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Data Science for non-Data Scientists
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Data Analytics has become critical for many business decision makers. With new, powerful and affordable tools available on the market, more and more business people are finding they are empowered to do something intelligent with their data. As a result, many businesses already have a number of valuable super users. Their hybrid role bridges the gap between the high level, technical Data Scientist and the more business-oriented data discovery analyst.
In this unique 4-day Data Science for non-Data Scientists training you gain the expertise and skills to perform some of the analytics functions that have been traditionally reserved for Data Scientists.
The demand for Data Scientists continues to grow beyond supply. Companies of all kinds are feeling the acute shortage of trained Data Scientists today. Empowering business people to do much of the data analysis themselves allow the highly trained Data Scientists to focus on the things that require their specific expertise.
As a business user/non-Data Scientist you can succeed in this critical position, but only if you are empowered with the right tools and processes. You need to be able to analyze the data without having extensive technical knowledge of the underlying IT applications and infrastructure. Self-service data preparation and advanced analytical tools will provide you with the ability to perform the critical task of combining Big Data with your organization's issues and opportunities. The growing set of powerful self-service tools available on the market, make this possible.
Is your primary job function outside the field of statistics and analytics? Do you have a line-of-business role, are you a business analyst, data analyst or a member of the Business Intelligence (BI) or IT team? Do you want to practice the self-service data preparation capabilities of SAS and the ease of use of advanced analytics in exploring and visualizing data? Then this is the training for you!
We have developed a unique 4-day curriculum to specifically address the training needs of non-Data Scientists/business users. If you are willing to learn new methods and use new tools, regardless of whether or not you currently use SAS software, then join this 5-day training for the non-Data Scientist.
DATA SCIENCE FOR NON-DATA SCIENTISTS – PROGRAM
I. BIG DATA AND ANALYTICS
Data Science - introduction
- The era of abundance
- Big Data explained
- Data analysis overview
Statistics - introduction
- Examining data distributions
- Obtaining and interpreting sample statistics
- Examining data distributions graphically
- Using exploratory data analysis
- Producing correlations
- Fitting a simple linear regression model
II. PREPARING FOR ANALYSIS
Getting started with Visual Analytics
- Exploring SAS Visual Analytics concepts
- Using the SAS Visual Analytics home page
- Discussing the course environment and scenario
Using Visual Analytics Explorer
- Examining SAS Visual Analytics Explorer
- Selecting data and defining data item properties
- Creating visualizations
- Enhancing visualizations with analytics
- Interacting with visualizations and explorations
Examining Visual Data Builder
- Exploring SAS Visual Data Builder
- Creating simple queries
Creating complex queries in Visual Data Builder
Importing data using SAS Visual Data Builder
- Creating calculated columns and filtering data
- creating advanced queries
Advanced topics for Visual Data Builder
- Accessing user-defined formats
Using the Explorer and Designer to load data
- Using the Explorer and Designer to import data
- Using the Explorer and Designer to create calculated columns
III. ANALYTICAL DATA VISUALIZATION AND MODELING DATA
- Understanding segmentation
- Using cluster analysis
Models with continuous targets
- Managing projects and models
- Using linear regression models
- Using generalized linear models
Models with categorical targets
Using logistic regression
- Using decision trees
Model comparison and assessment
- Scoring models
You will be able to put your knowledge into practice with real-world, scenario-based examples.
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