Smart Data Collective

MAY 20, 2019

We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason. Basic Business Intelligence Experience is a Must.

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Analytics Analytics Data Analyst Machine Learning 111

Dataconomy

JULY 24, 2023

Phase 3: Acquiring ancillary skills Apart from programming languages, data scientists should also familiarize themselves with tools and techniques for data visualization, machine learning, and handling big data. The question “How to become a data scientist?”

Data Scientist 172

Data Scientist Data Analyst Data Science Machine Learning 172

Smart Data Collective

JUNE 3, 2019

In a previous article I shared some of the challenges, benefits and trends of Big Data in the telecommunications industry. Big Data’s promise of value in the financial services industry is particularly differentiating. Customer-focused analysis dominates Big Data initiatives. Debt and Income Ratio.

Big Data 73

Big Data Big Data Data Silos AI 73

Data Science Blog

MAY 15, 2023

Data Lakehouse Architecture Eine kurze Geschichte des Data Lakehouse Das Konzept des Data Lakehouse ist relativ neu und entstand Mitte der 2010er Jahre als Reaktion auf die Einschränkungen des traditionellen Data Warehousing und die wachsende Beliebtheit von Data Lakes.

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Data Warehouse Data Lakes Azure AWS 147

Dataconomy

JULY 25, 2023

Diverse job roles: Data science offers a wide array of job roles catering to various interests and skill sets. Some common positions include data analyst, machine learning engineer, data engineer, and business intelligence analyst. This versatility provides added job security and flexibility in career choices.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Pickl AI

JULY 18, 2023

Java: Scalability and Performance Java is renowned for its scalability and robustness, making it an excellent choice for handling large-scale data processing. With its powerful ecosystem and libraries like Apache Hadoop and Apache Spark, Java provides the tools necessary for distributed computing and parallel processing.

Data Science 52

Data Science SQL Data Scientist Apache Hadoop 52

Pickl AI

FEBRUARY 28, 2023

However, Data Scientists use tools like Python, Java, and Machine Learning for manipulating and analysing data. Significantly, in contrast, Data Analysts utilise their proficiency in a relational databases, Business Intelligence programs and statistical software.

Data Analyst 52

Data Analyst Data Scientist Data Science Computer Science 52

Data Science Dojo

JANUARY 12, 2023

It can process any type of data, regardless of its variety or magnitude, and save it in its original format. Hadoop systems and data lakes are frequently mentioned together. However, instead of using Hadoop, data lakes are increasingly being constructed using cloud object storage services.

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Data Lakes Data Warehouse Hadoop Machine Learning 221

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

Data Science 84

Data Science Analytics Analytics Data Scientist 84

Analytics Vidhya

JUNE 27, 2020

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

Machine Learning 400

Machine Learning Machine Learning Data Science Analytics 400

O'Reilly Media

SEPTEMBER 19, 2023

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.

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Smart Data Collective

SEPTEMBER 8, 2021

The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements. Extract : In this step, data is extracted from a vast array of sources present in different formats such as Flat Files, Hadoop Files, XML, JSON, etc.

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ETL Hadoop Data Warehouse Data Pipeline 94

Pickl AI

NOVEMBER 15, 2023

Data scientists can explore, experiment, and derive valuable insights without the constraints of a predefined structure. This capability empowers organizations to uncover hidden patterns, trends, and correlations in their data, leading to more informed decision-making. It often serves as a source for Data Warehouses.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Alation

FEBRUARY 20, 2020

Business Intelligence used to require months of effort from BI and ETL teams. Today, any data scientist, business analyst or business person can use Trifacta to transform, prepare, and move data. First, there is no easy way to find the data you want to prepare. Now you have iPhones and YouTube.

ETL 52

ETL Hadoop Tableau Business Intelligence 52

Pickl AI

APRIL 2, 2024

The Three Types of Data Science Data science isn’t a one-size-fits-all solution. There are three main types, each serving a distinct purpose: Descriptive Analytics (Business Intelligence): This focuses on understanding what happened. Hadoop/Spark: Frameworks for distributed storage and processing of big data.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52