Become a Data Engineer- BI, Python, SQL, SSIS, ETL

Become a Data Engineer- BI, Python, SQL, SSIS, ETL, available at $19.99, has an average rating of 2.5, with 107 lectures, based on 2 reviews, and has 1020 subscribers.

You will learn about Develop a solid understanding of data engineering principles within the context of Business Intelligence (BI). Master the fundamentals of Python programming for data manipulation, analysis, and visualization. Proficiently utilize SQL for database management, querying, and optimization. Comprehend the role and functionalities of SSIS (SQL Server Integration Services) in data integration and ETL processes. Design and implement ETL (Extract, Transform, Load) solutions using SSIS for efficient data processing. Implement data cleansing, validation, and transformation strategies within ETL processes. Understand data warehousing concepts and their significance in BI and analytics. Develop skills in performance optimization for ETL workflows and data processing. Analyze real-world case studies and practical projects involving ETL processes and BI tasks. Integrate Python scripts and libraries within ETL workflows to enhance data processing capabilities. Utilize SQL queries and SSIS functionalities for error handling and debugging in data pipelines. Create interactive and insightful data visualizations using Python's libraries like Matplotlib and Seaborn. Demonstrate proficiency in manipulating and preparing data for BI applications and analytics. Implement advanced techniques for data aggregation, grouping, and summarization. Design and execute a comprehensive capstone project integrating Python, SQL, SSIS, and ETL techniques. This course is ideal for individuals who are Aspiring Data Engineers: Individuals looking to specialize in data engineering with a specific focus on Business Intelligence applications, tools, and processes. or Data Analysts or Data Scientists: Professionals seeking to expand their skill set by gaining expertise in data engineering for BI, enabling them to handle data pipelines, integration, and processing efficiently. or Professionals Transitioning to Data Engineering Roles: Individuals transitioning from related fields or roles (such as software development, analytics, or IT) into data engineering roles, especially within the BI domain. or Students and Graduates: Students pursuing degrees in computer science, data science, or related fields interested in specializing in data engineering and its applications in BI. or IT Professionals and Database Administrators: Those working in IT, database administration, or related roles looking to broaden their knowledge and skill set to encompass data engineering principles for BI purposes. or Career Changers or Business Professionals: Professionals from diverse backgrounds aiming to pivot their careers into the field of data engineering and BI by acquiring the necessary technical skills and knowledge. or Individuals Seeking Career Advancement: Professionals already working in data-related roles (such as analysts or engineers) looking to enhance their career prospects by gaining expertise in data engineering specifically for Business Intelligence. It is particularly useful for Aspiring Data Engineers: Individuals looking to specialize in data engineering with a specific focus on Business Intelligence applications, tools, and processes. or Data Analysts or Data Scientists: Professionals seeking to expand their skill set by gaining expertise in data engineering for BI, enabling them to handle data pipelines, integration, and processing efficiently. or Professionals Transitioning to Data Engineering Roles: Individuals transitioning from related fields or roles (such as software development, analytics, or IT) into data engineering roles, especially within the BI domain. or Students and Graduates: Students pursuing degrees in computer science, data science, or related fields interested in specializing in data engineering and its applications in BI. or IT Professionals and Database Administrators: Those working in IT, database administration, or related roles looking to broaden their knowledge and skill set to encompass data engineering principles for BI purposes. or Career Changers or Business Professionals: Professionals from diverse backgrounds aiming to pivot their careers into the field of data engineering and BI by acquiring the necessary technical skills and knowledge. or Individuals Seeking Career Advancement: Professionals already working in data-related roles (such as analysts or engineers) looking to enhance their career prospects by gaining expertise in data engineering specifically for Business Intelligence.

Enroll now: Become a Data Engineer- BI, Python, SQL, SSIS, ETL

Summary

Title: Become a Data Engineer- BI, Python, SQL, SSIS, ETL

Price: $19.99

Average Rating: 2.5

Number of Lectures: 107

Number of Published Lectures: 107

Number of Curriculum Items: 107

Number of Published Curriculum Objects: 107

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

Develop a solid understanding of data engineering principles within the context of Business Intelligence (BI).
Master the fundamentals of Python programming for data manipulation, analysis, and visualization.
Proficiently utilize SQL for database management, querying, and optimization.
Comprehend the role and functionalities of SSIS (SQL Server Integration Services) in data integration and ETL processes.
Design and implement ETL (Extract, Transform, Load) solutions using SSIS for efficient data processing.
Implement data cleansing, validation, and transformation strategies within ETL processes.
Understand data warehousing concepts and their significance in BI and analytics.
Develop skills in performance optimization for ETL workflows and data processing.
Analyze real-world case studies and practical projects involving ETL processes and BI tasks.
Integrate Python scripts and libraries within ETL workflows to enhance data processing capabilities.
Utilize SQL queries and SSIS functionalities for error handling and debugging in data pipelines.
Create interactive and insightful data visualizations using Python's libraries like Matplotlib and Seaborn.
Demonstrate proficiency in manipulating and preparing data for BI applications and analytics.
Implement advanced techniques for data aggregation, grouping, and summarization.
Design and execute a comprehensive capstone project integrating Python, SQL, SSIS, and ETL techniques.

