Explore the use of containers in deploying data science solutions using Docker with R, Python, Jupyter, and Anaconda.
Discover the steps in applying DevOps to data science, including integration, packings, deployment, monitoring, and logging.
Explore deploying data models into production through serialization, packaging, deployment, and rollback.
To carry out DevOps for data science, you need to extend the ideas of DevOps to be compatible with the processes of data science and machine learning. Explore the concepts behind integrating data and DevOps.
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.
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.
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.
A key component to wrangling data is the data lake framework. In this Skillsoft Aspire course, you will discover how to implement data lakes for real-time data management. Explore data ingestion, data processing, and data life-cycle management using AWS and other open-source ecosystem products.
For effective data wrangling, you need an architecture that will enable you to meet your goals. In this Skillsoft Aspire course, you will explore the concept of Serverless, Lambda architecture and process implementation using Serverless and Lambda architecture. We will also explore the various types of data architecture, data risks and the essential data discovery processes.
Discover how to apply statistical algorithms like PDF, CDF, binomial distribution, and interval estimation for data research. How to implement visualizations to graphically represent the outcomes of data research is also covered.
For effective data wrangling, you need an architecture that will enable you to meet your goals. In this Skillsoft Aspire course, you will explore various types of data architecture and implementation of strategies using NoSQL, CAP theorem, and partitioning to improve performance.
A key component to wrangling data is the data lake framework. In this Skiillsoft Aspire course, you will discover how to design and implement data lakes in the cloud and on-premises using standard reference architectures and patterns that can help identify the proper data architecture.
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…
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…
Explore the application of key mathematical topics related to linear algebra with the Python programming language.
In order for an organization to be data science aware, it must evolve and become data driven. In this course, you will examine the meaning of a data driven organization and explore analytic maturity, data quality, missing data, duplicate data, truncated data, and data provenance.
To master data science, you must learn the techniques around data research. In this course you will discover how to apply essential data research techniques, including JMP measurement, and how to valuate data using descriptive and inferential methods.
To master data science, you must learn the techniques around data research. In this course you will discover how to use data exploration techniques to derive different data dimensions and derive value from the data. How to practically implement data exploration using R, Python, linear algebra, and plots is also covered.
To master data science it is important to take raw data and turn that into insights. In this course you will learn to apply and implement various essential data correction techniques, transformation rules, deductive correction techniques, and predictive modelling using critical data analytical approaches.
Examine the different uses of data science tools and the overall platform, as well as the benefits and challenges of deployment.
The four Vs of big data and data science are a popular paradigm used to extract the meaning and value from massive datasets. In this course, you discover the four Vs (volume, variety, velocity, and veracity), their purpose and uses, and how to extract value using the four Vs.
With the popularatity of data science, there has been an increase in the volume of tools available. In this course you will discover the different uses of data science tools and the benefits and challenges in deploying them.
While understanding data analysis key for data science, applying data analysis with different languages and applications is important for any data scientist. In this Skillsoft Aspire course, you will discover how to perform data analysis using Anaconda Python, R, and related analytical libraries and tools.
Spark is an analytics engine built on Hadoop that can be used for working with big data, data science and processing batch and streaming data. In this course you will explore the fundamentals of working with streams using Spark.
Spark is an analytics engine built on Hadoop that can be used for working with big data, datascience and processing batch and streaming data. In this course you will discover how to develop applications in Spark to work with streaming data and explore the different ways to process streams and generate an output.
Data science requires you to gather, filter, transform, and explore data sets. Examine data wrangling in R by working with the dplyr library, data manipulation using the tidyr library, and data extraction with the readr library.
There are a variety of frequently used programming techniques you can apply to get the most out of the R language. Explore a variety of techniques in R in order to increase the quality and performance of your R programs.
R includes a number of support libraries to aid in program creation. Examine a range of domain-specific tools in R to perform tasks such as rendering, documentation, referencing, string manipulation, and library management.
One of the most important tasks in any programming language or development environment is debugging. Discover ways you can debug R code and improve the resilience of your R programs through defensive programming.
The final step in the data science pipeline is to communicate the results or findings. Explore communication and visualization concepts needed by data scientists.
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. Examine clustering, errors, and validation in machine learning.
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. Explore the conceptual elements of various machine learning techniques.
To effectively use the software and programming tools available for data scientists, you must understand underlying concepts. Discover how to use estimates and measures in data analysis.
There are many software and programming tools for data scientists. Before applying these tools effectively, you must understand underlying concepts. Explore data analysis concepts for effectively employing software and programming tools.
Data integration is the last step in the data wrangling process where data is put into its useable and structured format for analysis. Explore examples of practical tools and techniques for data integration.
Once data is transformed into a useable format, the next step is to carry out preliminary data exploration on the data. Explore examples of practical tools and techniques for data exploration.
After filtering data, the next step is to transform it into a usable format. Explore examples of practical tools and techniques for data transformation.
Once data is gathered for data science, it is often in an unstructured or raw format and must be filtered for content and validity. Explore examples of practical tools and techniques for data filtering.
In data science, you need to gather data, extracting, parsing, and scraping data from various sources, both internal and external as a critical first part in the data science pipeline. Explore examples of practical tools for data gathering.
Data science differentiates itself from statistics and application programming by using what it needs from a variety of disciplines. Explore what it means to be a data scientist and what sets data science apart from other disciplines.