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Data Analist naar Data Scientist - Deel 4 Data Scientist


€ 299,00
€ 361,79 Incl. BTW

1 x Data Analist naar Data Scientist - Deel 4 Data Scientist   +
€ 299,00
€ 361,79 Incl. BTW

€ 299,00
€ 361,79 Incl. BTW

Bestellen namens een bedrijf?

Duur: 38 uur |

Taal: Engels (US) |

Online toegang: 365 dagen |

In Onbeperkt Leren


Dit is deel 4 van het leerpad Data Analist naar Data Scientist.

In dit deel wordt gefocust op de vaardigheden en kennis die je nodig hebt als Data Scientist. Vaardigheden en kennis over het gebruik van data visualisatie, APIs, Machine Learning and Deep Learning algorithms komen aan bod.

Je vindt hier verschillende cursussen die je voorbereiden om aan de slag te kunnen als Data Scientist. Daarnaast is er een livelab beschikbaar om te oefenen. Je sluit dit deel af met een examen.


Na het afronden van dit onderdeel beschik je over kennis en vaardigheden die je nodig hebt in het werk als Data Scientist.


Je wordt verondersteld de kennis en vaardigheden te beheersen die in deel 1 (Data Analist), deel 2 (Data Wrangler) en deel 3 (Data Ops) van dit leerpad worden behandeld.


Data Analist naar Data Scientist - Deel 4 Data Scientist

38 uur

Balancing the Four Vs of Data: The Four Vs of Data

The four Vs of big data are a popular paradigm used to extract the meaning and value from massive datasets. Discover the four Vs, their purpose and uses, and how to extract value using the four Vs.

Data Science 2: Data Driven Organizations

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.

Raw Data to Insights: Data Ingestion & Statistical Analysis

To master data science it is important to take raw data and turn that into insights. In this course you will explore the concept of statistical analysis and implement data ingestion using various technologies including NiFi, Sqoop, and Wavefront.

Raw Data to Insights: Data Management & Decision Making

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.

Tableau Desktop: Real Time Dashboards

To become a data science expert, you must master the art of data visualization. In this course you will explore how to create and use real time dashboards with Tableau

Storytelling with Data: Introduction

Explore the concept of storytelling with data, the processes involved in storytelling and interpreting data contexts. We will also explore the prominent types of analysis, visualizations, and graphic tools that we can use for storytelling.

Storytelling with Data: Tableau & PowerBI

Explore how to select the most effective visuals for storytelling, eliminating clutters, and the best practices for story design. We will also learn to work with Tableau and PowerBI to facilitate storytelling with data.

Python for Data Science: Basic Data Visualization Using Seaborn

Seaborn is a data visualization

Python for Data Science: Advanced Data Visualization Using Seaborn

Explore how to analyze continuous

Data Science Statistics: Using Python to Compute & Visualize Statistics

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.

Advanced Visualizations & Dashboards: Visualization Using Python

Explore approaches to building and implementing visualizations, as well as plotting and graphing using Python libraries like Matplotlib, ggplot, bokeh, and Pygal.

R for Data Science: Data Visualization

Explore how to use R to create plots

and charts of data.

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

Classification, regression, and clustering are some of the most commonly used machine learning techniques and there are various algorithms available for these tasks. Explore their application in Pandas ML.

Advanced Visualizations & Dashboards: Visualization Using R

Discover how to build advanced charts using Python and Jupyter Notebook. Explore R and ggplot2 visualization capabilities and how to build charts and graphs with them.

Powering Recommendation Engines: Recommendation Engines

Explore how Recommendation Engines can be created and used to provide recommendations for products and content.

Data Insights, Anomalies, & Verification: Handling Anomalies

Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy.

Data Science Statisitcs: Applied Inferential Statistics

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.

Data Science 9: Data Research Techniques

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.

Data Science 10: Data Research Exploration Techniques

  • 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.

Data Scientist 14: Data Research Statistical Approaches

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.

Machine & Deep Learning Algorithms: Introduction

Examine the fundamentals of machine learning and how Pandas ML can be used to build ML models. The workings of Support Vector Machines to perform classification of data is also covered.

Machine & Deep Learning Algorithms: Regression & Clustering

Explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models.

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

The imbalanced-learn library that integrates with Pandas ML offers several techniques to address the imbalance in datasets used for classification. Explore oversampling, undersampling, and a combination of these techniques.

Creating Data APIs Using Node.js

Explore how to create RESTful OAuth APIs using Node.js.

Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R

Bridging the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better, this book will give you the confidence and skills needed when developing all the major machine learning models.

