Data Analist naar Data Scientist - Deel 2 Data Wrangler

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

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.

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.

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

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.

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

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 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.

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.

Spark is an analytics engine built

Discover how to develop applications

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.

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.

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.

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

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.

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.

• 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.

• 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.

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.

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.

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

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