Great summary of the current technologies in data analytics! Indeed, tools and platforms like machine learning, AI, big-data frameworks (e.g. Hadoop, Spark), data visualization (Tableau, Power BI), cloud services (AWS, Azure, Google Cloud), SQL/NoSQL databases, and languages like Python and R (with libraries such as Pandas, NumPy, Scikit-learn) — along with data warehousing solutions like Redshift and Snowflake — form the backbone of modern analytics infrastructure.
If your analytics needs go deeper — especially involving advanced pattern detection, predictive analytics, image/video data, or complex unstructured data — having specialized deep learning expertise can make a big difference. I’ve explored this in a blog that outlines what it means to hire deep learning experts, the core skills and roles involved, and the advantages they bring to analytics or AI-driven projects: [Hire Deep Learning Experts: Skills, Roles & Project Advantages](https://www.amplework.com/blog/hire-deep...dvantages/).
For teams evaluating whether to stick with traditional analytics or step up to deep-learning based solutions, this may help guide that decision.
If your analytics needs go deeper — especially involving advanced pattern detection, predictive analytics, image/video data, or complex unstructured data — having specialized deep learning expertise can make a big difference. I’ve explored this in a blog that outlines what it means to hire deep learning experts, the core skills and roles involved, and the advantages they bring to analytics or AI-driven projects: [Hire Deep Learning Experts: Skills, Roles & Project Advantages](https://www.amplework.com/blog/hire-deep...dvantages/).
For teams evaluating whether to stick with traditional analytics or step up to deep-learning based solutions, this may help guide that decision.