Final Exam: Data Scientist

WHAT YOU WILL LEARN

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  Load data from databases using r
  

  implement dask arrays in order to manage numpy apis
  

  list dask task scheduling and big data collection features
  

  demonstrate the steps involved in ingesting data from databases to hadoop clusters using sqoop
  

  list and compare the various essential data ingestion tools that we can use to ingest data
  

  demonstrate how we can ingest data using wavefront
  

  describe the various essential distributed data management frameworks used to handle big data
  

  define the concept of storyboarding along with the prominent storyboarding templates that we can use to implement storyboarding
  

  compare the different types of recommendation engines and how they can be used to solve different recommendation problems
  

  identify different cloud data sources available
  

  create an r function that finds similar users and finds products they liked which would be good to recommend to the user
  

  build heat maps and scatter plots using r
  

  describe the gestalt principles of visual perception
  

  pandas ml to explore a dataset where the samples are not evenly distributed across the target classes
  

  describe how regression works by finding the best fit straight line to model the relationships in your data
  

  describe the process involved in learning a relationship between input and output during the training phase of machine learning
  

  use pandas and seaborn to visualize the correlated fields in a dataset
  

  combine the use of oversampling and pca in building a classification model
  

  recognize how to enable data-driven decision making
  

  can be leveraged to extract value from big data
  

  implement python luigi in order to set up data pipelines
  

  organize your dashboard by adding objects and adjusting the layout
  

  identify libraries that can be used in python to implement data visualization
  

  share your dashboard to others
  

  implement point and interval estimation using r
  

  create an http server using hapi.js
  

  compare the differences between the descriptive and inferential statistical analysis
  

  list libraries that can be used in python to implement data visualization
  

  describe the concept of serverless computing and its benefits
  

  describe what truncated data is and how to remove it using azure automation
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  recognize the impact of implementing containerization on cloud hosting environments
  

  demonstrate how to craft visual data using tableau
  

  recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue
  

  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
  

  recall cloud migration models from the perspective of architectural preferences
  

  demonstrate how to create a stacked bar plot
  

  describe the aspects of data quality
  

  demonstrate how to implement different types of bar charts using powerbi
  

  create histograms, scatter plots, and box plots using python libraries
  

  define a port
  

  recognize the data pipeline building capabilities provided by kafka, spark, and pyspark
  

  build and customize graphs using ggplot2 in r
  

  add extensions to your dashboard such as tableau extensions api
  

  implement data exploration using plots in r
  

  recall the various essential decluttering steps and approaches that we can implement to eliminate clutters
  

  build backup and restore mechanisms in the cloud
  

  describe blockchain
  

  recognize the impact of the implementing kubernetes and docker in the cloud
  

  demonstrate how to implement data exploration using r
  

  implement correlogram and build area charts using r
  

  use r to import, filter, and massage data into data sets
  

  linear regression
  

  use modules in your api using node.js
  

  how the four vs should be balanced in order to implement a successful big data strategy
  

  install and prepare r for data exploration
  

  specify volume in big data analytics and its role in the principle of the four vs
  

  integrate spark and tableau to manage data pipelines
  

  implement missing values and outliers using python
  

  identify the process and approaches involved in storytelling with data