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Data Science using R courses online in Delhi NCR | Yami Services

DATA SCIENCE USING R

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Courses Overview

The Data Science with R Programming course focuses on in-depth knowledge of various techniques for data analytics using R programming.

Learn different statistical concepts like linear and logistic regression, cluster analysis, and forecasts with estimated data analysis. It extends the learning curve by teaching techniques used for data manipulation and the overview of basic data structures. The business analyst and other professionals dealing in large amount of data can derive results using the ready-made functions available in R.

This course provides an in-depth understanding of Data Science with R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various packages available in 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 are leading companies using the power of analytics?

• Critical Success Driver

• Overview of analysis tools and their popularity

• Analytics method and problem solving framework

• List of steps in Analytics projects

• Identify the most suitable solution design for the given problem statement

• Project plan for Analytics project and key milestones based on effort projections

• Create resource plan for analytics project

• Why R for Data Science?

• Introduction R / R-studio - GUI

• Package concept - useful package (base and other package)

• Data structure and data types (vector, matrices, factors, data frames, and lists)

• Import data from different sources (txt, dlm, excel, sas7bdata, db, etc.)

• Database input (connected to database)

• Exporting data in different formats)

• Viewing Data (Viewing Partial Data and Full Data)

• Variable and value labels - date values

• Data Manipulation Steps

• Creating new variables (calculation and binning)

• Dummy Variable Construction

• Applying changes

• Duplicate Handling

• Handling Missing

• Sorting and filtering

• Subscription (rows / columns)

• Attached (attachment line / column attached)

• Mergers / Joins (left, right, internal, complete, external etc.)

• Data type conversion

• change the name of

• Formatting

• Replacing the data

• to sample

• Data Manipulation Equipment

• Operators

• Work

• Packages

• Control Structures (if, if otherwise)

• Loops (conditional, repeat loop, apply function)

• Schedule

R built-in function (text, numeric, date, utility)

• Numerical work

• Text work

• Date of work

• Utility work

• R user defined work

• R package for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc.)

• Introduction to exploration data analysis

• Descriptive figures, frequency table and compaction

• Universal analysis (distribution of data and graphical analysis)

• Biwariate analysis (cross tab, distribution and relationships, graphical analysis)

• Creating graphs - bar / pie / line chart / histogram / boxplot / scatter / density etc.)

• R package for exploratory data analysis (dplyr, plyr, gmodes, car, vcd, hmisc, psych, doby etc)

·         R Packages for Graphical Analysis (base, ggplot, lattice,etc)

·         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

·         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

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·         Need of Data preparation

Consolidation / Aggregation - External Treatment - Flat Liner - Missing Value - Dummy Construction - Convertible Deficiencies

• Variable reduction techniques - Factor and PCA analysis

• 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

·         Interpretation of results - Implementation on new data

·         Introduction - Applications

·         Assumptions of Linear Regression

·         Building Linear Regression Model

Understand the standard metrics (variable significance, R-class / adjusted R-square, global hypothesis, etc.)

• Measure the overall effectiveness of the model

• Model Verification (vs vs vs scoring)

• Standard trade output (desil analysis, error distribution (histogram), model equation, driver etc.)

·         Explanation of the results - Business Verification - implementation on new data

• Introduction - Applications

• Linear Regression vs. Logistic Regression vs. Normalized Linear Models

• Construction of Logistic Regression Models (Binary Logistic Model)

• Understanding the Standard Model Metric (Coordination, Variable Importance, Hosmer Lamesov Test, Guinea, KS, Miscellaneous, 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)

Explanation of the results - Business Verification - implementation on new data

• Introduction - Applications

• Time series components (trend, seasonal, cyclicity and levels) and decomposition

• Classification of Techniques (Pattern Based - Pattern Low)

·         Basic techniques - average, smoothening, etc.

• Advanced technology - AR model, area, etc.

• Understanding forecast accuracy - MAPE, MAD, MSE, etc.

• Introduction to machine learning and prediction modeling

• Types of Business Problems - Mapping of Techniques - Regression vs. Classification vs. Segmentation vs. Forecasting

• Major categories of learning algorithms - supervised versus unprivileged learning

• Various stages of predictive modeling (data preprocessing, sampling, model building, verification)

• Overfitting (twenty-variance trade off) and performance metrics

• Feature engineering and dimension reduction

• Concept of customization and cost function

• Overview of gradient line algorithms

• Cross verification overview (bootstrapping, K-fold verification etc.)

• Model performance metric (R-square, adjusted R-Square, RMSE, MAPE, AUC, ROC curve, memory, accuracy, sensitivity, specificity, illusion metrics)

• What is the division and role of ML in segmentation?

• The concept of distance and related math background

• Meaning Clustering

• Maximum Expectancy

• hierarchical clustering

• Spectral Clustering (DBSCAN)

• Principle Component Analysis (PCA)

• Decision Trees - Introduction - Applications

• Types of Decision Tree Algorithms

• Construction of decision tree through simplified examples; Selecting the "best" attribute on each non-address node; Entropy; Information Benefits, Guinea Index, Chi Square, Regression Trees

• making decision tree normal; Information content and profit ratio; Working with numerical variables; Other Remedies of Randomness

• Cutting a decision tree; Cost as consideration; Unwrapping trees as rules

• Decision Trees - Verification

• Overfitting - Best Practices for Avoiding

• Concept of Ensembling

• Manual enabling vs. Automatic Ensembling

• Methods of Ensembling (Stacks, Combinations of Experts)

• Bagging (logic, practical application)

• Random jungle (logic, practical application)

• Boosting (Logic, Practical Applications)

• Eda Boost

• Gradient Boosting Machines (GBM)

• XGBoost

• Inspiration for neural networks and its applications

·         Perceptron and Single Layer Neural Network, and Hand Calculations

·         Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques

·         Neural Networks for Regression

·         Neural Networks for Classification

·         Interpretation of Outputs and Fine tune the models with hyper parameters

·         Validating ANN models

·         Motivation for Support Vector Machine & Applications

·         Support Vector Regression

·         Support vector classifier (Linear & Non-Linear)

·         Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

·         Define output and fine tune model with hyper parameter

• SVM Model Valid

• What is KNN and Application?

• KNN for missing treatment

• To solve KNN regression problems

• KNN to solve classification problems

• validating the KNN model

• Fix tuning model with hyper parameter

• The concept of conditional probability

• Bayes theorem and its applications

• Naval Bayes for classification

• Applications of Naive Bayes in classification

• Big text taming, uncontrolled versus semi-structured data; Information retrieval basics, properties of words; Creating a Term-Document (TxD); Matrice; Equality measures, low-level processes (sentence division) tokenization; Speech-speech-tagging; Stemming; Chanking)

·         Finding patterns in text: text mining, text as a graph

·         Natural Language processing (NLP)

·         Text Analytics – Sentiment Analysis using R

·         Text Analytics – Word cloud analysis using R

·         Text Analytics - Segmentation using K-Means/Hierarchical Clustering

·         Text Analytics - Classification (Spam/Not spam)

·         Applications of Social Media Analytics

·         Metrics(Measures Actions) in social media analytics

Examples and Actionable Insights utilizing Social Media Analytics

 

•        Important R bundles for Machine Learning (caret, H2O, Randomforest, nnet, tm and so on)

 

•        Fine tuning the models utilizing Hyper parameters, matrix seek, funneling and so forth.

 

Applying distinctive calculations to take care of the business issues and seat check the outcomes

 

  • Duration: 40 hrs
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