Data Analyst naar Data Scientist - Part 1 Data Analyst
As low as
duration: 39 hours |
Language: English (US) |
access duration: 365 days |
Details
Data analysts are concerned with collecting and checking data in order to process it into information. This information is then analyzed and converted into knowledge.
In the Data Analyst learning track you will learn how to use Python and Microsoft R to analyze data. In addition to these languages, you will also learn more about Hadoop and MongoDB and you will get started with Data Silos.
Result
You have the tools to get started as a Data analyst because besides Python and Microsoft R you are also able to work with Hadoop, MongoDB and Data Silos.
Note: This is part 1 of 4, part 2 is also available, parts 3 and
4 will follow.
Prerequisites
Good analytical skills and basic knowledge about statistics are a big plus!
Target audience
Data analist
Content
Data Analyst naar Data Scientist - Part 1 Data Analyst
Data Architecture Primer
Data Engineering Fundamentals
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.
Python for Data Science: Introduction to NumPy for Multi-dimentional Data
Python for Data Science: Advanced Operations with NumPy Arrays
Python for Data Science: Introduction to Pandas
Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames
R for Data Science: Data Structures
R for Data Science: Importing and Exporting Data
R for Data Science: Data Exploration
R for Data Science: Regression Methods
Discover how to apply regression
R for Data Science: Classification & Clustering
Data Science Statistics: Simple Descriptive Statistics
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.
Data Science Statistics: Common Approaches to Sampling Data
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.
Data Science Statistics: Inferential Statistics
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.
Accessing Data with Spark: An Introduction to Spark
Getting Started with Hadoop: Fundamentals & MapReduce
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
Getting Started with Hadoop: Developing a Basic MapReduce Application
Hadoop HDFS: Introduction
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.
Hadoop HDFS: Introduction to the Shell
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.
Hadoop HDFS: Working with Files
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.
Hadoop HDFS: File Permissions
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.
Data Silos, Lakes, & Streams: Introduction
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.
Data Silos, Lakes, and Streams: Data Lakes on AWS
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.
Data Silos, Lakes, & Streams: Sources, Visualizations, & ETL Operations
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.
Data Analysis Application
Discover how to perform data analysis using Anaconda Python, R, and related analytical libraries and tools.
Data Science Fundamentals for Python and MongoDB
Comparative Approaches to Using R and Python for Statistical Data Analysis
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning Library
Big Data and Hadoop: Learn by Example
Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem
Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake
Enterprise Big Data Engineering, Analytics, and Management
Statistics: Unlocking the Power of Data, Second Edition
Final Exam: Data Analyst
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.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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