Data Management, Analysis and Graphics with R

Introduction to data Science with R

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem-solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project
• Why R for data science?

Data Importation & Exportation.

• Introduction R/R-Studio – GUI
• Concept of Packages – Useful Packages (Base & Other packages)
• Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
• Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
• Database Input (Connecting to database)
• Exporting Data to various formats)
• Viewing Data (Viewing partial data and full data)
• Variable & Value Labels – Date Values

Data Manipulation.

• Data Manipulation steps
• Creating New Variables (calculations & Binning)
• Dummy variable creation
• Applying transformations
• Handling duplicates
• Handling missing’s
• Sorting and Filtering
• Subsetting (Rows/Columns)
• Appending (Row appending/column appending)
• Merging/Joining (Left, right, inner, full, outer etc)
• Data type conversions
• Renaming
• Formatting
• Reshaping data
• Sampling
• Data manipulation tools
• Loops (Conditional, iterative loops, apply functions)
• Arrays
• R Built-in Functions (Text, Numeric, Date, utility)
• Numerical Functions
• Text Functions
• Date Functions
• Utilities Functions
• R User Defined Functions

Data Analysis- Visualization

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
• R Packages for Exploratory Data Analysis (dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
• R Packages for Graphical Analysis (base, ggplot, lattice,etc)

Introduction to Statistics

• Basic Statistics – Measures of Central Tendencies and Variance
• Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
• Inferential Statistics -Sampling – Concept of Hypothesis Testing
• Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square

Introduction to Predictive Modeling

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modeling process
• Popular modeling algorithms
• Types of Business problems – Mapping of Techniques
• Different Phases of Predictive Modeling

Data Exportation for Modelling

Data Preparations

• Need of Data preparation
• Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
• Variable Reduction Techniques – Factor & PCA Analysis

Segmentation: Solving segmentation problems

• Introduction to Segmentation
• Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Behavioral Segmentation Techniques (K-Means Cluster Analysis)
• Cluster evaluation and profiling – Identify cluster characteristics

Linear Regression

• Interpretation of results – Implementation on new data
• Introduction – Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results – Business Validation – Implementation on new data

Logistics Regression.

• Introduction – Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model (Binary Logistic Model)
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results – Business Validation – Implementation on new data

Time series Forecasting

• Introduction – Applications
• Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques (Pattern based – Pattern less)

Way forward After the Training

Participants will develop a work plan through the help of facilitators that stipulates application of skills acquired in improving their organizations. ASPM will continuously monitor implementation progress after the training.

Training Evaluation:

Participants will undertake a simple assessment before the training to gauge knowledge and skills and another assessment after the training in-order to monitor knowledge gained through the training