Data Structures and Algorithms Implementation Through C

Including source code, solved examples, and a practical approach, this book is especially designed for beginners and explains all basics and concepts about data structure.

Data Visualization and Statistical Literacy for Open and Big Data

Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development, this book highlights methodological developments in the way that data analytics is both learned and taught.

Data Visualization, Volume II: Uncovering the Hidden Pattern in Data Using Basic and New Quality Tools

Providing a collection of visuals and graphical tools, this book focuses on data visualization and information visualization tools—two major categories of data visualization.

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.

Processing Big Data with Azure HDInsight: Building Real-World Big Data Systems on Azure HDInsight Using the Hadoop Ecosystem

As most Hadoop and Big Data projects are written in either Java, Scala, or Python, this book minimizes the effort to learn another language and is written from the perspective of a .NET developer.

An Introduction to SAS Visual Analytics: How to Explore Numbers, Design Reports, and Gain Insight into Your Data

Focusing on the version of SAS Visual Analytics on SAS 9.4, this thorough guide will show you how to make sense of your complex data with the goal of leading you to smarter, data-driven decisions without having to write a single line of code – unless you want to.

Pro Tableau: A Step-by-Step Guide

Along with demonstrations and illustrations, this book will help you make sense of data quickly and effectively, make you look at data differently and more imaginatively, and help those familiar with Tableau software chart their journey to a visualization expert.

Jumpstart Tableau: A Step-By-Step Guide to Better Data Visualization

Covering the basic reporting and analysis functions that most BI users perform in their day-to-day work, this practical book simplifies the use of Tableau software functionality for novice users so that they can create powerful data visualizations easily and quickly.

Tableau Your Data! Fast and Easy Visual Analysis with Tableau Software, Second Edition

Showing you how to build dynamic, best of breed visualizations using the Tableau Software toolset, this comprehensive guide covers the core feature set for data analytics, and provides clear, step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual.

Scalable Big Data Architecture: A Practitioner's Guide to Choosing Relevant Big Data Architecture

Covering real-world, concrete industry use cases, this book is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a big data project and which tools to integrate into that pattern.

Cosmos DB for MongoDB Developers: Migrating to Azure Cosmos DB and Using the MongoDB API

For MongoDB developers who wish to learn Azure Cosmos DB, this book will guide you in identifying the why’s and how’s that you can employ in your applications and help you in achieving extraordinary success.

REST API Development with Node.js: Manage and Understand the Full Capabilities of Successful REST Development, Second Edition

REST API development is a hot topic in the programming world, but not many resources exist for developers to really understand how you can leverage the advantages. This book will enable you to manage and understand the full capabilities of successful REST development.

Practical API Architecture and Development with Azure and AWS: Design and Implementation of APIs for the Cloud

Introducing both business and technical requirements and necessities of API architecture and development, this book includes fundamental guidelines to start API development and implementation with minimal viable standards.

Handbook of Approximation Algorithms and Metaheuristics: Contemporary and Emerging Applications, Volume 2, Second Edition

Through contributions from leading experts, this book provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics.

Final Exam: Data Scientist

Final Exam: Data Scientist will test your knowledge and application of the topics presented throughout the Data Scientist track of the Skillsoft Aspire Data Science Journey.

Opties bij cursus

Wij bieden, naast de training, in sommige gevallen ook diverse extra leermiddelen aan. Wanneer u zich gaat voorbereiden op een officieel examen dan raden wij aan om ook de extra leermiddelen te gebruiken die beschikbaar zijn bij deze training. Het kan voorkomen dat bij sommige cursussen alleen een examentraining en/of LiveLab beschikbaar is.

Examentraining (proefexamens)

In aanvulling op deze training kunt u een speciale examentraining aanschaffen. De examentraining bevat verschillende proefexamens die het echte examen dicht benaderen. Zowel qua vorm als qua inhoud. Dit is de ultieme manier om te testen of u klaar bent voor het examen. 


Als extra mogelijkheid bij deze training kunt u een LiveLab toevoegen. U voert de opdrachten uit op de echte hardware en/of software die van toepassing zijn op uw Lab. De LiveLabs worden volledig door ons gehost in de cloud. U heeft zelf dus alleen een browser nodig om gebruik te maken van de LiveLabs. In de LiveLab omgeving vindt u de opdrachten waarmee u direct kunt starten. De labomgevingen bestaan uit complete netwerken met bijvoorbeeld clients, servers, routers etc. Dit is de ultieme manier om uitgebreide praktijkervaring op te doen.


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