Showing posts with label data management. Show all posts
Showing posts with label data management. Show all posts

Monday 19 February 2024

Web Applications and Command-Line Tools for Data Engineering

 


What you'll learn

Construct Python Microservices with FastAPI

Build a Command-Line Tool in Python using Click

Compare multiple ways to set up and use a Jupyter notebook

Join Free: Web Applications and Command-Line Tools for Data Engineering

There are 4 modules in this course

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.

Database Engineer Capstone

 


What you'll learn

Build a MySQL database solution.

Deploy level-up ideas to enhance the scope of a database project.

Join Free: Database Engineer Capstone

There are 4 modules in this course

In this course you’ll complete a capstone project in which you’ll create a database and client for Little Lemon restaurant.

To complete this course, you will need database engineering experience.  

The Capstone project enables you to demonstrate multiple skills from the Certificate by solving an authentic real-world problem. Each module includes a brief recap of, and links to, content that you have covered in previous courses in this program. 

In this course, you will demonstrate your new skillset by designing and composing a database solution, combining all the skills and technologies you've learned throughout this program to solve the problem at hand. 

By the end of this course, you’ll have proven your ability to:

-Set up a database project,
-Add sales reports,
-Create a table booking system,
-Work with data analytics and visualization,
-And create a database client.

You’ll also demonstrate your ability with the following tools and software:

-Git,
-MySQL Workbench,
-Tableau,
-And Python.

Thursday 15 February 2024

Regression Analysis: Simplify Complex Data Relationships

 


What you'll learn

Investigate relationships in datasets

Identify regression model assumptions 

Perform linear and logistic regression using Python

Practice model evaluation and interpretation

Join Free: Regression Analysis: Simplify Complex Data Relationships

There are 6 modules in this course

This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.  

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. 

By the end of this course, you will:

-Explore the use of predictive models to describe variable relationships, with an emphasis on correlation
-Determine how multiple regression builds upon simple linear regression at every step of the modeling process
-Run and interpret one-way and two-way ANOVA tests
-Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models

Thursday 25 January 2024

Introduction to Probability and Data with R

 


Build your subject-matter expertise

This course is part of the Data Analysis with R Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Introduction to Probability and Data with R

There are 8 modules in this course

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

Extract, Transform and Load Data in Power BI

 


What you'll learn

How to set up a data source and explain and configure storage modes in Power BI.

How to prepare for data modeling by cleaning and transforming data.

How to use profiling tools to identify data anomalies.

How to reference queries and dataflows and use the Advanced Editor to modify code. 

Join Free: Extract, Transform and Load Data in Power BI

There are 4 modules in this course

This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offers a good starting point for a career in data analysis using Microsoft Power BI.

In this course, you will learn the process of Extract, Transform and Load or ETL. You will identify how to collect data from and configure multiple sources in Power BI and prepare and clean data using Power Query. You’ll also have the opportunity to inspect and analyze ingested data to ensure data integrity. 

After completing this course, you’ll be able to: 

Identify, explain and configure multiple data sources in Power BI  
Clean and transform data using Power Query  
Inspect and analyze ingested data to ensure data integrity

This is also a great way to prepare for the Microsoft PL-300 exam. By passing the PL-300 exam, you’ll earn the Microsoft Power BI Data Analyst certification.

Wednesday 24 January 2024

Azure Data Lake Storage Gen2 and Data Streaming Solution

 


What you'll learn

How to use Azure Data Lake Storage to make processing Big Data analytical solutions more efficient. 

How to set up a stream analytics job to stream data and manage a running job

How to describe the concepts of event processing and streaming data and how this applies to Azure Stream Analytics 

How to use Advanced Threat Protection to proactively monitor your system and describe the various ways to upload data to Data Lake Storage Gen 2

Join Free: Azure Data Lake Storage Gen2 and Data Streaming Solution

There are 4 modules in this course

In this course, you will see how Azure Data Lake Storage can make processing Big Data analytical solutions more efficient and how easy it is to set up. You will also explore how it fits into common architectures, as well as the different methods of uploading the data to the data store. You will examine the myriad of security features that will ensure your data is secure. Learn the concepts of event processing and streaming data and how this applies to Azure Stream Analytics. You will then set up a stream analytics job to stream data, and learn how to manage and monitor a running job.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the ninth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

Prepare for DP-203: Data Engineering on Microsoft Azure Exam

 


What you'll learn

How to refresh and test your knowledge of the skills mapped to all the main topics covered in the DP-203 exam.

How to demonstrate proficiency in the skills measured in Exam DP-203: Data Engineering on Microsoft Azure

How to outline the key points covered in the Microsoft Data Engineer Associate Specialization

How to describe best practices for preparing for the Exam DP-203: Data Engineering on Microsoft Azure

Join Free: Prepare for DP-203: Data Engineering on Microsoft Azure Exam

There are 3 modules in this course

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the DP-203 Microsoft Azure Data Fundamentals certification exam. 

You will refresh your knowledge of how to use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. You will test your knowledge in a practice exam​ mapped to all the main topics covered in the DP-203 exam, ensuring you’re well prepared for certification success. 

You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the DP-203 proctored exam. By the end of this course, you will be ready to sign-up for and take the DP-203 exam.​

This is the last course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

Microsoft Azure Databricks for Data Engineering

 


What you'll learn

How to work with large amounts of data from multiple sources in different raw formats

How to create production workloads on Azure Databricks with Azure Data Factory

How to build and query a Delta Lake 

How to perform data transformations in DataFrame. How to understand the architecture of an Azure Databricks Spark Cluster and Spark Jobs 

Join Free: Microsoft Azure Databricks for Data Engineering

There are 9 modules in this course

In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

You will discover the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. You will come to understand the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark. You will also be introduced to the architecture of an Azure Databricks Spark Cluster and Spark Jobs. You will work with large amounts of data from multiple sources in different raw formats.  you will learn how Azure Databricks supports day-to-day data-handling functions, such as reads, writes, and queries.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the eighth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

Data Integration with Microsoft Azure Data Factory

 


What you'll learn

How to create and manage data pipelines in the cloud 

How to integrate data at scale with Azure Synapse Pipeline and Azure Data Factory

Join Free: Data Integration with Microsoft Azure Data Factory

There are 8 modules in this course

In this course, you will learn how to create and manage data pipelines in the cloud using Azure Data Factory.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services. It is ideal for anyone interested in preparing for the DP-203: Data Engineering on Microsoft Azure exam (beta). 

This is the third course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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