#### 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)

## Curriculum

##### 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