Machine learning in Datascience online training

• Deploying machine learning for data analysis, solving business problems, using algorithms for searching patterns in data, the relationship between variables, multivariate analysis, interpreting correlation, negative correlation.

Deep dive into Data Transformation

• Data Transformation key phases Data Mapping and Code Generation, Data Processing operation, data patterns, data sampling, sampling distribution, a normal and continuous variable, data extrapolation, regression, linear regression model.

Data Testing and Assessment

• Data analysis, hypothesis testing, simple linear regression, Chi-square for assessing compatibility between theoretical and observed data, implementing data testing on data warehouse, validating data, checking for accuracy, data operational monitoring capabilities.

Data Model, Algorithms & Prediction

• Various techniques of data modeling and generating algorithms, methods of business prediction, prediction approaches, data sampling, disproportionate sampling, data modeling rules, data iteration, and deploying data for mission-critical applications.

Data Segmentation and Analysis

• Working with large data sets in data warehouses, data clustering, grouping, horizontal & vertical slicing, data sharing in partitioning, clustering algorithms, K-means Clustering for analysis and data mining, exclusive clustering, hierarchy clustering, Mahout Clustering algorithm, and Probabilistic Clustering, nearest neighbor search, pattern recognition, and statistical classification.