DataSeries

AUGUST 15, 2024

Hadoop, Spark). Familiarize with data visualization techniques and tools like Matplotlib, Seaborn, Tableau, or Power BI. Understanding these fundamentals is essential for effective problem-solving in data engineering.

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

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 195

Data Science Machine Learning Machine Learning Data Visualization 195

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges. To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI.

Data Science 98

Data Science Analytics Analytics Data Scientist 98

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

Dataconomy

JULY 24, 2023

Tools like Tableau, Matplotlib, Seaborn, or Power BI can be incredibly helpful. Learn relevant tools Familiarize yourself with data science tools and platforms, such as Tableau for data visualization, or Hadoop for big data processing. This is where data visualization comes in.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

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

Pickl AI

APRIL 2, 2024

Tableau/Power BI: Visualization tools for creating interactive and informative data visualizations. Hadoop/Spark: Frameworks for distributed storage and processing of big data. Tools and Technologies Python/R: Popular programming languages for data analysis and machine learning.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52