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.
WHAT YOU WILL LEARN
add extensions to your dashboard such as Tableau Extensions APIbuild and customize graphs using ggplot2 in Rbuild backup and restore mechanisms in the cloudbuild heat maps and scatter plots using Rcan be leveraged to extract value from big datacombine the use of oversampling and PCA in building a classification modelcompare the differences between the descriptive and inferential statistical analysiscompare the different types of Recommendation Engines and how they can be used to solve different recommendation problemscreate an HTTP server using hapi.jscreate an R function that finds similar users and finds products they liked which would be good to recommend to the usercreate Histograms, Scatter plots, and Box plots using Python librariesdefine a portdefine the concept of storyboarding along with the prominent storyboarding templates that we can use to implement storyboardingdemonstrate how to craft visual data using Tableaudemonstrate how to create a stacked bar plotdemonstrate how to implement data exploration using Rdemonstrate how to implement different types of bar charts using PowerBIdemonstrate how we can ingest data using WaveFrontdemonstrate the steps involved in ingesting data from databases to Hadoop clusters using Sqoopdescribe blockchaindescribe how regression works by finding the best fit straight line to model the relationships in your datadescribe the aspects of data qualitydescribe the concept of serverless computing and its benefitsdescribe the Gestalt principles of visual perceptiondescribe the process involved in learning a relationship between input and output during the training phase of machine learningdescribe the various essential distributed data management frameworks used to handle big datadescribe what truncated data is and how to remove it using Azure Automationhow the four Vs should be balanced in order to implement a successful big data strategyidentify different cloud data sources availableidentify libraries that can be used in Python to implement data visualization
identify the process and approaches involved in storytelling with dataimplement correlogram and build area charts using Rimplement Dask arrays in order to manage NumPy APIsimplement data exploration using plots in Rimplement missing values and outliers using Pythonimplement point and interval estimation using Rimplement Python Luigi in order to set up data pipelinesinstall and prepare R for data explorationintegrate Spark and Tableau to manage data pipelinesLinear regressionlist and compare the various essential data ingestion tools that we can use to ingest datalist Dask task scheduling and big data collection featureslist libraries that can be used in Python to implement data visualizationload data from databases using Rorganize your dashboard by adding objects and adjusting the layoutPandas ML to explore a dataset where the samples are not evenly distributed across the target classesrecall cloud migration models from the perspective of architectural preferencesrecall the various essential decluttering steps and approaches that we can implement to eliminate cluttersrecognize how to enable data-driven decision makingrecognize the data pipeline building capabilities provided by Kafka, Spark, and PySparkrecognize the impact of implementing containerization on cloud hosting environmentsrecognize the impact of the implementing Kubernetes and Docker in the cloudrecognize the problems associated with a model that is overfitted to training data and how to mitigate the issueshare your dashboard to othersspecify volume in big data analytics and its role in the principle of the four Vsuse modules in your API using node.jsuse Pandas and Seaborn to visualize the correlated fields in a datasetuse R to import, filter, and massage data into data setsuse the scikit-learn library to build and train a LinearSVC classification model and then evaluate its performance using the available model evaluation functionswork with vectors and metrics using Python and R
IN THIS COURSE
1.Data Scientist33sUP NEXT
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