Become A Data Engineer- Bi, Python, Sql, Ssis, Etl

Transforming Business Intelligence with Python, SQL, SSIS, and ETL

What you'll 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.

Requirements

Basic Understanding of Data Concepts: Familiarity with fundamental data concepts such as data types, databases, data structures, and data manipulation principles can provide a foundation for grasping more advanced data engineering concepts.

Basic Programming Knowledge: While not mandatory, having a basic understanding of programming concepts can be beneficial. Knowledge of variables, loops, functions, and conditional statements may facilitate the learning process, especially when diving into Python programming.

Computer Literacy: Students should possess basic computer literacy skills, including familiarity with operating systems, file management, and navigating the command line or terminal. Access to a computer with a stable internet connection is necessary for accessing course materials and conducting practical exercises.

Interest in Data Engineering and Business Intelligence: An interest in data engineering, BI concepts, and the desire to delve deeper into the practical applications of Python, SQL, SSIS, and ETL processes can significantly enhance motivation and engagement throughout the course.

Optional: Prior Experience with Database Management: Some prior exposure to database management systems (DBMS), SQL querying, or data manipulation using spreadsheets may be advantageous but is not a strict requirement as these topics will be covered during the course.

Having a strong desire to learn, explore, and engage actively with the course content, exercises, and projects is crucial. The course structure may accommodate learners with varying levels of prior knowledge, but a solid understanding of fundamental data concepts and an eagerness to learn about data engineering for BI purposes will be beneficial for maximum comprehension and successful completion of the course.

Description

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.

Overview

Section 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

Section 2: Foundations of Python for Data Engineers

Lecture 7 Basics of Python programming language

Lecture 8 Introduction to NumPy for numerical computing

Lecture 9 What is Jupyter Notebook

Lecture 10 Guide to installing Jupyter Notebook Server

Lecture 11 Installing Jupyter Notebook Server on Windows

Lecture 12 Running Jupyter Notebook Server

Lecture 13 Common Jupyter Notebook Commands

Lecture 14 Jupyter Notebook Components

Lecture 15 Jupyter Notebook Dashboard

Lecture 16 Jupyter Notebook User Interface

Lecture 17 Creating a new notebook

Lecture 18 Python Expressions

Lecture 19 Python Statements

Lecture 20 Python Comments

Lecture 21 Python Data Types

Lecture 22 Casting Data Types

Lecture 23 Python Variables

Lecture 24 Python List

Lecture 25 Python Tuple

Lecture 26 Python Dictionaries

Lecture 27 Python Operators

Lecture 28 Python Conditional Statements

Lecture 29 Python Loops

Lecture 30 Python Functions

Lecture 31 Tabular Data

Lecture 32 Data manipulation and analysis using Pandas library

Lecture 33 Exploring Pandas DataFrame

Lecture 34 Manipulating a Pandas DataFrame

Lecture 35 What is data cleaning

Lecture 36 Basic data cleaning process

Lecture 37 What is data visualization

Lecture 38 Visualizing Qualitative Data

Lecture 39 Visualizing Quantitative Data

Section 3: SQL Database Management

Lecture 40 What is SQL

Lecture 41 What is TSQL

Lecture 42 What is SQL Server

Lecture 43 SQL Server Installation Requirements

Lecture 44 SQL Server Editions

Lecture 45 Download SQL Server Developer Edition

Lecture 46 SQL Server Developer Edition Installation

Lecture 47 Installing SQL Server Management Studio

Lecture 48 Connecting to SQL Server with SSMS

Lecture 49 Download and install sample database

Lecture 50 Basic database concepts

Lecture 51 Introduction to joining tables with SQL

Lecture 52 INNER JOIN

Lecture 53 LEFT Outer Join

Lecture 54 RIGHT Outer Join

Lecture 55 Introduction to filtering data with SQL

Lecture 56 Filtering Records Using Basic Equality Filters

Lecture 57 Filtering Records Using Basic Comparisons

Lecture 58 Filtering Records Using Logical Comparisons

Lecture 59 Filtering Records Using String Comparisons

Lecture 60 Filtering Records Using NULL Comparisons

Lecture 61 Introduction to sorting data with SQL

Lecture 62 Sorting by Ascending

Lecture 63 Sorting By Descending

Lecture 64 Sorting By multiple columns

Lecture 65 Introduction to aggregate functions

Lecture 66 COUNT () Aggregate Function

Lecture 67 AVG() Aggregate Function

Lecture 68 MAX() Aggregate Function

Lecture 69 MIN() Aggregate Function

Lecture 70 SUM() Aggregate Function

Lecture 71 Using Multiple Aggregate Functions

Lecture 72 Grouping Data

Lecture 73 Using Subqueries

Lecture 74 Common Table Expressions (CTEs)

Lecture 75 Using Windows Functions

Lecture 76 Using Pivot and Unpivot operations

Lecture 77 Advanced SQL queries for data manipulation and extraction

Lecture 78 Database optimization and performance tuning techniques

Section 4: SSIS (SQL Server Integration Services)

Lecture 79 Understanding SSIS and its role in ETL processes

Lecture 80 Designing and implementing ETL solutions using SSIS

Lecture 81 Handling data extraction, transformation, and loading tasks

Lecture 82 Error handling and debugging in SSIS packages

Lecture 83 Installing sample Datawarehouse Database

Lecture 84 What is Visual Studio

Lecture 85 Visual studio installation requirements

Lecture 86 Install Visual Studio

Lecture 87 Install SQL Server Data Tools - SSDT

Lecture 88 Install SSDT Designer Templates

Lecture 89 What is ETL

Lecture 90 Create a new Integration Services project

Lecture 91 Add and configure a Flat File connection manager

Lecture 92 Remapping Column Data Types

Lecture 93 Add and configure an OLE DB connection manager

Lecture 94 Add a Data Flow task to the package

Lecture 95 Add and configure the flat file source

Lecture 96 Add and configure the lookup transformations

Lecture 97 Add and configure Lookup for DateKey Transformation

Lecture 98 Add and configure the OLE DB destination

Lecture 99 Test the package

Section 5: Advanced ETL Techniques

Lecture 100 Best practices for ETL development and implementation

Lecture 101 Working with unstructured and semi-structured data

Lecture 102 Data cleansing, validation, and transformation strategies

Lecture 103 Real-life case studies and project-based learning for ETL tasks

Section 6: Capstone Project and Advanced Topics

Lecture 104 Capstone project integrating Python, SQL, SSIS, and ETL techniques

Lecture 105 Performance optimization in ETL workflows

Lecture 106 Introduction to Data Warehousing concepts

Lecture 107 Emerging trends and advanced topics in Data Engineering and BI

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.