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Business Intelligence Layers Development Using Python
Rating: 3.6 out of 5(43 ratings)
4,129 students

Business Intelligence Layers Development Using Python

Use of Python within the different layers of Business Intelligence.
Created byOsama Hassan
Last updated 5/2025
English

What you'll learn

  • Business Intelligence Architecture
  • Fetching data from different data sources including files, web and Database servers
  • Data Preparation.
  • Data Visualization.
  • Extract , Transform and Load Data frames.
  • Perform remote data transformation.
  • Develop interactive charts.
  • Apply mathematical sets theory and Predictive analysis.

Course content

6 sections • 41 lectures • 13h 37m total length

  • Fetch data from No-SQL Data Source15:17
  • Course Objectives and Data Analysis Cycle8:18
  • File : CSV, Spreadsheet, Text36:20
  • Files : HTML, PDF16:55
  • Connect to Database Servers37:49
  • Database Servers Q and A18:54
  • Remote Data Access35:02
  • Remote Data Access Q and A25:29

Requirements

  • Completed course: "Foundational Python Programming For Business Analytics." or familiar with python practical use
  • Beginners may enroll but they have to start with the review lecture of python programming for beginners. in appendix..

Description

Embark on a journey into the world of data analytics and visualization with our comprehensive course, "Data Analytics and Visualization: From Sources to Insights." This course is meticulously crafted to equip you with the knowledge and skills needed to harness the power of data for informed decision-making and insightful analysis.

In Section 1, "Data Sources Layer," you'll learn how to fetch data from various sources, including No-SQL databases, files such as CSV, spreadsheets, text, HTML, and PDF, as well as connect to database servers and access remote data.

Section 2, "Data Preparation Layer - ETL," focuses on preparing data for analysis through operations on data frames, handling strings, dates, and times, and transforming data remotely using techniques such as Oracle PL SQL.

Moving on to Section 3, "Data Visualization," you'll discover how to create standard and interactive charts, visualize sets, and analyze customer behavior through visualization techniques.

Section 4, "Data Analytics," delves into the core of data analysis, covering the data analysis cycle, basics of statistics, linear regression, linear programming, and complete data analysis cases, including securities analysis.

Section 5, "Data Sharing," explores techniques for sharing data, including starting servers from the command line, configuring Jupyter Notebook servers in a LAN, securing notebook servers, and integrating HTML and external web sources into Python code.

In Section 6, "Business Intelligence Context," you'll delve into the context of business intelligence, explore Python topics relevant to BI, discuss different types of data, and extend Python scripts in Power BI, including getting data from Excel, SQL Server, and web sources.

Whether you're a beginner looking to explore the world of data analytics or an experienced professional seeking to enhance your skills, "Data Analytics and Visualization: From Sources to Insights" provides a comprehensive and practical learning experience to help you unlock the full potential of data-driven insights.

Who this course is for:

  • Bankers
  • Business Managers
  • Accountants
  • Data Analysts
  • BI Developers and Analysts
  • Financial Analysts

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