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The stores of data relevant to our organizations, customers, operations, and goals have never accumulated at a faster pace or to a larger volume. Likewise, the need for intelligent data analysis has never been greater. Vast reserves of value hidden within huge and sophisticated data sets. It can be a challenge to find that value – but if we can tease out the insights and answers lurking within our information, they can be translated into a host of opportunities and advantages. With the right skills, only your own creativity limits how you can leverage your stores of data for better decisions, analytics, and prediction.
Fortunately, today's data science methods are more practical and accessible than ever. The open-source R environment provides a straightforward yet incredibly powerful toolbox for performing useful predictive modeling and deep analysis. This hands-on machine learning course advances your data analysis skills into the realm of real-world data science. If you have a working familiarity with R, our three-day class equips you to go back to work with real-world predictive modeling and basic machine learning techniques. Led by expert data scientists, you will work in R to lay your data science foundation and learn techniques that allow you to leverage your data in sophisticated, powerful new ways.
- Upcoming Dates and Locations
All Live Online times are listed in Eastern Time Guaranteed To Run
- Course Outline
Part 1: Overview of Data Science
- Data Science as a quantitative discipline
- How to define Data Science scopes
- The many faces of Data Science: Data Mining, Data Analysis, Data Analytics, Machine Learning, Predictive Modeling, Statistical Learning, Mathematical Modeling. What are these all about?
- Data Mining as a data exploration process
- Machine Learning: supervised vs. unsupervised
- Machine Learning vs. Predictive Analytics
- Big Data Analytics: what is it and why it's important
- Overview of a Data Mining process cycle
- Understanding business needs and identifying new business opportunities
- Formulating a business problem and associated requirements
- Defining key quantitative metrics to measure success and evaluating business benefits
- Translating business requirements into technical requirements and documentation
- Formulating data models based on business and technical requirements
- Identifying a set of quantitative models based on technical requirements and metrics of success
- Running the models and evaluating results
- Selecting the best model
- Deploying the model
Part 2: The Data Foundation
- Data sources
- Types of data
- Structured vs. unstructured data
- Static data vs. real-time data
- Types of data attributes: numerical vs. categorical
- Role of time factor and time trends in data analysis
- Working with missing values
- Main causes of missing data
- Understanding the importance of missing information
- Types of missing information
- Restoring missing values
- Imputing missing values and selecting imputation techniques
- Understanding and evaluating potential consequences of manipulating records with missing values
- Working with outliers
- Defining quantitative criteria for outlier detection in 1D cases
- Understanding role of outliers in model building
- Deciding on outlier removal
- Defining outlier detection metrics in multi-dimensional space
- Working with duplicate records
- Defining duplicates
- Understanding sources of duplicates
- Deciding on duplicate removal
Part 3: Sampling and Hypothesis Testing
- Why sampling may be important for Machine Learning
- Sampling techniques and sample bias
- Statistical hypothesis
- Z-score, t-score and F statistic
- Implementation of hypothesis testing for model evaluation analysis
Part 4: Machine Learning Fundamentals
- What is Machine Learning?
- Supervised vs. unsupervised learning
- Overview of supervised Machine Learning
- Regression models
- Classification models
- Overview of unsupervised Machine Learning
- Clustering methods
- Principal component analysis and dimension reduction
- Association rules
- Overview of major steps in building and testing quantitative models
- Criteria for model selection
- How to prepare a training set
- Criteria for selecting model attributes/predictors
- Working with collinear variables
- Addressing imbalance problem
- Dealing with over-fitting; bias-variance tradeoff
- Validation and cross-validation
Part 5: Building a Linear Regression Model with R.
- Univariate regression vs. multiple regression
- Mathematical foundation of linear regression overview: least square method vs. maximum likelihood method
- Model assumptions
- Working with continuous attributes
- Dealing with collinear variable
- Model subset selection:
- Forward stepwise selection
- Backward selection
- Shrinkage methods: ridge regression and Lasso
- Dimension reduction
- Information criteria
- Automating model selection procedure
- Model parameter evaluation, R squared vs. adjusted R squared
- Validating the model
- Working with categorical variables
- Considering input variable interactions
Part 6: Example of building a Classification Model with R
- Dealing with imbalanced training sets
- Understanding confusion matrix
- Evaluating binary classifiers using ROC / AUC
Part 7: Example of Cluster Analysis with R
- Overview of cluster analysis mathematical foundation
- K-means clustering method
- Algorithm overview
- Convergence criteria
- How to determine the number of clusters
Part 8: Dimension Reduction techniques with R
- What is dimension reduction?
- The practical goals of dimension reduction implementation
- Principal component analysis vs. singular value decomposition
- How many components to choose
Part 9: Class Conclusion
- What was not covered in the class
- Big Data Analytics – the future of machine learning: main tools and concepts
- Data Science as a quantitative discipline
- Who should attend
Intermediate level data analysts interested in expanding their data mining processes. We emphasize Data Foundation and Machine Learning concepts. All exercises are performed in R.
This machine learning course is for individuals intermediate data analysis skills and basic knowledge of descriptive statistics. Any experience with R is also beneficial.
Technical requirements: Installed R and some R packages. Installation of RStudio is helpful, but not required.