Filter Keyword to...

Category: Computer and Information Systems Manager

Results Per Page :
Explore how we define data, it's lifecyle, the importance of privacy, and SQL and NoSQL database solutions and key data management concepts.
Explore how we define data, it's lifecyle, the importance of privacy, and SQL and NoSQL database solutions and key data management concepts.
[READ LESS]
Produced by: SkillSoft
Data engineering is the area of data science that focuses on practical applications of data collection and analysis. In this course, you will explore distributed systems, batch vs. in-memory processing, NoSQL...
[READ MORE]
Data engineering is the area of data science that focuses on practical applications of data collection and analysis. In this course, you will explore distributed systems, batch vs. in-memory processing, NoSQL uses, and the various tools available for data management/big data and the ETL process.
Data engineering is the area of data science that focuses on practical applications of data collection and analysis. In this course, you will explore distributed systems, batch vs. in-memory processing, NoSQL uses, and the various tools available for data management/big data and the ETL process.
[READ LESS]
Produced by: SkillSoft
Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.
Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.
[READ LESS]
Produced by: SkillSoft
Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy.
Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy.
[READ LESS]
Produced by: SkillSoft
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.
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.
[READ LESS]
Produced by: SkillSoft
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.
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.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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.
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.
[READ LESS]
Produced by: SkillSoft
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 data pipelines, the processes and stages involved in building them, and the technologies like Tableau and AWS that can be used.
[READ LESS]
Produced by: SkillSoft
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.
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.
[READ LESS]
Produced by: SkillSoft
Explore the differences between transaction management using NoSQL and MongoDB. Discover how to implement of change data capture in databases and NoSQL.
Explore the differences between transaction management using NoSQL and MongoDB. Discover how to implement of change data capture in databases and NoSQL.
[READ LESS]
Produced by: SkillSoft
Explore the concepts of transactions, transaction management policies, and rollbacks. Discover how to implement transaction management and rollbacks using SQL Server.
Explore the concepts of transactions, transaction management policies, and rollbacks. Discover how to implement transaction management and rollbacks using SQL Server.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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, 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.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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.
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.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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 apply essential data research techniques, including JMP measurement, and how to valuate data using descriptive and inferential methods.
[READ LESS]
Produced by: SkillSoft
Explore how different t-tests can be performed using the SciPy library to test hypotheses. How to calculate the skewness and kurtosis of data using SciPy and compute regressions using scikit-learn is also...
[READ MORE]
Explore how different t-tests can be performed using the SciPy library to test hypotheses. How to calculate the skewness and kurtosis of data using SciPy and compute regressions using scikit-learn is also covered.
Explore how different t-tests can be performed using the SciPy library to test hypotheses. How to calculate the skewness and kurtosis of data using SciPy and compute regressions using scikit-learn is also covered.
[READ LESS]
Produced by: SkillSoft
The goal of all modeling is generalizing as well as possible from a sample to the population as a whole. Explore the first step in this process, obtaining a representative sample from which meaningful...
[READ MORE]
The goal of all modeling is generalizing as well as possible from a sample to the population as a whole. Explore the first step in this process, obtaining a representative sample from which meaningful generalizable insights can be obtained.
The goal of all modeling is generalizing as well as possible from a sample to the population as a whole. Explore the first step in this process, obtaining a representative sample from which meaningful generalizable insights can be obtained.
[READ LESS]
Produced by: SkillSoft
Inferential statistics go beyond merely describing a dataset and seek to posit and prove or disprove the existence of relationships within the data. Explore hypothesis testing, which finds wide applications...
[READ MORE]
Inferential statistics go beyond merely describing a dataset and seek to posit and prove or disprove the existence of relationships within the data. Explore hypothesis testing, which finds wide applications in data science.
Inferential statistics go beyond merely describing a dataset and seek to posit and prove or disprove the existence of relationships within the data. Explore hypothesis testing, which finds wide applications in data science.
[READ LESS]
Produced by: SkillSoft
Explore the two most basic types of descriptive statistics, measures of central tendency and dispersion. Examine the most common measures of each type, as well as their strengths and weaknesses.
Explore the two most basic types of descriptive statistics, measures of central tendency and dispersion. Examine the most common measures of each type, as well as their strengths and weaknesses.
[READ LESS]
Produced by: SkillSoft
Discover how to use the NumPy, Pandas, and SciPy libraries to perform various statistical summary operations on real datasets and how to visualize your datasets in the context of these summaries using...
[READ MORE]
Discover how to use the NumPy, Pandas, and SciPy libraries to perform various statistical summary operations on real datasets and how to visualize your datasets in the context of these summaries using Matplotlib.
Discover how to use the NumPy, Pandas, and SciPy libraries to perform various statistical summary operations on real datasets and how to visualize your datasets in the context of these summaries using Matplotlib.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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.
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.
[READ LESS]
Produced by: SkillSoft
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course you will examine the organizational implications of data...
[READ MORE]
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course you will examine the organizational implications of data silos and explore how data lakes can help make data secure, discoverable, and queryable. Discover how data lakes can work with batch and streaming data.
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course you will examine the organizational implications of data silos and explore how data lakes can help make data secure, discoverable, and queryable. Discover how data lakes can work with batch and streaming data.
[READ LESS]
Produced by: SkillSoft
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course, you will discover how to configure Glue crawlers to work...
[READ MORE]
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course, you will discover how to configure Glue crawlers to work with different data stores on AWS. Examine how to visualize the data stored in the data lake with AWS QuickSight and how to perform ETL operations on the data using Glue scripts.
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course, you will discover how to configure Glue crawlers to work with different data stores on AWS. Examine how to visualize the data stored in the data lake with AWS QuickSight and how to perform ETL operations on the data using Glue scripts.
[READ LESS]
Produced by: SkillSoft
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course, you will discover how to build a data lake on the AWS...
[READ MORE]
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course, you will discover how to build a data lake on the AWS cloud by storing data in S3 buckets and indexing this data using AWS Glue. Explore how to run crawlers to automatically crawl data in S3 to generate metadata tables in Glue.
Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course, you will discover how to build a data lake on the AWS cloud by storing data in S3 buckets and indexing this data using AWS Glue. Explore how to run crawlers to automatically crawl data in S3 to generate metadata tables in Glue.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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.
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.
[READ LESS]
Produced by: SkillSoft
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...
[READ MORE]
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 exam the architecture of IoT solutions and the essential approaches of integrating data sources.
[READ LESS]
Produced by: SkillSoft