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Ontwikkel je van Data Analist naar Data Scientist

€ 825,00
€ 998,25 Incl. BTW

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Duur: 134 uur |

Taal: Engels (US) |

Online toegang: 365 dagen |

In Onbeperkt Leren

Gegevens

Heb jij affiniteit met data? En heb jij reeds ervaring met het werken met data en het analyseren van data en wil jij jouw kennis en vaardigheden naar een hoger niveau tillen? Lijkt het je wat om jezelf te ontwikkelen van Data Analist naar Data Scientist? Dan is dit ontwikkelpad voor jou!

Wanneer je kiest voor dit ontwikkelpad, krijg jij:

  • toegang tot trainingen, livelabs en examens over de rollen van Data Analist, Data Wrangler, Data Ops en Data Scientist. Daarnaast krijg je toegang tot nog veel meer trainingen, proefexamens, bootcamps, e-books enzovoort.
  • begeleiding van ons Learning & Development team, samen met jou stellen we doelen, maken we een planning en monitoren we je voortgang.

Dit ontwikkelpad is ideaal om jouw STAP budget voor in te zetten!

Data Analist

Je gaat van start met de vaardigheden en kennis die je nodig hebt als Data Analist. Je leert hoe je data kan verzamelen en controleren om deze vervolgens te verwerken tot informatie. Deze informatie wordt vervolgens geanalyseerd en omgezet in kennis. Hiervoor leer je hoe je Python en Microsoft R inzet om data te analyseren. Naast deze talen leer je ook meer over Hadoop en MongoDB en ga je aan de slag met Data Silo's.

Data Wrangler

Vervolgens verken je de rol van Data Wrangler. Onderwerpen als wrangling met Python, MongoDB en het bouwen van data pipelines worden behandeld.

Data Ops

Daarna ben je klaar om de kennis en vaardigheden op te doen als Data Ops. Je leert hoe je data kunt operationaliseren en schalen, rekening houdend met compliance en governance. Je verkent hiervoor onderwerpen zoals governance, security, harnessing volume en velocity.

Data Scientist

Tot slot stoom je jezelf klaar tot junior Data Scientist, zodat je aan de hand van data slimme zakelijke beslissingen kunt nemen. Vaardigheden en kennis over het gebruik van data visualisatie, APIs, Machine Learning and Deep Learning algorithms komen aan bod.

Resultaat

Na het doorlopen van dit ontwikkelpad heb jij voldoende handvatten om slimme zakelijkse beslissingen te nemen als Data Scientist. Je hebt gedegen kennis en vaardigheden opgedaan omtrent het analyseren, wranglen, operationaliseren en visualiseren van data.

Voorkennis

Goede analytische skills en basiskennis over statistieken is aangeraden.

Doelgroep

Business analist, Data-analist

Inhoud

Ontwikkel je van Data Analist naar Data Scientist

134 uur

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.

Analyzing Data with Python

  • Practice performing data analysis tasks using Python by

  • configuring VSCode, loading data from SQLite into Pandas, grouping
  • data and using box plots. Then, test your skills by answering
  • assessment questions after using Python to calculate frequency
  • distribution, measures of center, and coefficient of
  • dispersion.

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 Analyst to Data Scientist Journey.

Data Wrangling with Pandas: Working with Series & DataFrames

Discover how to perform data transformations, data cleaning, and statistical aggregations using Pandas DataFrames.

Data Wrangling with Pandas: Visualizations and Time-Series Data

Visualize and explore data in Pandas using popular chart types like the bar graph, histogram, pie chart, and box plot. Discover how to work with time series and string data in datasets.

Data Wrangling with Pandas: Advanced Features

Discover how to perform advanced grouping, aggregations, and filtering operations on DataFrames. Working with masks and indexes, cleaning duplicated data, and assigning columns as categorical to perform operations is also covered.

Data Wrangler 4: Cleaning Data in R

Explore the essential methods for wrangling data with R.

Data Tools: Technology Landscape & Tools for Data Management

Explore the technology landscape and the tools used to implement data management. Discover how to use machine learning in data analytics and the capabilities of machine learning implementation in the cloud.

Data Tools: Machine Learning & Deep Learning in the Cloud

Explore machine learning solutions provided by AWS and Microsoft. Compare the prominent tools and frameworks that can be used to implement machine learning and deep learning.

Trifacta for Data Wrangling: Wrangling Data

Discover the essential methods for wrangling data with Trifacta.

MongoDB for Data Wrangling: Querying

To carry out data wrangling tasks you need to gather, filter, modify, and query data. Perform MongoDB actions related to data wrangling using Python with the PyMongo library.

MongoDB for Data Wrangling: Aggregation

To carry out data wrangling tasks you need to reshape, aggregate, and summarize data. Perform MongoDB actions related to data wrangling through the PyMongo library.

Getting Started with Hive: Introduction

Apache Hive is one of the most

Getting Started with Hive: Loading and Querying Data

Hive simplifies working with large

Getting Started with Hive: Viewing and Querying Complex Data

Apache Hive is one of the most popular data warehouses out in the market used for data science. It simplifies working with large datasets in files by representing them as tables. This allows them to be queried with a simple and intuitive query language. In this course you will explore how to create, load, and query Hive tables.

Getting Started with Hive: Optimizing Query Executions

Hive allows analysis of big data by

Getting Started with Hive: Optimizing Query Executions with Partitioning

In this course, you will explore the ways query executions can be optimized, including the powerful technique of partitioning datasets.

Getting Started with Hive: Bucketing & Window Functions

Explore how Hive query executions

Getting Started with Hadoop: Filtering Data Using MapReduce

Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. Extracting only the meaningful information from a dataset can be painstaking, especially if it is very large. In this course, you will examine how Hadoop's MapReduce can be used to speed up this operation.

