Data Analyst naar Data Scientist - Part 1 Data Analyst

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duration: 39 hours |

Language: English (US) |

access duration: 365 days |


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.


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.


Good analytical skills and basic knowledge about statistics are a big plus!

Target audience

Data analist


Data Analyst naar Data Scientist - Part 1 Data Analyst

39 hours

Data Architecture Primer

Explore how we define data, it

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

NumPy is a Python library that works

Python for Data Science: Advanced Operations with NumPy Arrays

NumPy is a Python library that works

Python for Data Science: Introduction to Pandas

Discover how to work with series and

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

Explore different ways to iterate

R for Data Science: Data Structures

Explore the use of the common data

R for Data Science: Importing and Exporting Data

Discover how to use R to import and

R for Data Science: Data Exploration

Explore data in R using the dplyr

R for Data Science: Regression Methods

Discover how to apply regression

R for Data Science: Classification & Clustering

Examine how to apply classification

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

Explore the basics of Apache 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

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.

Comparative Approaches to Using R and Python for Statistical Data Analysis

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.

Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning Library

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.

Big Data and Hadoop: Learn by Example

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.

Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem

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.

Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake

Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues.

Enterprise Big Data Engineering, Analytics, and Management

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.

Statistics: Unlocking the Power of Data, Second Edition

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

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 immediatelyThe 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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