Data Analyst to Data Scientist - Part 1 E-learning
Order this unique Elearning Data Analyst to Data Scientist - Part 1 online, 1 year 24/7 access to rich interactive videos, speech, progress monitoring through reports and tests per chapter to directly test the knowledge.
Data Analist naar Data Scientist - Deel 1 Data Analist
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Explore how we define data, it
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.
NumPy is a Python library that works
NumPy is a Python library that works
Discover how to work with series and
Explore different ways to iterate
Explore the use of the common data
Discover how to use R to import and
Explore data in R using the dplyr
Discover how to apply regression
Examine how to apply classification
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.
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.
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.
Explore the basics of Apache Spark,
Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. In this course, you will explore the theory behind big data analysis using Hadoop and how MapReduce enables the parallel processing of large datasets distributed on a cluster of machines.
Getting Started with Hadoop: Developing a Basic MapReduce Application
HDFS is the file system which enables the parallel processing of big data in distributed cluster. Explore the concepts of analyzing large datasets and explore how Hadoop and HDFS make this process very efficient.
Discover how to set up a Hadoop Cluster on the cloud and explore the bundled web apps - the YARN Cluster Manager app and the HDFS NameNode UI. Then use the hadoop fs and hdfs dfs shells to browse the Hadoop file system.
Explore the Hadoop file system using the HDFS dfs shell and perform basic file and directory-level operations. Transfer files between a local file system and HDFS and explore ways to create and delete files on HDFS.
HDFS is the file system which enables the parallel processing of big data in distributed cluster. When managing a data warehouse, not all users should be given free reign over all the datasets. Explore how file permissions can be viewed and configured in HDFS. The NameNode UI is used to monitor and explore HDFS.
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 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 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.
Discover how to perform data analysis using Anaconda Python, R, and related analytical libraries and tools.
Helping you build the foundational data science skills necessary to work with and better understand complex data science algorithms, this book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience.
Providing insights on relevant topics, such as inference, factor analysis, and linear regression, this book is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data.
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.
Containing the latest trends in big data and Hadoop, this learn-by-doing resource explains how big Big Data is and why everybody is trying to implement it into their IT projects.
Emphasizing best practices to ensure coherent, efficient development, this book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation.
Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues.
Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this book presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation.
Driven by real data and real applications, this book focuses on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data.
Final Exam: Data Analyst will test your knowledge and application of the topics presented throughout the Data Analyst track of the Skillsoft Aspire Data Science Journey.
And all for a fraction of the cost of a classroom training
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