Data Science Dojo

MAY 24, 2024

Java is also widely used in big data technologies, supported by powerful Java-based tools like Apache Hadoop and Spark, which are essential for data processing in AI. Big Data Technologies With the growth of data-driven technologies, AI engineers must be proficient in big data platforms like Hadoop, Spark, and NoSQL databases.

Deep Learning 195

Deep Learning Deep Learning Machine Learning Machine Learning 195

Pickl AI

MARCH 19, 2025

A data engineer creates and manages the pipelines that transfer data from different sources to databases or cloud storage. Data Storage : Keeping data safe in databases or cloud platforms. It allows them to retrieve, manipulate, and manage structured data in relational databases. What Does a Data Engineer Do?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Data Science Dojo

JULY 3, 2024

Tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn are commonly taught. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. R : Often used for statistical analysis and data visualization.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Alation

FEBRUARY 20, 2020

A “catalog-first” approach to business intelligence enables both empowerment and accuracy; and Alation has long enabled this combination over Tableau. Alation’s deep integration with tools like MicroStrategy and Tableau provides visibility into the complete data pipeline: from storage through visualization.

Data Governance 52

Data Governance Tableau Hadoop Data Pipeline 52

Dataconomy

JULY 24, 2023

” Data management and manipulation Data scientists often deal with vast amounts of data, so it’s crucial to understand databases, data architecture, and query languages like SQL. Tools like Tableau, Matplotlib, Seaborn, or Power BI can be incredibly helpful. This is where data visualization comes in.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Women in Big Data

OCTOBER 9, 2024

Components of a Big Data Pipeline Data Sources (Collection): Data originates from various sources, such as databases, APIs, and log files. Examples include transactional databases, social media feeds, and IoT sensors. Batch Processing: For large datasets, frameworks like Apache Hadoop MapReduce or Apache Spark are used.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Smart Data Collective

MAY 20, 2019

With databases, for example, choices may include NoSQL, HBase and MongoDB but its likely priorities may shift over time. For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. SQL programming skills, specific tool experience — Tableau for example — and problem-solving are just a handful of examples.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

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 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

MARCH 15, 2023

Familiarity with Databases; SQL for structured data, and NOSQL for unstructured data. Experience with visualization tools like; Tableau and Power BI. Knowledge of big data platforms like; Hadoop and Apache Spark. Knowledge of big data platforms like; Hadoop and Apache Spark. Basic programming knowledge in R or Python.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

Pickl AI

JANUARY 13, 2025

Familiarity with SQL for database management. Strong understanding of database management systems (e.g., Hadoop , Apache Spark ) is beneficial for handling large datasets effectively. They play a crucial role in shaping business strategies based on data insights. Salary Range: 6,00,000 – 18,00,000 per annum.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 28, 2023

Furthermore, they must be highly efficient in programming languages like Python or R and have data visualization tools and database expertise. Significantly, in contrast, Data Analysts utilise their proficiency in a relational databases, Business Intelligence programs and statistical software. Who is a Data Analyst?

Data Analyst 52

Data Analyst Data Scientist Data Science Computer Science 52

Pickl AI

JANUARY 28, 2025

They encompass all the origins from which data is collected, including: Internal Data Sources: These include databases, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and flat files within an organization. databases), semi-structured (e.g., Data can be structured (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

phData

FEBRUARY 5, 2024

Creating the databases, schemas, roles, and access grants that comprise a data system information architecture can be time-consuming and error-prone. Replicate can interact with a wide variety of databases, data warehouses, and data lakes (on-premise or based in the cloud).

SQL 52

SQL Database Data Quality Data Warehouse 52

ODSC - Open Data Science

APRIL 26, 2023

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. Some of the tools and techniques unique to business analysts are pivot tables, financial modeling in Excel, Power BI Dashboards for forecasting, and Tableau for similar purposes.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Pickl AI

DECEMBER 26, 2024

Variety highlights the diverse data formats, including text, images, videos, and structured databases. Role of Analytics Tools in Big Data Analytics tools like Hadoop , Tableau , and predictive platforms make Big Data manageable.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g., Hadoop, Spark).

Data Engineer 40

Data Engineer Data Engineering Data Engineering Data Engineering 40

Pickl AI

MAY 29, 2024

SQL is indispensable for database management and querying. Proficiency with tools like Tableau , Matplotlib , and ggplot2 helps create charts, graphs, and dashboards that effectively communicate insights to stakeholders. The curriculum covers data extraction, querying, and connecting to databases using SQL and NoSQL.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

Understanding Data Structured Data: Organized data with a clear format, often found in databases or spreadsheets. SQL (Structured Query Language): Language for managing and querying relational databases. Tableau/Power BI: Visualization tools for creating interactive and informative data visualizations.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

Pickl AI

DECEMBER 4, 2024

Focus on Python and R for Data Analysis, along with SQL for database management. Gain Experience with Big Data Technologies With the rise of Big Data, familiarity with technologies like Hadoop and Spark is essential. Learn to use tools like Tableau, Power BI, or Matplotlib to create compelling visual representations of data.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52