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.
build and run the application and confirm the output using HDFS from both the command line and the web application
compare and contrast SQL and NoSQL database solutions
configure a JDBC connection on Glue to the Redshift cluster
configure and view permissions for individual files and directories using the getfacl and chmod commands
configure HDFS using the hdfs-site.xml file and identify the properties which can be set from it
crawl data stored in a DynamoDB table
create and configure a Hadoop cluster on the Google Cloud Platform using its Cloud Dataproc service
create and configure simple graphs with lines and markers using the Matplotlib data visualization library
create and load data into an RDD
Create data frames in R
create matrices in R
create vectors in R
define linear regression
define the contents of a DataFrame using the SQLContext
define the inter-quartile range of a dataset and enumerate its properties
Define the mean of a dataset and enumerate its properties
delete a Google Cloud Dataproc cluster and all of its associated resources
deploy DynamoDB in the Amazon Web Services cloud
describe and apply the different techniques involved in handling datasets where some information is missing
describe NoSQL Stores and how they are used
describe the concept of hierarchical index or multi-index and why can be useful
describe the ETL process and different tools available
describe the options available when iterating over 1-dimensional and multi-dimensional arrays
draw the shape of a Gaussian distribution and enumerate its defining properties
edit individual cells and entire rows and columns in a Pandas DataFrame
execute the application and verify that the filtering has worked correctly; examine the job and the output files using the YARN Cluster Manager and HDFS NameNode web UIs
explain the concept of hierarchical index or multi-index and why can be useful
export the contents of a DataFrame into files of various formats
export the contents of a DataFrame into files of various formats
identify different tools available for data management
identify the various GCP services used by Dataproc when provisioning a cluster
import and export data in R
initialize a Spark DataFrame from the contents of an RDD
install Pandas and create a Pandas Series
list the six phases of the data lifecycle
load data into a Redshift cluster from S3 buckets
read data from an Excel spreadsheet
read data from files and write data to files using the Python Pandas library
recall how Apache Zookeeper enables the HDFS NameNode and YARN ResourceManager to run in high-availability mode
recall the steps involved in building a MapReduce application and the specific workings of the Map phase in processing each row of data in the input file
recognize and deal with missing data in R
recognize the challenges involved in processing big data and the options available to address them such as vertical and horizontal scaling
retrieve specific parts of an array using row and column indices
run ETL scripts using Glue
run the application and examine the outputs generated to get the word frequencies in the input text document
set up a JDBC connection on Glue to the Redshift cluster
specify the configurations of the MapReduce applications in the Driver program and the project's pom.xml file
standardize a distribution to express its values as z-scores and use Pandas to generate a correlation and covariance matrix for your dataset
transfer files from your local file system to HDFS using the copyFromLocal command
use fancy indexing with arrays using an index mask
use NumPy to compute statistics such as the mean and median on your data
use NumPy to compute the correlation and covariance of two distributions and visualize their relationship with scatterplots
use the dplyr library to load data frames
use the get and getmerge functions to retrieve one or multiple files from HDFS
use the ggplot2 library to visualize data using R
use the NumPy library to manipulate arrays and the Pandas library to load and analyze a dataset
using the independent t-test and with a related sample using a paired t-test using the SciPy library
using the mutate method
work with the YARN Cluster Manager and HDFS NameNode web applications that come packaged with Hadoop