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Data-Analytics-Online-Training



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.com
Phone/WhatsApp : +91-(850) 012-2107  
USA Number : 214-628-3894
Gtalk : raavi.sriraja@gmail.com


Big 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 Content

Introduction 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:
• Random Forest
• What is Naive Bayes?

Unsupervised learning:
What is Clustering & its Use Cases?
• What is K-means Clustering?
• What is Hierarchical Clustering?

Association Analysis and Recommendation engine:
• Market Basket Analysis (MBA)
• Association Rules
• Apriori Algorithm for MBA
• Introduction of Recommendation Engine
• Types of Recommendation - User-Based and Item-Based
• Recommendation Use-case