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