DATA SCIENCE AND ANALYTICS


DATA EXPLORATION WITH STATISTICS


  • Types of Data
  • Data Summarization
  • Frequency tables and Distributions
  • Histograms
  • Measures of central tendency
    • Mean, Mode, and Median
    • Skewness and Kurtosis
  • Probability
  • Conditional Probability

  • SAMPLING AND HYPHOTHESIS TESTING


  • Normal Distribution
    • Significance Level
    • p-Value
  • Sampling and Estimation
  • Central Limit Theorem
  • Point and Interval Estimates
  • Null and Alternate Hypothesis
  • Types of Errors

  • PREDICTIVE ANALYTICS:LINEAR REGRESSION


  • Covariance and Correlation
  • Simple Linear Regression
  • ANOVA
  • Significance Tests
  • Multiple Linear Regression
  • Interpretation of Regression Coefficients
  • Categorical/ Dummy Variables
  • Assumptions of Linear Regression and implications
    • heteroscedasticity
    • Multicollinearity
    • Serial Correlation
  • Outliers
  • Regression Models Building

  • PREDICTIVE ANALYTICS:LOGISTIC REGRESSION


  • When to use logistic regression
  • Assumptions
  • Logistic Function
  • Model Fit
    • Chi-Square test
    • Log likelihood
    • Classification table
  • Interpreting Coefficients
  • Inferential Statistics
  • Dependent Variable Prediction

  • PREDICTIVE ANALYTICS: FORECASTING


  • Principles of Forecasting
    • Time Series
    • Casual Models
  • Forecasting Methods and Characteristics
    • Moving Average
    • Exponential Smoothing
  • Forecast Data Patterns Types
    • Level
    • rend
    • Seasonality
    • Cyclical
  • Compute Forecast Accuracy
  • Selection of Forecasting Models

  • MARKET BASKET ANALYSIS :UNSUPERVISED LEARNING


  • Concepts
  • Frequent Item set Methods
    • Apriori Algorithm, coding and Examples
    • FP-Growth Algorithm, coding and Examples
    • Pattern Evaluation Methods
  • Lift
  • Chi-Square

  • CLASSIFICATION


  • Concepts
  • Decision Trees coding examples
  • Bayes Classification method
  • Model Evaluation and Selection
  • Techniques to improve classification Accuracy
  • CLUSTERING


  • Concepts
  • Partitioning methods
  • Hierarchical methods
  • Density based methods
  • Grid based methods
  • Evaluation of Clustering

  • TOOLS


    Introduction to MS-Excel

  • Sumifs, countIfs, AvergaeIfs
  • Pivot Tables and Charts
  • Filters, advanced Filters
  • VLOOKUP, HLOOKUP, OFFSET, INDEX, and Match
  • Case Study
  • HANDS ON CODING TO RAPID PROTOTYPING FRAMEWORK RSTUDIO


  • Reading and Writing to R
  • Vectors
  • Frames and Subsets
  • Examples Regression
  • Examples Logistic Regression
  • Examples Machine Learning
    • Configuration Management Activity
    • Configuration Control
    • Incident Management
    • Change Management
    • Problem Management

    BIG DATA APPLICATIONS


  • Coding in SQL based Hive examples
  • Coding in Non-SQL based PIG examples
  • Hadoop Java MapReduce Programs: 10 examples
  • Spark Machine Learning Examples
  • Storm and Kafka Examples
  • PROJECTS REAL WORLD CASES


  • Churn Prediction in Telecom
  • Churn Prediction in HR
  • Prediction of Sales
  • Prediction of ATM Cash Deposits / Day
  • Recommendation Systems Algorithms used in Amazon/Linkedin
  • Text Analytics Classification using Linear SVM
  • Business cases 4
  • DESIGN OF EXPERIMENTS


  • Design of Experiments methods
  • A/B Testing for E-Commerce platform performance upgrade
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