Who Should Attend

Aspiring Data Engineers: Individuals looking to specialize in data engineering with a specific focus on Business Intelligence applications, tools, and processes.
Data Analysts or Data Scientists: Professionals seeking to expand their skill set by gaining expertise in data engineering for BI, enabling them to handle data pipelines, integration, and processing efficiently.
Professionals Transitioning to Data Engineering Roles: Individuals transitioning from related fields or roles (such as software development, analytics, or IT) into data engineering roles, especially within the BI domain.
Students and Graduates: Students pursuing degrees in computer science, data science, or related fields interested in specializing in data engineering and its applications in BI.
IT Professionals and Database Administrators: Those working in IT, database administration, or related roles looking to broaden their knowledge and skill set to encompass data engineering principles for BI purposes.
Career Changers or Business Professionals: Professionals from diverse backgrounds aiming to pivot their careers into the field of data engineering and BI by acquiring the necessary technical skills and knowledge.
Individuals Seeking Career Advancement: Professionals already working in data-related roles (such as analysts or engineers) looking to enhance their career prospects by gaining expertise in data engineering specifically for Business Intelligence.

Target Audiences

Aspiring Data Engineers: Individuals looking to specialize in data engineering with a specific focus on Business Intelligence applications, tools, and processes.
Data Analysts or Data Scientists: Professionals seeking to expand their skill set by gaining expertise in data engineering for BI, enabling them to handle data pipelines, integration, and processing efficiently.
Professionals Transitioning to Data Engineering Roles: Individuals transitioning from related fields or roles (such as software development, analytics, or IT) into data engineering roles, especially within the BI domain.
Students and Graduates: Students pursuing degrees in computer science, data science, or related fields interested in specializing in data engineering and its applications in BI.
IT Professionals and Database Administrators: Those working in IT, database administration, or related roles looking to broaden their knowledge and skill set to encompass data engineering principles for BI purposes.
Career Changers or Business Professionals: Professionals from diverse backgrounds aiming to pivot their careers into the field of data engineering and BI by acquiring the necessary technical skills and knowledge.
Individuals Seeking Career Advancement: Professionals already working in data-related roles (such as analysts or engineers) looking to enhance their career prospects by gaining expertise in data engineering specifically for Business Intelligence.

This course aims to equip individuals with the essential skills required to become proficient Data Engineers specializing in Business Intelligence. Participants will gain a comprehensive understanding of Python programming, SQL database management, SSIS (SQL Server Integration Services), and the fundamentals of Extract, Transform, Load (ETL) processes. Through a combination of theoretical learning and hands-on practical exercises, students will develop the expertise needed to excel in the field of Data Engineering, particularly in BI-related tasks.

Skills Students Will Learn:

Throughout this course, participants will gain proficiency in the following key areas:

Python Programming: Learn the fundamentals of Python programming and its application in data manipulation, analysis, and visualization, using libraries such as Pandas and NumPy.

SQL Database Management: Master SQL for database querying, management, optimization, and advanced data manipulation techniques.

SSIS (SQL Server Integration Services): Gain a comprehensive understanding of SSIS and its role in designing and implementing ETL solutions for data integration.

ETL Processes: Learn the principles and best practices of Extract, Transform, Load (ETL) processes, including data extraction, transformation, and loading into target systems.

Course Requirements:

This course is suitable for individuals with a basic understanding of data concepts and a strong interest in pursuing a career in data engineering and business intelligence. Prerequisites for this course include:

Familiarity with Data Concepts: Basic understanding of data types, databases, and data manipulation concepts is recommended.

Basic Programming Knowledge: Some familiarity with programming concepts would be beneficial, but not mandatory.

Computer Literacy: Access to a computer with a stable internet connection and the ability to install necessary software (Python, SQL tools, etc.) for hands-on exercises.

Who Is the Course Designed For?

This course is ideal for:

Aspiring Data Engineers seeking to specialize in Business Intelligence.

Data Analysts or Data Scientists aiming to expand their skill set into the realm of data engineering for BI applications.

Professionals transitioning to careers in the field of data engineering with a specific focus on BI tools and processes.

Designed to be accessible and comprehensive, this course provides a solid foundation for individuals looking to embark on or advance within a career in data engineering, particularly within the Business Intelligence domain.

Join us on this learning journey as we delve into the core concepts and practical applications essential for becoming proficient in Data Engineering for Business Intelligence.

