Organizing, storing, protecting, and maintaining data, utilizing tools and technologies such as cloud storage, data lakes, warehouses, ETL pipelines, and data governance frameworks
Inspecting, cleaning, transforming, and modeling data to derive insights and conclusions, which can be achieved through tools like Python, R, SQL, statistical and ML algorithms.
Graphical representation of information and data using Tableau, Power BI, Looker, Python's libraries to enhance understanding, gain insights, and make better decisions.
The process of combining data from disparate sources into a unified view, and popular tools and technologies used for this include Apache Kafka, AWS Glue and Azure Data Factory.
Using statistical, machine learning, and programming skills to extract insights and knowledge from data using tools such as Python, R, SQL, Hadoop, Spark, TensorFlow, etc.
Technology-driven process for analyzing data and delivering insights, leveraging tools such as Power BI, Tableau, and Snowflake to enable data-driven decisions.
Enable users to efficiently store, process, manage, and analyze data in the cloud through scalable and cost-effective solutions using AWS, Google or Microsoft cloud technologies.
Designing, building, and maintaining data pipelines, using Apache Spark, Hadoop, Kafka, Airflow, Azure and AWS services, to ensure data is reliable, efficient, and accessible for analysis.
Providing expert advice and solutions for managing and analyzing complex data sets using tools and technologies such as Python, Tableau, and cloud-based platforms like AWS and Azure.
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