such, many customers are building RTDW applications as part of their overall strategy of using Cloudera to modernize their data warehouse practice. Basic Architecture for Real-Time Data Warehousing. for active archive or joining live data with historical data), or machine learning. SQL editor for running Impala+Kudu queries.

Data Warehouse 96

Data Warehouse Optimization Dashboards Interactive 96

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. Deep learning,” for example, fell year over year to No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Alation

SEPTEMBER 23, 2021

Collaborative catalog features, like ratings, reviews, and conversations , can crowdsource that expertise so folks learn from each other and build on one another’s work. SQL Query Log Analysis – SQL analysis provides insights as to the usage log and the behavior of people accessing data sets.

Enterprise 52

Enterprise Metadata Data Governance Visualization 52