Big Data Analytics largely involves collecting data from different sources, munge it in a way that it becomes available to be consumed by analysts and finally deliver data products useful to the organization business. The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Big Data Analytics. FOR FREE DEMO contact :Email : raj@apex-online-it-training.comPhone/WhatsApp : +91-(850) 012-2107 USA Number : 214-628-3894Gtalk : raavi.sriraja@gmail.comBig Data Analytics Interview Questions and Answers, Recorded Video Sessions, Materials, Mock Interviews Assignments Will be provided Big Data Analytics Agenda/Syllabus(we can customize the course Curriculum as per your
requirements)DATA ANALYTICS Course ContentIntroduction
to Data Science and Statistical Analytics:•
Introduction to Data Science, Use cases
• Need of Business Analytics • Data Science Life Cycle • Different tools available for Data Science Introduction
to R:•
Installing R and R-Studio, R packages, R Operators, if statements and loops
(for, while, repeat, break, next), switch case
Data
Exploration, Data Wrangling and R Data Structure:• Data exploratory analysis
• R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions Data
Visualization:•
Bar Graph (Simple, Grouped, Stacked)
• Histogram, Pi Chart • Line Chart • Box (Whisker) Plot, Scatter Plot Introduction
to Statistics:Terminologies
of Statistics
• Measures of Centers • Measures of Spread • Probability • Normal Distribution • Binary Distribution • Hypothesis Testing • Chi Square Test • ANOVA Predictive
Modeling - 1:•
Supervised Learning - Linear Regression ,Bivariate Regression, Multiple
Regression Analysis, Correlation( Positive, negative and neutral)
• Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories Predictive
Modeling - 2:•
Logistic Regression Decision
Trees:What
is Classification and its use cases? • What is Decision Tree? • Algorithm for Decision Tree Induction • Creating a Perfect Decision Tree • Confusion Matrix Random
Forest:• What is Naive Bayes? Unsupervised
learning:• What is K-means Clustering? • What is Hierarchical Clustering? Association
Analysis and Recommendation engine:• Association Rules • Apriori Algorithm for MBA • Introduction of Recommendation Engine • Types of Recommendation - User-Based and Item-Based • Recommendation Use-case |

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