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Course curriculum / Syllabus
Exploratory Data Analysis with R
- Use are for querying loading, and manipulating data
- Raw data cleaning for modeling
- Using Principal Component Analysis in the reduction of dimensions
- Using user-defined packages to extend R
Facilitating good analytical thinking with data visualization
- Data set characteristics investigation through visualization
- Use boxplots, histograms and density plots to make charts for data distribution
- Identifying outliers in data
Working with Unstructured and Large Data Sets
Mining unstructured data for business applications
- Unstructured data Preprocessing to prepare deeper analysis
- Making a corpus of documents using term-document matrix
Managing additional complexities of Big Data
- Examining the MapReduce and Hadoop architectures
- Integrating R and Hadoop with RHadoop
Predicting Outcomes with Regression Techniques
Future values estimation through logistic and linear regression
- Relationship Modeling between several input variables and an output variable
- Interpretation of coefficients of categorical and continuous data
Regression techniques for dealing with Big Data
- Overcoming issues of volume with RHadoop
- Creating regression modules for RHadoop
Categorizing Data with Classification Techniques
Automating the labeling of new data items
- Predicting target values using Decision Trees
- Model building for future predictions from existing data
- Combination of tree predictors and random forests in RHadoop
Assessing model performance
- Visualizing model performance with a ROC curve
- Evaluating classifiers with confusion matrices
Patterns Detection in Complex Data using Link Analysis and Clustering
Identification of unknown groupings within a data set
- Customer market segmentation using the K-Means algorithm
- What are appropriate distance measures?
- Tree-like clusters building with the help of hierarchical clustering
- Text documents and tweets clustering to aid understanding
Discovering connections with Link Analysis
- Using Social Network Analysis to capture important connections
- Usage of social networks results in marketing
Usage of Transaction Data in order to get the results regarding Association and Recommendations Rules
Building and evaluating association rules
- Knowledge about customer preferences in transaction data to enhance customer experience
- Support, confidence and lift calculation to distinguish “good” rules from “bad” rules
- Differentiating actionable, trivial and inexplicable rules
- Searching large data sets for rules with RHadoop
Constructing recommendation engines
- Cross-selling, up-selling and substitution as motivations
- Leveraging recommendations based on collaborative filtering
Implementing Analytics within Your Organization
Expanding analytic capabilities
- Use manageable steps for Big Data Analytics
- Integrating analytics into current business processes
- Spark, MLib and Mahout review for machine learning
Dissemination and Big Data policies
- Examining ethical questions of privacy in Big Data
- Disseminating results to different types of stakeholders
data analytics certification FAQ’s:
1.Can the fee for data analytics training be paid in installment?
Yes! You can pay the fee in installments. The course is affordable so you can pay full fee at once and get the benefit of the course.
2.Which mode of payment can I use to pay the fee?
You can use credit cards, debit cards, bank transfer and other modes of payment.
3.How can I practice the code written in R and Python?
We will provide you all the facilities for practicing codes of R and Python Language.
4.Will I get any certificate after completing data analyst training
Yes! You will get a certificate after completing the course of data analyst training.
5.In what format will we get the certificate?
You will get an online data analytics certificate and we will also provide physical copies of the certificate.
6.What is a data analytics manager’s salary?
The data analytics manager salary is $112K.
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Mock projects
Real world project samples for practical sessions
