Data Analyst naar Data Scientist - Part 2 Data Wrangler training

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duration: 33 hours |

Language: English (US) |

access duration: 365 days |

Details

This is part 2 of the learning path Data Analyst to Data Scientist.

This part focuses on the Data Wrangler role. Topics such as wrangling with Python, Mongo and building data pipelines are covered.

You will find different courses that prepare you to get started as a Data Wrangler. In addition, a livelab is available for practice. You conclude this part with an exam.

Result

After completing this part you have the knowledge and skills to get started as a Data Wrangler.


Note: This is part 2 of 4

Prerequisites

You are supposed to master the knowledge and skills that are covered in part 1 (Data Analyst) of this learning path.

Target audience

Data analist

Content

Data Analyst naar Data Scientist - Part 2 Data Wrangler

33 hours

Data Wrangling with Pandas: Working with Series & DataFrames

Discover how to perform data transformations, data cleaning, and statistical aggregations using Pandas DataFrames.

Data Wrangling with Pandas: Visualizations and Time-Series Data

Visualize and explore data in Pandas using popular chart types like the bar graph, histogram, pie chart, and box plot. Discover how to work with time series and string data in datasets.

Delivering Dashboards: Exploration & Analytics

Explore the role played by dashboards in data exploration and deep analytics. Examine the essential patterns of dashboard design and how to implement appropriate dashboards using Kibana, Tableau, and Qlikview.

Cloud Data Architecture: DevOps & Containerization

Discover how to implement cloud architecture for large scale applications, serverless computing, adequate storage, and analytical platforms using DevOps tools and cloud resources.

Cloud Data Architecture: Data Management & Adoption Frameworks

Explore how to implement containers and data management on popular cloud platforms like AWS and GCP. Planning big data solutions, disaster recovery, and backup and restore in the cloud are also covered.

Compliance Issues and Strategies: Data Compliance

It's crucial that organizations remain compliant with their big data implementations. Examine compliance and its relationship with big data, as well as popular resources for developing compliance strategies

Implementing Governance Strategies

As organizations become more data science aware, it's critical to understand the role of governance in big data implementation. In this course you will examine governance and its relationship with big data, and how to plan and design a big data governance strategy.

Data Access & Governance Policies: Data Access Oversight and IAM

Data sensitivity and security breaches are common in news media reports. Explore how a structured data access governance framework results in reducing the likelihood of data security breaches.

Data Access & Governance Policies: Data Classification, Encryption, and Monitoring

Before data can be sufficiently protected, its sensitivity must be known. Explore how data classification determines which security measure applies to varying classes of data.

Streaming Data Architectures: An Introduction to Streaming Data

Spark is an analytics engine built

Streaming Data Architectures: Processing Streaming Data

Discover how to develop applications

Scalable Data Architectures: Introduction

Explore a theoretical foundation on the need for and the characteristics of scalable data architectures. Using data warehouses to store, process, and analyze big data is also covered.

Scalable Data Architectures: Introduction to Amazon Redshift

Using a hands-on lab approach, explore how to use Amazon Redshift to set up and configure a data warehouse on the cloud. Discover how to interact with the Redshift service using both the console and the AWS CLI.

Scalable Data Architectures: Working with Amazon Redshift & QuickSight

Explore the loading of data from an external source such as Amazon S3 into a Redshift cluster, as well as the configuration of snapshots and the resizing of clusters. Discover how to use Amazon QuickSight to visualize data.

Building Data Pipelines

Explore data pipelines and methods of processing them with and without ETL. Creating data pipelines using Apache Airflow is also covered.

Data Pipeline: Process Implementation Using Tableau & AWS

Explore the concept of data pipelines, the processes and stages involved in building them, and the technologies like Tableau and AWS that can be used.

Data Pipeline: Using Frameworks for Advanced Data Management

Discover how to implement data pipelines using Python Luigi, integrate Spark, and Tableau to manage data pipelines, use Dask arrays, and build data pipeline visualization with Python.

Data Sources: Integration

To become proficient in data science, you have to understand edge computing. This is where data is processed near the source or at the edge of the network while in a typical cloud environment, data processing happens in a centralized data storage location. In this course you will exam the architecture of IoT solutions and the essential approaches of integrating data sources.

Data Sources: Implementing Edge on the Cloud

To become proficient in data science, you have to understand edge computing. This is where data is processed near the source or at the edge of the network while in a typical cloud environment, data processing happens in a centralized data storage location. In this course you will explore the implementation of IoT on prominent cloud platforms like AWS and GCP. Discover how to work with IoT Device Simulator and generate data streams using MQTT.

Data Ops 16: Securing Big Data Streams

Examine the security risks related to modern data capture and processing methods such as streaming analytics, the techniques and tools employed to mitigate security risks, and best practices related to securing big data.

Harnessing Data Volume & Velocity: Big Data to Smart Data

Explore the concept of smart data and the associated life cycle and benefits afforded by smart data. Frameworks and algorithms that can help transition big data to smart data are also covered.

Data Rollbacks: Transaction Rollbacks & Their Impact

Explore the concepts of transactions, transaction management policies, and rollbacks. Discover how to implement transaction management and rollbacks using SQL Server.

Data Rollbacks: Transaction Management & Rollbacks in NoSQL

Explore the differences between transaction management using NoSQL and MongoDB. Discover how to implement of change data capture in databases and NoSQL.

EU General Data Protection Regulation (GDPR): An Implementation and Compliance Guide, Second Edition

A clear and comprehensive guide to this new data protection law, this book explains the Regulation and sets out the obligations of data processors and controllers in terms you can understand.

The Psychology of Information Security: Resolving Conflicts Between Security Compliance and Human Behaviour

Providing methods and techniques to engage stakeholders and encourage buy-in, this insightful book explains the importance of careful risk management and how to align a security program with wider business objectives.

Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning Library

A tutorial on the Apache Spark platform written by an expert engineer and trainer, this book will give you the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications.

Pro Spark Streaming: The Zen of Real-Time Analytics Using Apache Spark

Introducing use cases in each chapter from a specific industry, and using publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation, this book walks you through end-to-end real-time application development using real-world applications, data, and code.

Information Security Governance: Framework and Toolset for CISOs and Decision Makers

Presenting tools and examples allowing the various managers to take on their roles, this book presents a framework to model the main activities of information security management and governance.

Network and Data Security for Non-Engineers

Presenting the tools, establishing persistent presence, and examining the use of sites as testbeds to determine successful variations of software that elude detection, this book explains network and data security by analyzing the Anthem breach step-by-step, and how hackers gain entry, place hidden software, download information, and hide the evidence of their entry.

Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake

Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues.

Processing Big Data with Azure HDInsight: Building Real-World Big Data Systems on Azure HDInsight Using the Hadoop Ecosystem

As most Hadoop and Big Data projects are written in either Java, Scala, or Python, this book minimizes the effort to learn another language and is written from the perspective of a .NET developer.

Statistical Data Cleaning with Applications in R

Bringing together a wide range of techniques for cleaning textual, numeric or categorical data, this comprehensive book examines technical data cleaning methods relating to data representation and data structure.

Final Exam: Data Wrangler

Final Exam: Data Analyst will test your knowledge and application of the topics presented throughout the Data Wrangler track of the Skillsoft Aspire Data Science Journey.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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