€ 299,00
Incl. BTW
Data Analist naar Data Scientist - Deel 2 Data Wrangler
Python
33 uur
Engels (US)
Data Analist naar Data Scientist - Deel 2 Data Wrangler
Data Analist naar Data Scientist - Deel 2 Data Wrangler
Python
33 uur
Engels (US)
Vanaf € 361,79 € 299,00
Data Analist naar Data Scientist - Deel 2 Data Wrangler
Vanaf € 361,79 € 299,00
Productinformatie
Dit is deel 2 van het leerpad Data Analist naar Data Scientist.
In dit deel ligt de focus op de rol Data Wrangler. Onderwerpen als wrangling met Python, MongoDB en het bouwen van data pipelines worden behandeld.
Je vindt hier verschillende cursussen die je voorbereiden om aan de slag te kunnen als Data Wrangler. Daarnaast is er een livelab beschikbaar om te oefenen. Je sluit dit deel af met een examen.
Inhoud van de training
Data Analist naar Data Scientist - Deel 2 Data Wrangler
33 uur
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.
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.
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.
Kenmerken
Docent inbegrepen
Bereidt voor op officieel examen
Engels (US)
33 uur
Python
365 dagen online toegang
HBO
Meer informatie
| Doelgroep | Data-analist |
|---|---|
| Voorkennis | Je wordt verondersteld de kennis en vaardigheden te beheersen die in deel 1 (Data Analist) van dit leerpad worden behandeld. |
| Resultaat | Na het doorlopen van dit deel beschik je over de kennis en vaardigheden om aan de slag te gaan als Data Wrangler. Let op: Dit is deel 2 van 4 delen. |
