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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