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

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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