Data Science
The vast range of careers in the data science industry is
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Data Engineering
Data engineering is the discipline of designing and building systems for collecting, storing, transforming, and analyzing data at scale to enable data scientists and analysts to extract meaningful insights. It involves creating data pipelines, managing databases, and ensuring data quality for various applications, including machine learning.
Why Pursue a Career in Data Engineering?
A career in this field can be both rewarding and challenging. You’ll play an important role in an organization’s success, providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. You’ll rely on your programming and problem-solving skills to create scalable solutions.
Data-related jobs can be broadly grouped into a family of roles, including data analysts, data scientists, data engineers, and business intelligence analysts. These roles often involve working with data to extract insights, build models, and support decision-making.
Data Engineering Use Cases
Data collection, storage and management
Data engineers streamline data intake and storage across an organization for convenient access and analysis. This facilitates scalability by storing data efficiently and establishing processes to manage it in a way that is easy to maintain as a business grows. The field of DataOps automates data management and is made possible by the work of data engineers.
Real-time data analysis
With the right data pipelines in place, businesses can automate the processes of collecting, cleaning and formatting data for use in data analytics. When vast quantities of usable data are accessible from one location, data analysts can easily find the information they need to help business leaders learn and make key strategic decisions.
The solutions that data engineers create set the stage for real-time learning as data flows into data models that serve as living representations of an organization’s status at any given moment.
Machine learning
Machine learning (ML) uses vast reams of data to train artificial intelligence (AI) models and improve their accuracy. From the product recommendation services seen in many e-commerce platforms to the fast-growing field of generative AI (gen AI), ML algorithms are in widespread use. Machine learning engineers rely on data pipelines to transport data from the point at which it is collected to the models that consume it for training.
Data Engineering Course Overview
Our Data engineering courses teach the design and construction of systems for collecting, storing, and analyzing large sets of data efficiently. Build your skills in technologies such as SQL, Data Warehousing, AWS, Informatica, Alteryx, Tableau/PowerBI, Spark Computing and more
Introduction to Data Engineering
This sets the stage, explaining what data engineers do and the role of data engineering in organizations.
Master SQL for Data Engineering
Learn to design databases in SQL to process, store, and organize data in a more efficient way. [Tools/Technologies involved: SQL, NOSQL databases]
Data Modeling and Design
Designing databases and data models that are efficient and scalable using high end databases [Tools/Technologies involved: SQL server, Oracle, AWS Redshift, Azure Synapse Analytics, GCP Bigquery]
Data Pipelines and ETL
Building pipelines to extract, transform, and load data from various sources. [Tools/Technologies involved: Alteryx, Informatica, AWS Glue]
Big Data Technologies
Exploring technologies like Hadoop and Spark for large-scale data processing. [Tools/Technologies involved: Spark/Hadoop]
Cloud Computing
Learning to use cloud services for data storage, processing, and analytics. [Tools/Technologies involved: AWS, AZURE & GCP]
Data Governance and Security
Implementing data governance policies and ensuring data security.
Business Intelligence Solutions
Learning to develop BI solutions using BI specific tools [Tools/Technologies involved: Tableau/PowerBI/PowerApps]
Python for Data Manipulation
Learning Python to develop ETL & automate the solutions [Tools/Technologies involved: Python, Pandas, Numpy, Madplotlib]
Hands-on Projects
Applying learned concepts to real-world scenarios, building data pipelines, and working with databases.