Top Certification For IT Sector Employees And Candidate With The Help Of DS-200

Author: Skye Gray

Qualifying in this qualification is not a simple thing to attain. Candidate needs to be prepared along with necessary material and resources to complete the exam.

In the written examination segment of CCP:DS, applicants are tested on their acquaintance of essential data science subjects. Applicants must complete DS-200 to become entitled for a Data Science Essentials. Following are the topics covered in the examination.

Data Acquisition

  • Deploy a variety of acquisition methods for obtaining data, containing database integration running with APIs
  • Utilize Hadoop tools such as Flume and Sqoop
  • Utilize command line tools such curl and wget

Data Evaluation

  • Knowledge of the file types commonly used for input and output and the advantages and disadvantages of each
  • A familiarity with Hadoop SequenceFiles and serialization using Avro
  • An understanding of filtering and sampling techniques
  • Tools, utilities and techniques for evaluating data from the command line and at scale
  • Methods for working with various file formats containing binary files, XML, JSON and.csv

Data Transformation

  • Write records into a new format such AvroOutputFormat or SequenceFileOutputFormat
  • Write a custom subclass of FileOutputFormat
  • Write a Mapper using Python and invoke via Hadoop streaming
  • Write scripts to anonymize data sets
  • Join data sets
  • Invoke Unix tools to convert file formats
  • Write a script that receives records on stdin and write them to stdout
  • Write a map-only Hadoop Streaming job

Machine Learning Basics

  • Understand how to use Mappers and Reducers to create predictive models
  • Identify appropriate uses of the following: parametric/non-parametric algorithms, kernels, support vector machines, clustering, neural networks, recommender systems and dimensionality reduction
  • Understand the different kinds of machine learning, including supervised and unsupervised learning

Clustering

  • Identify appropriate uses of various models including distribution, centroid, group, density and graph
  • Classify the algorithms applicable to each model
  • Describe clustering and identify appropriate use cases
  • Explain the value and use of similarity metrics including Euclidean distance, Pearson correlation and block distance

Classification

  • Explain the steps for training a set of data in order to classify new data based on known data
  • Describe classification formulas and techniques
  • Classify the utilize cases for logistic regression, Bayes theorem

Collaborative Filtering

  • Explain the limitations and strengths of collaborative filtering techniques
  • Classify the use of item-based and user-based collaborative filtering techniques
  • Decide the metrics one should use to evaluate the accuracy of a recommender system
  • Decide the appropriate collaborative filtering implementation

Model/Feature Selection

  • Examine a scenario and determine the appropriate attributes and features to select
  • Explain the role and function of feature selection
  • Examine a scenario and determine the methods to deploy for optimal feature selection

Probability

  • Decide sample percentiles
  • Examine a scenario and determine the likelihood of a particular outcome
  • Summarize a distribution of sample numbers
  • Decide a range of items based on a sample probability density function

Visualization

  • Examine data visualization and interpret its meaning
  • Decide the most effective visualization for a given problem

Optimization

  • Classify 1st order and 2nd order optimization techniques
  • Understand optimization methods
  • Decide the sources of errors in a model
  • Decide the learning rate for a particular algorithm

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