What You Will Learn!
- How To Design A Database
- How To Install MySQL
- Steps To Design A Simple Data base
- Understand the difference between Data / Database and DBMS
- Fundamentals of cloud computing
- DP-203 : Data Engineering on Microsoft Azure Part 1
- How to Use Microsoft Excel For Data Analysis
- How To Design Dashboards Using Power BI
- Introduction To Python & Jupyter Notebook
- Numpy: Data science and analysis Using Python 1
Description
Data Engineer Technical Skills
To become a data engineer, you should be very good at SQL, and you should know those programming languages used for statistical modeling and data analysis. Also you should know, how to design a data warehousing solutions, and how to build data pipelines.
Database design:
You should know SQL. SQL is the standard programming language for building and managing relational database systems.
Data warehousing solutions:
ETL tools.:
In computing, extract, transform, load (ETL) is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s). The ETL process became a popular concept in the 1970s and is often used in data warehousing.
Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming them into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational data store, a data mart, data lake or a data warehouse
Machine learning:
Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks
Who Should Attend!
- Any student
- Student who want to lean data engineering concepts
