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Week 1 – 3

Introduction to Data Analytics, Data manipulation, Data cleaning, Functions used in data inspection, and basic SQL queries, Exploratory Data Analysis (EDA)

Week 4 – 6

Programming in R Language, Data Mining GUI in R, Data Wrangling, Data Management using dplyr in R, Statistics & Probability, Random Variables, Data Distributions like uniform, Binomal, Exponential.

Week 7 – 9

Modeling in R, Linear Regression, Mining Algorithms Using R, Apriori Algorithm, Ensemble Models, Segmentation Analysis, Data Mining: Cluster Techniques.

Week 10 – 12

Data Mining: Association Rule Mining and Collaborative Filtering, Regression (Linear and Logistic), Data Mining: Entropy, BAGGING OF Regression and Classification Trees.

Week 13 – 15

Time Series Forecasting in R and Model Deployment, Using SQL Server, and Data Visualization Tool Tableau.

Week 16 – 18

Practice Day, Assignment, Coding Challenges, Capstone Project: Real time Live Project, Technical Interview Workshop, Hiring Day

Duration: 80 hours

Cost: $750/course
(excluding any certification cost)

Data Analytics

  • Basics of R
  • Conditional and loops
  • R packages/libraries
  • Data mining GUI in R
  • Data structures in R
  • Exceptions/ debugging in R
  • Reading CSV, JSON, XML, .XLSX and HTML files using R
  • ETL operations in R
  • Sorting/ merging data in R
  • Cleaning data
  • Data management using dplyr in R
  • Descriptive statistics, random variables, and probability distribution functions
  • Data distributions like uniform, binomial, exponential, poisson, etc
  • Probability concepts, set theory and hypothesis testing
  • Central limit theorem, t-test, chi-square, z-test
  • Central limit theorem
  • ANOVA
  • Linear regression model in R
  • Multiple linear regressions model
  • Representation of regression results
  • Non-linear regression models
  • Tree-based regression models
  • Decision tree-based models
  • Rule-based systems
  • Association analysis
  • Market-based analysis/ rules
  • Apriori algorithm
  • Ensemble models – random forest model, boosting model
  • Segmentation analysis- types of segmentation, k-means clustering, Bayesian clustering.
  • Component analysis.
  • Axes,Covariance
  • Basics of time series
  • Components of time series
  • Time series forecasting
  • Deploying predictive models
  • Using SQL server
  • Using external tools
  • Using big data tools
  • Integrating R with Hadoop/Spark
  • SQL queries
  • Integrating with R
  • Deployment and execution
  • Data modeling and formatting using Excel
  • Excel formulas to perform analytics
  • Macros for job automation
  • Introduction to Tableau and its layout
  • Connecting tableau to files and databases
  • Data filters in Tableau
  • Calculation and parameters
  • Tableau graphs and maps
  • Creating Tableau dashboard
  • Data blending
  • Creating superimposed graphs
  • Integrating Tableau with R