Programming Business Intelligence Layers Using Python

Programming Business Intelligence Layers Using Python, available at $44.99, has an average rating of 3.05, with 41 lectures, based on 42 reviews, and has 4129 subscribers.

You will learn about 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. This course is ideal for individuals who are Bankers or Business Managers or Accountants or Data Analysts or BI Developers and Analysts or Financial Analysts It is particularly useful for Bankers or Business Managers or Accountants or Data Analysts or BI Developers and Analysts or Financial Analysts.

Enroll now: Programming Business Intelligence Layers Using Python

Summary

Title: Programming Business Intelligence Layers Using Python

Price: $44.99

Average Rating: 3.05

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Bankers

Business Managers

Accountants

Data Analysts

BI Developers and Analysts

Financial Analysts

Target Audiences

Bankers

Business Managers

Accountants

Data Analysts

BI Developers and Analysts

Financial Analysts

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.

Course Curriculum

Chapter 1: Data Sources Layer

Lecture 1: Fetch data from No-SQL Data Source

Lecture 2: Course Objectives and Data Analysis Cycle

Lecture 3: File : CSV, Spreadsheet, Text

Lecture 4: Files : HTML, PDF

Lecture 5: Connect to Database Servers

Lecture 6: Database Servers Q and A

Lecture 7: Remote Data Access

Lecture 8: Remote Data Access Q and A

Chapter 2: Data Preparation Layer – ETL

Lecture 1: Data Frames Operations

Lecture 2: String, Date and Time

Lecture 3: Transform Data Remotely

Lecture 4: Oracle PL SQL

Lecture 5: Transform Data Remotely Q&A

Chapter 3: Data Visualization

Lecture 1: Creating standard charts

Lecture 2: Interactive Charts.

Lecture 3: Sets Visualization and customers analysis

Lecture 4: Data Visualization Q&A I

Lecture 5: Data Visualization Q&A II

Chapter 4: Data Analytics

Lecture 1: Data Analysis Cycle

Lecture 2: Basics of statistics

Lecture 3: Statistics. Review and Discussion

Lecture 4: Linear Regression

Lecture 5: Linear Regression. Review and Discussion

Lecture 6: Linear Regression. Discussion Part (2)

Lecture 7: Linear Programming.

Lecture 8: Complete data analysis case: Securities

Lecture 9: Data Analysis Case. Review and Discussion

Chapter 5: Data Sharing

Lecture 1: Starting Server from command line

Lecture 2: Configure jupyter notebook server in LAN

Lecture 3: Accessing server from other computers within LAN.

Lecture 4: Securing notebook server

Lecture 5: Using HTML in python code

Lecture 6: Display external web sources in notebook

Chapter 6: Business Intelligence Context

Lecture 1: Business Intelligence Context

Lecture 2: Python Topics For BI

Lecture 3: Types of Data

Lecture 4: Extend Python script in Power BI

Lecture 5: Power BI get data from Excel

Lecture 6: Power BI connect to SQL Server

Lecture 7: Power BI get data from web sources

Lecture 8: Python Review For Beginners

Instructors

Osama Hassan
Computer Programmer | Fujitsu Certified System Analyst

Rating Distribution

1 stars: 4 votes

2 stars: 4 votes

3 stars: 2 votes

4 stars: 4 votes

5 stars: 28 votes

Frequently Asked Questions

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