Course Curriculum

Chapter 1: Introduction to Data Engineering and BI

Lecture 1: Introduction

Lecture 2: What is data engineering

Lecture 3: What is BI

Lecture 4: Overview of Data Engineering and its significance in BI

Lecture 5: Understanding the role of a Data Engineer in modern enterprises

Lecture 6: Introduction to BI concepts and tools

Chapter 2: Foundations of Python for Data Engineers

Lecture 1: Basics of Python programming language

Lecture 2: Introduction to NumPy for numerical computing

Lecture 3: What is Jupyter Notebook

Lecture 4: Guide to installing Jupyter Notebook Server

Lecture 5: Installing Jupyter Notebook Server on Windows

Lecture 6: Running Jupyter Notebook Server

Lecture 7: Common Jupyter Notebook Commands

Lecture 8: Jupyter Notebook Components

Lecture 9: Jupyter Notebook Dashboard

Lecture 10: Jupyter Notebook User Interface

Lecture 11: Creating a new notebook

Lecture 12: Python Expressions

Lecture 13: Python Statements

Lecture 14: Python Comments

Lecture 15: Python Data Types

Lecture 16: Casting Data Types

Lecture 17: Python Variables

Lecture 18: Python List

Lecture 19: Python Tuple

Lecture 20: Python Dictionaries

Lecture 21: Python Operators

Lecture 22: Python Conditional Statements

Lecture 23: Python Loops

Lecture 24: Python Functions

Lecture 25: Tabular Data

Lecture 26: Data manipulation and analysis using Pandas library

Lecture 27: Exploring Pandas DataFrame

Lecture 28: Manipulating a Pandas DataFrame

Lecture 29: What is data cleaning

Lecture 30: Basic data cleaning process

Lecture 31: What is data visualization

Lecture 32: Visualizing Qualitative Data

Lecture 33: Visualizing Quantitative Data

Chapter 3: SQL Database Management

Lecture 1: What is SQL

Lecture 2: What is TSQL

Lecture 3: What is SQL Server

Lecture 4: SQL Server Installation Requirements

Lecture 5: SQL Server Editions

Lecture 6: Download SQL Server Developer Edition

Lecture 7: SQL Server Developer Edition Installation

Lecture 8: Installing SQL Server Management Studio

Lecture 9: Connecting to SQL Server with SSMS

Lecture 10: Download and install sample database

Lecture 11: Basic database concepts

Lecture 12: Introduction to joining tables with SQL

Lecture 13: INNER JOIN

Lecture 14: LEFT Outer Join

Lecture 15: RIGHT Outer Join

Lecture 16: Introduction to filtering data with SQL

Lecture 17: Filtering Records Using Basic Equality Filters

Lecture 18: Filtering Records Using Basic Comparisons

Lecture 19: Filtering Records Using Logical Comparisons

Lecture 20: Filtering Records Using String Comparisons

Lecture 21: Filtering Records Using NULL Comparisons

Lecture 22: Introduction to sorting data with SQL

Lecture 23: Sorting by Ascending

Lecture 24: Sorting By Descending

Lecture 25: Sorting By multiple columns

Lecture 26: Introduction to aggregate functions

Lecture 27: COUNT () Aggregate Function

Lecture 28: AVG() Aggregate Function

Lecture 29: MAX() Aggregate Function

Lecture 30: MIN() Aggregate Function

Lecture 31: SUM() Aggregate Function

Lecture 32: Using Multiple Aggregate Functions

Lecture 33: Grouping Data

Lecture 34: Using Subqueries

Lecture 35: Common Table Expressions (CTEs)

Lecture 36: Using Windows Functions

Lecture 37: Using Pivot and Unpivot operations

Lecture 38: Advanced SQL queries for data manipulation and extraction

Lecture 39: Database optimization and performance tuning techniques

Chapter 4: SSIS (SQL Server Integration Services)

Lecture 1: Understanding SSIS and its role in ETL processes

Lecture 2: Designing and implementing ETL solutions using SSIS

Lecture 3: Handling data extraction, transformation, and loading tasks

Lecture 4: Error handling and debugging in SSIS packages

Lecture 5: Installing sample Datawarehouse Database

Lecture 6: What is Visual Studio

Lecture 7: Visual studio installation requirements

Lecture 8: Install Visual Studio

Lecture 9: Install SQL Server Data Tools – SSDT

Lecture 10: Install SSDT Designer Templates

Lecture 11: What is ETL

Lecture 12: Create a new Integration Services project

Lecture 13: Add and configure a Flat File connection manager

Lecture 14: Remapping Column Data Types

Lecture 15: Add and configure an OLE DB connection manager

Lecture 16: Add a Data Flow task to the package

Lecture 17: Add and configure the flat file source

Lecture 18: Add and configure the lookup transformations

Instructors

Bluelime Learning Solutions
Making Learning Simple

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