DS-200: Knowing About Data Science Essentials

DS-200 Exams: Data Science Essentials exam sections that validate the applicants in a very challenging way are termed as, Data Acquisition, Data Evaluation, Data Transformation, Machine Learning Basics, Clustering, Classification, Collaborative Filtering, Model/Feature and Optimization.

DS-200 PDF Kits: Data Science Essentials exam main topics elaborations have been given here so that there is not confusion of what to learn and what to not.

Data Acquisition is the first topic, the objectives of which are to deploy a variety of acquisition techniques with API, Use command line tools such as Sqoop and Flume. DS-200 Preparation Material can be done by Apache Sqoop, Cloudera’s blogs on Apache Sqoop, Aaron Kimball on Sqoop, Apache Flume, Cloudera's blogs on Apache Flume, Cloudera's blogs on data collection, HDFS File System Shell Guide, Hadoop: The Definitive Guide, 3rd Edition: Chapter 15 and Hadoop In Practice: Chapter 2.

DS-200 Study Guides Data Transformation consists of how to write a map-only Hadoop Streaming job, script that receives records on stdin and write them to stdout, Invoke Unix tools to convert file formats, Join data sets, scripts to anonymize data sets, Mapper using Python and invoke via Hadoop streaming, custom subclass of FileOutputFormat and records into a new format such AvroOutputFormat or SequenceFileOutputFormat.

Machine Learning Basics consists of the DS-200 Online Educations: Data Science Essentials knowledge of the understanding on how to use Mappers and Reducers to create predictive models, different kinds of machine and unsupervised learning parametric/non-parametric algorithms, support vector machines, kernels, neural dimensionality reduction, and recommender systems.

This section can be learned by Apache Mahout, Apache Mahout wiki, Cloudera's blogs on Apache Mahout, Hadoop In Practice: Chapter 9, Hadoop: The Definitive Guide, 3rd Edition: Chapters 16, Algorithms of the Intelligent Web: Chapter 7 and also A Programmers Guide to Data Mining.

DS-200 Questions Answers Clustering comprises of the task to define clustering and identify appropriate use cases, uses of various models value and use of similarity metrics including Pearson correlation, Euclidean distance, and block distance and algorithms applicable to each model.

Classification includes the knowledge of, steps for DS-200 Training Guides a set of data in order to identify new data based on known data, use cases for logistic regression, Bayes theorem and classification techniques and formula.

Collaborative Filtering covers the knowledge of how to identify the use of user-based and item-based collaborative filtering techniques describe the limitations and strengths of collaborative filtering techniques, determine the appropriate collaborative filtering implementation, metrics one should use to evaluate the accuracy of a recommender system, preparation suggestions include Recommendation engines with Apache Mahout, Programming Collective Intelligence: Chapter 2, Algorithms of the Intelligent Web: Chapter 3 and Mahout In Action: Part 1.

In the DS-200 E-Books : Data Science Essentials topic of Model/Feature Selection the candidates have to describe the role and function of feature selection, Analyze a scenario and determine the appropriate features and attributes to select and methods to deploy for optimal feature selection. Probability is the next section that covers the likelihood of a particular outcome, Determine DS-200 Sample Percentiles, range of items based on a sample probability density function and Summarize a distribution of sample numbers.

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