Data Science and Big Data Analytics
This course provides practical foundation level training that enables immediate and effective participation in Big Data and other analytics projects. It includes an introduction to Big Data and the data analytics lifecycle to address business challenges that leverage Big Data.
Tyto autorizované kurzy jsou dostupné pouze v anglickém jazyce, proto i popis školení není přeložen.
Školení dodává autorizovaný distributor DNS a.s.
The course provides grounding in basic and advanced analytic methods and an introduction to Big Data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to understand how these methods and tools may be applied to real world business challenges by a practicing data scientist.
The course takes an "open", or technology-neutral approach and includes a final lab which addresses a big data analytics challenge by applying the concepts taught in the course in the context of the data analytics lifecycle.
The course prepares the student for the Dell EMC Proven™ Professional Data Scientist Associate (EMCDSA) certification exam.
Cíle kurzu
- Upon successful completion of this course, participants should be able to:
- Immediately participate as a data science team member
- Work with large data sets and generate insights
- Build predictive and classification models
- Manage a data analytics project through the entire lifecycle
Osnova kurzu
1
- Business value from Big Data
- Data scientist
- Data analytics lifecycle overview
- Discovery phase
- Data preparation phase
- Model planning phase
- Model building phase
- Communicate results phase
- Operationalize phase
- Introduction to the R programming language
- Analyzing and exploring data
- Statistics for model building and evaluation
- Introduction to advanced analytics—theory and methods. It includes an introduction to Big Data and the data analytics lifecycle to address business challenges that leverage Big Data.
- K-means clustering
- Association rules
- Linear regression
- Logistic regression
- Text analysis
- Naïve Bayes
- Decision trees
- Time series analysis
- Introduction to advanced analytics—technology and tools
- Hadoop ecosystem
- In-database analytics SQL essentials
- Advanced SQL and MADlib
- Preparing to operationalize
- Preparing project presentations
- Data visualization techniques
Požadavky
To complete this course successfully and gain the maximum benefits from it, a student should have the following knowledge and skill sets:
A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course
Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open source statistical tool and programming
Experience with SQL
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