Following a proper learning path keeps you focused and saves you from unnecessary detours.
Developed by industry experts, hiring managers, and highly recognized by our hiring partners, WeCloudData’s learning paths have helped many students make successful transitions into data roles that fit their background and passion.
Help build better products, services, and improve business metrics via advanced predictive analytics, visualizations, and machine learning.
Help businesses make informed decisions and understand the “what” and “why” via data analysis and visual storytelling.
Build data pipelines that feed reliable data to every part of the data-driven decision engines via sound data architecting, workflow automation, big data, and data governance.
Train and tune advanced AI/ML models and put them in production via model pipelines, registry, packaging, deployment, and monitoring in a scalable way.
Help companies streamline and shorten the software development life cycle and provide continuous delivery with higher quality software.
Professionals who like to solve interesting data problems and help businesses realize the value of the data/ML insights.
Anyone who’s willing to develop solid coding skills, practical knowledge of applied statistics and machine learning, and a strong aptitude for business communication and data storytelling.
Professionals who want to help businesses answer key business metrics-related questions through database queries, deep-dive analysis, and visualizations.
Anyone who’s interested in building strong data sense and business acumen and is willing to learn SQL, Python, and data visualization.
IT professionals, data scientists, and analytics professionals who realize the importance of data engineering.
Anyone who likes problem-solving, gets excited about performance optimization, and gets fascinated by exciting technologies.
Suitable for people who are willing to learn coding and various tools in the cloud and big data space.
Data scientists, data engineers, software engineers, and AI researchers who are interested in ML model deployment in production.
Suitable for anyone who has prior experience with machine learning, data science, and has solid programming skills.
Professionals who want to build the necessary skills and technical acumen to thrive in today’s modern, cloud-native environment.
Especially suited for IT professionals who may have some background and experience in either software development or IT operations/sysadmin domains but want to complement, expand and up-skill with the holistic DevOps toolchain to advance their career.
The average base pay for ML Engineers is between $110k and $130k in the U.S.
Junior DevOps engineer:
Senior DevOps engineer:
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