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 Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 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. NoSQL and SQL. Machine Learning. Other coursework.

Big Data 99

Big Data Big Data Apache Hadoop Hadoop 99

Pickl AI

JUNE 5, 2025

Big Data Technologies: Familiarity with Hadoop, Apache Spark, and cloud platforms like AWS, Azure, and Google Cloud is increasingly important as Indian companies scale data operations. Data Visualization: Ability to create intuitive visualizations using Matplotlib, Seaborn, Tableau, or Power BI to convey insights clearly.

Data Scientist 52

Data Scientist Data Science Power BI Machine Learning 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 Machine Learning Machine Learning 195

Pickl AI

DECEMBER 25, 2024

Programming languages like Python and R are commonly used for data manipulation, visualization, and statistical modeling. Machine learning algorithms play a central role in building predictive models and enabling systems to learn from data. Data Scientists rely on technical proficiency.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

Women in Big Data

OCTOBER 9, 2024

Data Processing (Preparation): Ingested data undergoes processing to ensure it’s suitable for storage and analysis. This phase ensures quality and consistency using frameworks like Apache Spark or AWS Glue. 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

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