Data Engineer Data Scientist Power Bi Python ETL SSIS
Organizations often grapple with the challenge of aggregating data from diverse sources in various formats and consolidating it into one or more data repositories. Usually, the target destination differs from the source, requiring data transformation, shaping, or cleansing before loading.
Extract, Transform, Load (ETL) is a data pipeline designed to gather data from disparate sources, apply business rules for transformation, and deposit it into a designated data store.
SQL Server Integration Services (SSIS) emerge as a potent Business Intelligence Tool, particularly tailored for SQL Server Databases. Integrated with SQL Server Data Tools (SSDT), it augments Visual Studio with Business Intelligence Templates to craft Integration projects.
SSIS functionalities include:
- Data Integration
- Data Transformation
- Resolution of intricate Business challenges
- Data warehouse updates
- Data cleansing
- Data mining
- SQL Server object and data management
- Data extraction from diverse sources
- Data loading into single or multiple destinations
Power BI emerges as a robust business analytics solution. It facilitates data visualization and insights dissemination across the organization and embedding capabilities within applications or websites. It enables connectivity with myriad data sources and facilitates the creation of dynamic dashboards and reports.
This beginner's guide to Power BI familiarizes users with the platform, covering areas like connecting to and transforming web-based data sources and publishing and sharing reports and visuals on the Power BI service.
Data science involves the comprehensive study of data, encompassing methodologies for recording, storing, and analyzing data to extract valuable insights. It underpins various domains and serves as a cornerstone for informed decision-making and business enhancement.
Python, a modern object-oriented programming language, is a versatile tool in data science endeavors. Renowned for its ease of learning and extensive capabilities, Python's high-level nature makes it conducive to human comprehension. It is widely used in data science applications.
What You Will Learn:
- Setting up the environment for Jupyter Notebook exploration
- Importing Python Libraries
- Working with Tabular data and Pandas DataFrame
- Exploring Pandas Series
- Manipulating Pandas DataFrame
- Data cleaning and visualization techniques
Who this course is for:
- Data science beginners
- Data engineering beginners
- Novice data analysts
- Beginner data engineers
- Those keen on exploring Pandas Series and DataFrame manipulation
- Individuals interested in data cleaning and visualization techniques