Data Science Dojo

JULY 6, 2023

Data engineering tools offer a range of features and functionalities, including data integration, data transformation, data quality management, workflow orchestration, and data visualization. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

Data Engineer 195

Data Engineer Data Engineering Data Engineering Data Engineering 195

Smart Data Collective

OCTOBER 14, 2019

They’re looking to hire experienced data analysts, data scientists and data engineers. With big data careers in high demand, the required skillsets will include: Apache Hadoop. Software businesses are using Hadoop clusters on a more regular basis now. Other coursework.

Big Data 99

Big Data Big Data Apache Hadoop Hadoop 99

Mlearning.ai

MAY 23, 2023

A good course to upskill in this area is — Machine Learning Specialization Data Visualization The ability to effectively communicate insights through data visualization is important. Check out this course to upskill on Apache Spark —  [link] Cloud Computing technologies such as AWS, GCP, Azure will also be a plus.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 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. Wrapping it up !!!

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 10, 2023

The programming language can handle Big Data and perform effective data analysis and statistical modelling. Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling. How is R Used in Data Science? Accordingly, Caret represents regression as well as classification training.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

JULY 20, 2023

Descriptive Analytics Projects: These projects focus on summarizing historical data to gain insights into past trends and patterns. Examples include generating reports, dashboards, and data visualizations to understand business performance, customer behavior, or operational efficiency.

Analytics 40

Analytics Analytics Big Data Big Data 40