Learning Paths

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.

Unsure which path to take?

Explore our Learning Paths

Short description

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.

Who is it for?

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.

Skills needed
  • Coding (Python, SQL)
  • Math & Stats
  • Machine Learning
  • Cloud Computing
  • Big Data (Spark, Hadoop)
  • Product Sense
  • Analytical Thinking
  • Coding (Python, SQL)
  • Math & Stats
  • Excel
  • Data Visualization
  • Data Wrangling
  • Business Communications
  • Domain Knowledge
  • Analytical Thinking
  • Coding (Python, Scala)
  • Containers (Docker, k8s)
  • Databases (SQL, NoSQL)
  • Big Data (Spark, Hadoop)
  • Cloud (AWS, GCP, Azure)
  • Data Ingestion (Kafka, API)
  • Data Integration (ETL, ELT)
  • Data Governance
  • Coding (Python, Java)
  • ML, Deep Learning
  • Tensorflow, PyTorch, TFX
  • ML at Scale (Spark ML)
  • Containerization (Docker)
  • Kubeflow, MLflow
  • CI/CD Cloud (AWS, GCP, Azure)
  • Airflow, SageMaker
  • Coding (Python, Shell Scripting, Hashicorp Language (HCL))
  • Terraform
  • Cloudformation
  • Containers and Container Orchestration (Docker & Kubernetes)
  • CI/CD
  • Building Declarative Configuration Files (YAML)
  • Cloud Computing (AWS, GCP, Azure)
  • Software Development Lifecycle (SDLC)
  • Application Development & Deployment
  • Linux SysAdmin
  • Analytical Thinking
Tools used
Job Titles
  • Data Scientist
  • Decision Scientist
  • Statistician
  • Data Science Specialist
  • ML Researcher
  • Analytics Consultant
  • ML Engineer
  • Machine Learning Scientist
  • AI Engineer
  • Data Analyst
  • Digital Analyst
  • BI Specialist
  • BI Developer
  • Marketing Data Analyst
  • Product Data Analyst
  • Visualization Analyst
  • Insights Analyst
  • Business Analyst
  • Data Engineer
  • Data Developer
  • Big Data Developer
  • Data Architect
  • ETL Specialist
  • Data Warehouse Specialist
  • Big Data Engineer
  • BI Engineer
  • DataOps Engineer
  • Data Governance Specialist
  • ML Engineer
  • MLOps Engineer
  • AI Engineer
  • AI Implementation Engineer
  • Deep Learning Specialist
  • DevOps Architect
  • DevOps Engineer
  • Infrastructure Developer
  • Site Reliability Engineer
  • Build and Release Manager
  • Full-stack Developer and Software Engineer
  • Automation Engineer
  • CI/CD Engineer
  • Systems and Cloud Architect
Salary Range

Junior DS:

  • $70k-$90k (Canada)
  • $90k-$120k (U.S.)

Senior DS:

  • May get paid well over $200k

Junior DA:

  • $60k-$80k (Canada)
  • $70k-$90k (U.S.)

Senior DA:

  • It’s not uncommon for Senior DA’s to be paid over $100k

Junior DE:

  • $70k-$90k (Canada)
  • $90k-$120k (U.S.)

Senior DE:

  • May get paid well over $200k

The average base pay for ML Engineers is between $110k and $130k in the U.S.

Junior DevOps engineer:

  • ~$70k (Canada)
  • ~$74k (U.S.)

Senior DevOps engineer:

  • $100k+ (Canada)
  • ~$200k (U.S.)

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