Women in Big Data

OCTOBER 9, 2024

They can process data in real-time, in batches, or through hybrid methods, allowing organizations to scale operations and complete tasks in a fraction of the time traditional pipelines require. Components of a Big Data Pipeline Data Sources (Collection): Data originates from various sources, such as databases, APIs, and log files.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Pickl AI

AUGUST 9, 2024

Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities. Businesses need to analyse data as it streams in to make timely decisions. This diversity requires flexible data processing and storage solutions. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 18, 2023

It is popular for its powerful data visualization and analysis capabilities. Hence, Data Scientists rely on R to perform complex statistical operations. With a wide array of packages like ggplot2 and dplyr, R allows for sophisticated data visualization and efficient data manipulation.

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

JANUARY 13, 2025

They employ statistical methods and machine learning techniques to interpret data. Key Skills Expertise in statistical analysis and data visualization tools. They play a crucial role in shaping business strategies based on data insights. Key Skills Proficiency in data visualization tools (e.g.,

AI 52

AI AI Machine Learning Machine Learning 52

Dataconomy

JULY 25, 2023

It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze data, draw conclusions, and forecast future trends. Data scientists use a combination of programming languages (Python, R, etc.), Acquiring and maintaining this breadth of knowledge can be challenging and time-consuming.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

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. Data can be structured (e.g., databases), semi-structured (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Mlearning.ai

MARCH 15, 2023

Responsibilities of a Data Analyst Data analysts, on the other hand, help businesses and organizations make data-driven decisions through their analytical skills. Their job is mainly to collect, process, analyze, and create detailed reports on data to meet business needs. Basic programming knowledge in R or Python.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

Pickl AI

FEBRUARY 28, 2023

Significantly, Data Science experts have a strong foundation in mathematics, statistics, and computer science. Furthermore, they must be highly efficient in programming languages like Python or R and have data visualization tools and database expertise. Who is a Data Analyst?

Data Analyst 52

Data Analyst Data Scientist Data Science Computer Science 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.,

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

ODSC - Open Data Science

JUNE 1, 2023

Apache Pinot is a real-time OLAP database built at LinkedIn to deliver scalable real-time analytics with low latency. It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, and Google Cloud Storage) as well as stream data sources (such as Apache Kafka and Redpanda).

Analytics 98

Analytics Analytics Apache Kafka Data Science 98

Alation

MARCH 29, 2021

Alation helps connects to any source Alation helps connect to virtually any data source through pre-built connectors. Alation crawls and indexes data assets stored across disparate repositories, including cloud data lakes, databases, Hadoop files, and data visualization tools.

Machine Learning 59

Machine Learning Machine Learning Data Lakes Hadoop 59

Pickl AI

JUNE 19, 2024

Popular libraries for Data Science in Python include NumPy (numerical computing), pandas (data manipulation and analysis), and scikit-learn (machine learning algorithms). R: A powerful language specifically designed for statistical computing and data visualization. Databases and SQL Data doesn’t exist in a vacuum.

Data Science 52

Data Science Data Scientist Hypothesis Testing Natural Language Processing 52

ODSC - Open Data Science

APRIL 26, 2023

As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Data Science Dojo

MAY 24, 2024

Navigate through 6 Popular Python Libraries for Data Science R R is another important language, particularly valued in statistics and data analysis, making it useful for AI applications that require intensive data processing. C++ C++ is essential for AI engineering due to its efficiency and control over system resources.

Deep Learning 195

Deep Learning Deep Learning AI Machine Learning 195

Pickl AI

APRIL 2, 2024

Understanding Data Structured Data: Organized data with a clear format, often found in databases or spreadsheets. Unstructured Data: Data without a predefined structure, like text documents, social media posts, or images. Hadoop/Spark: Frameworks for distributed storage and processing of big data.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

Pickl AI

DECEMBER 4, 2024

This foundational knowledge is essential for any Data Science project. Develop Programming Skills Proficiency in programming languages is crucial for Data Scientists. Focus on Python and R for Data Analysis, along with SQL for database management.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

MARCH 19, 2025

Without data engineering , companies would struggle to analyse information and make informed decisions. What Does a Data Engineer Do? A data engineer creates and manages the pipelines that transfer data from different sources to databases or cloud storage. How is Data Engineering Different from Data Science?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52