Final Exam: Data Scientist

  • 1 Video | 30m 32s
  • Includes Assessment
  • Earns a Badge
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.


  • add extensions to your dashboard such as Tableau Extensions API
    build and customize graphs using ggplot2 in R
    build backup and restore mechanisms in the cloud
    build heat maps and scatter plots using R
    can be leveraged to extract value from big data
    combine the use of oversampling and PCA in building a classification model
    compare the differences between the descriptive and inferential statistical analysis
    compare the different types of Recommendation Engines and how they can be used to solve different recommendation problems
    create an HTTP server using hapi.js
    create an R function that finds similar users and finds products they liked which would be good to recommend to the user
    create Histograms, Scatter plots, and Box plots using Python libraries
    define a port
    define the concept of storyboarding along with the prominent storyboarding templates that we can use to implement storyboarding
    demonstrate how to craft visual data using Tableau
    demonstrate how to create a stacked bar plot
    demonstrate how to implement data exploration using R
    demonstrate how to implement different types of bar charts using PowerBI
    demonstrate how we can ingest data using WaveFront
    demonstrate the steps involved in ingesting data from databases to Hadoop clusters using Sqoop
    describe blockchain
    describe how regression works by finding the best fit straight line to model the relationships in your data
    describe the aspects of data quality
    describe the concept of serverless computing and its benefits
    describe the Gestalt principles of visual perception
    describe the process involved in learning a relationship between input and output during the training phase of machine learning
    describe the various essential distributed data management frameworks used to handle big data
    describe what truncated data is and how to remove it using Azure Automation
    how the four Vs should be balanced in order to implement a successful big data strategy
    identify different cloud data sources available
    identify libraries that can be used in Python to implement data visualization
  • identify the process and approaches involved in storytelling with data
    implement correlogram and build area charts using R
    implement Dask arrays in order to manage NumPy APIs
    implement data exploration using plots in R
    implement missing values and outliers using Python
    implement point and interval estimation using R
    implement Python Luigi in order to set up data pipelines
    install and prepare R for data exploration
    integrate Spark and Tableau to manage data pipelines
    Linear regression
    list and compare the various essential data ingestion tools that we can use to ingest data
    list Dask task scheduling and big data collection features
    list libraries that can be used in Python to implement data visualization
    load data from databases using R
    organize your dashboard by adding objects and adjusting the layout
    Pandas ML to explore a dataset where the samples are not evenly distributed across the target classes
    recall cloud migration models from the perspective of architectural preferences
    recall the various essential decluttering steps and approaches that we can implement to eliminate clutters
    recognize how to enable data-driven decision making
    recognize the data pipeline building capabilities provided by Kafka, Spark, and PySpark
    recognize the impact of implementing containerization on cloud hosting environments
    recognize the impact of the implementing Kubernetes and Docker in the cloud
    recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue
    share your dashboard to others
    specify volume in big data analytics and its role in the principle of the four Vs
    use modules in your API using node.js
    use Pandas and Seaborn to visualize the correlated fields in a dataset
    use R to import, filter, and massage data into data sets
    use the scikit-learn library to build and train a LinearSVC classification model and then evaluate its performance using the available model evaluation functions
    work with vectors and metrics using Python and R


  • Playable
    Data Scientist


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