COURSES OFFERED BY WE CLOUD DATA

The Data Science with Python course is the second course in the data science learning path. It prepares you for a job as a data analyst or junior data scientist. It helps you understand the end-to-end workflow of a data science project.

Big Data for Data Scientists is a 6-week project-based course that teaches data scientists the necessary tools to work on large-scale data science problems. The entire course is built around an end-to-end real-time machine learning problem.

The big data course teaches aspiring data engineers how to scale ETL pipelines using Spark in the Azure cloud. According to the job market report published by WeCloudData, engineers with big data skills earn higher average salary.

The NLP and LLM short course is designed for data scientists and software engineers who want to become specialists in natural language processing.

The machine learning course is the perfect one for data analysts, junior data scientist, and python engineers who want to develop ML project experience, learn various industry use cases, and become a data scientist.

Whether you want to land a job in data science or data analytics, become a data engineer, or get prepared for WeCloudData’s data bootcamps, SQL, Python, and basic data visualization skills are essential.

Under the mentorship and guidance of WeCloudData’s career mentors as well as project managers, you learn to build up your real project experience and gain the confidence and necessary background to land your dream job in BI and data analytics.

Modern data scientists need to become familiar with cloud technologies. Most production data science solutions are implemented on private/public cloud. This course teaches the fundamentals of cloud computing for data scientists such as storage and compute.

Data collection and extraction is the first step in most of the data science projects. Learners will learn how to extract data from the web and databases using Python in this module.

This module focuses on the Computer Vision applications of deep learning.