Pickl AI

JULY 25, 2023

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. Big Data Technologies: Hadoop, Spark, etc. ETL Tools: Apache NiFi, Talend, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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. High proficiency in visualization tools like; Tableau, Google Studio, and Power BI.

Data Science 52

Data Science Analytics Analytics Data Analyst 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 Analyst Data Science Machine Learning 172

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 you can expect to see used will be Power BI and Tableau Data Architect Before you ask, yes a data architect and a data engineer are quite different.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 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 83

Data Science Analytics Analytics Data Scientist 83

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52

Smart Data Collective

MAY 20, 2019

For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. 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