Getting Started with Hadoop: MapReduce Applications With Combiners

Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. Hadoop enables speedy analysis of large datasets by distributing them on a cluster and processing them in parallel. Explore the use of Combiners to make MapReduce applications more efficient by minimizing data transfers.

Getting Started with Hadoop: Advanced Operations Using MapReduce

Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. In this course, explore how MapReduce can be used to extract the five most expensive vehicles in a dataset and then how to build an inverted index for the words appearing in a set of text files.

Accessing Data with Spark: Data Analysis Using the Spark DataFrame API

Apache Spark is an open-source cluster-computing framework used for data science and it has become the defacto big data framework. In this course, you will explore how to analyze real datasets using the DataFrame API methods. Discover how to optimize operations using shared variables and combine data from multiple DataFrames using joins.

Accessing Data with Spark: Data Analysis using Spark SQL

Analyze a Spark DataFrame by

Data Lake: Framework & Design Implementation

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.

Data Lake: Architectures & Data Management Principles

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.

Data Architecture - Deep Dive: Design & Implementation

Explore various types of data architecture and implementation of strategies using NoSQL, CAP theorem, and partitioning to improve performance.

Data Architecture - Deep Dive: Microservices & Serverless Computing

Explore various types of data architecture and implementation of strategies using NoSQL, CAP theorem, and partitioning to improve performance.

Data Wrangling with Python

Perform data wrangling tasks including using a Pandas DataFrame to convert multiple Excel sheets to separate JSON documents, extract a table from an HTML file, use mean substitution and convert dates within a DataFrame. Then, test your skills by answering assessment questions after using a Pandas DataFrame to convert a CSV document to a JSON document, replace missing values with a default value, split a column with a delimiter and combine two columns by concatenating text.

Final Exam: Data Wrangler

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

Deploying Data Tools: Data Science Tools

Discover the different uses of data

Delivering Dashboards: Management Patterns

Explore the concept of dashboards and the best practices that can be adopted to build effective dashboards. How to implement dashboards and visualizations using PowerBI and ELK and the concepts of leaderboard and scorecards is also covered.

Delivering Dashboards: Exploration & Analytics

Explore the role played by dashboards in data exploration and deep analytics. Examine the essential patterns of dashboard design and how to implement appropriate dashboards using Kibana, Tableau, and Qlikview.

Cloud Data Architecture: DevOps & Containerization

Discover how to implement cloud architecture for large scale applications, serverless computing, adequate storage, and analytical platforms using DevOps tools and cloud resources.

Compliance Issues and Strategies: Data Compliance

It's crucial that organizations remain compliant with their big data implementations. Examine compliance and its relationship with big data, as well as popular resources for developing compliance strategies

Implementing Governance Strategies

As organizations become more data science aware, it's critical to understand the role of governance in big data implementation. In this course you will examine governance and its relationship with big data, and how to plan and design a big data governance strategy.

Data Access & Governance Policies: Data Access Oversight and IAM

Data sensitivity and security breaches are common in news media reports. Explore how a structured data access governance framework results in reducing the likelihood of data security breaches.

Data Access & Governance Policies: Data Classification, Encryption, and Monitoring

Before data can be sufficiently protected, its sensitivity must be known. Explore how data classification determines which security measure applies to varying classes of data.

Streaming Data Architectures: An Introduction to Streaming Data

Spark is an analytics engine built

Streaming Data Architectures: Processing Streaming Data

Discover how to develop applications

Scalable Data Architectures: Introduction

Explore a theoretical foundation on the need for and the characteristics of scalable data architectures. Using data warehouses to store, process, and analyze big data is also covered.

Scalable Data Architectures: Introduction to Amazon Redshift

Using a hands-on lab approach, explore how to use Amazon Redshift to set up and configure a data warehouse on the cloud. Discover how to interact with the Redshift service using both the console and the AWS CLI.

Scalable Data Architectures: Working with Amazon Redshift & QuickSight

Explore the loading of data from an external source such as Amazon S3 into a Redshift cluster, as well as the configuration of snapshots and the resizing of clusters. Discover how to use Amazon QuickSight to visualize data.

Building Data Pipelines

Explore data pipelines and methods of processing them with and without ETL. Creating data pipelines using Apache Airflow is also covered.

Data Pipeline: Process Implementation Using Tableau & AWS

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.

Data Pipeline: Using Frameworks for Advanced Data Management

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.

Data Sources: Integration

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.

Data Sources: Implementing Edge on the Cloud

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.

Data Ops 16: Securing Big Data Streams

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.

Harnessing Data Volume & Velocity: Big Data to Smart Data

Explore the concept of smart data and the associated life cycle and benefits afforded by smart data. Frameworks and algorithms that can help transition big data to smart data are also covered.

Data Rollbacks: Transaction Rollbacks & Their Impact

Explore the concepts of transactions, transaction management policies, and rollbacks. Discover how to implement transaction management and rollbacks using SQL Server.

Data Rollbacks: Transaction Management & Rollbacks in NoSQL

Explore the differences between transaction management using NoSQL and MongoDB. Discover how to implement of change data capture in databases and NoSQL.

Implementing Data Ops with Python

Perform data ops tasks with Python including working with row subsets, creating new columns with Regex, performing joins and spreading rows. Then, test your skills by answering assessment questions after working with field subsets and computed columns, and performing set operations and binding rows.

Final Exam: Data Ops

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

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.

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: 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.

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.

Data Visualization with Python

Perform data visualization tasks with Python such as creating scatter plots, plotting linear regression, using logistic regression and creating decision tree. Then, test your skills by answering assessment questions after creating time-series graphs, resampling observations, creating histograms and using a grid pair.

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 Analyst to Data Scientist 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. 

LiveLab

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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