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Datascience with R programming

1. Introduction to Data Science.

• What is data science? How Data Science differ from BI and Reporting
• Who are data scientist? What skill sets are required?
• What are roles in a data science in project?
• What is Analytics and Predictive Analytics?
• Challenges in Using Predictive Analytics.

2. Setting Up the Problem

• Stages of a data science project.
• Setting Expectation.
• Overview with case study

3. Business Statistics

• Data types
• Continuous Variable
• Ordinary Variable
• Categorical Variables
• Descriptive Statics
• Inferential Statics
• Types of data
• Population and sampling
• Measure of Central Tendency
• Measure of Variability
• Histogram
• Box Plot
• Skewness
• Kurtosis
• Random variable
• Probability Distribution
• PDF and CDF of Random Variable
• Expected Value of Mean
• Variance and Standard Deviation
• Data Distribution.(Normal Distribution, Binomial Distribution and Gaussian
distribution)
• Z score, Standard Normal Distribution
• Statistical Inference
• Sampling : Types of Sampling
• Simple Random Sampling, Stratified Sampling
• Probability and non-probability Sampling
• Central Limit Theorem
• Point Estimation and Interval Estimation
• Confident Interval and Interpreting CI
• Confident Interval for the mean
• T-distribution and its characteristics
• Estimate Error with t-distribution
• When to use z or t distribution
• Hypothesis Testing
• Testing Process
• Type I error
• Type II error
• Null and Alternative hypothesis
• Reject or acceptance Criteria
• ANOVA
• F-distribution
• Statistical test for equality of mean
• Interview questions

4. R Programming

• What is R?
• R Data Types in R
• Types of objects in R – Lists, matrices, arrays, data. Frames etc.
• Creating new variables or updating existing variables
• Variable declaration.
• String Manipulations
• Writing Function in R
• Conditional Statement
• Loops and summaries
• Importing and Exporting
• Data Manipulation and Transformation
• Merging datasets
• Efficient Data handling in R
• Interview Question

5. Exploratory Data analysis and Visualization.

• Getting data into R – reading from files
• Cleaning and preparing the data – converting data types (characters to numeric)
• Importance of EDA in Data Science.
• Exploring Numerical Data
• Numerical Summary
• Correlation vs causation
• Case study and interview question
• Predictive Analytics
• Supervised Learning
• Simple Linear Regression.
• Assumptions
• Sum of least squares
• Model validation
• Error Measurement – RMSE – Root Mean Square Error
• Disadvantage of linear Models
• Multiple Linear regression.
• Multivariate Linear regression.
• Interpretation using R Programming
• Accuracy measurement
• Visualization using ggplot2
• Case Study with Interview questions

6. Predictive Analytics

• Supervised Learning
• Simple Linear Regression.
• Assumptions
• Sum of least squares
• Model validation
• Error Measurement – RMSE – Root Mean Square Error
• Disadvantage of linear Models
• Multiple Linear regression.
• Multivariate Linear regression.
• Interpretation using R Programming
• Accuracy measurement
• Visualization using ggplot2
• Case Study with Interview questions

7. Classification

• What a Decision Tree Is
• How to create a Tree
• Classification and Regression Tree.
• Algorithms ID3, C4.5, C5.0 with R-Implementation
• What can go wrong? (Over fitting)
• Accuracy measurement.
• Misclassification
• Area under the Curve
• Interview questions

8. Re-Sampling and Ensembles Methods

• Random Forest algorithm with R-Implementation
• Bagging and Boosting – Gradient Boosting Method

9. Logistic Regression

• What is Logistic Regression?
• Need for logistic Regression
• Logistic Regression Implication using R Programming
• Interpreting Logistic Regression Model
• Accuracy measurement
• Other Practical Consideration for Logistic regression model
• Interview questions

10. KNN – K nearest neighbors

• Advantages and disadvantages

11. Un-Supervised learning

• Hierarchical Clustering with implementation in R
• K-Means Clustering with implementation in R
• Interview Questions

12. Probalistic Methods

• Naive Bayes

13. Forecasting Algorithm

• Time series analysis using ARIMA model.
• Case study
• Interview questions.

14. Building Web application with shiny.

• Shiny review
• Make the perfect plot using shiny
• Explore a dataset interactively with shiny
• Interview Questions.

15. Model Deployment and Cross validation

• Need Cross Validation and k –fold cross validation
• Model Deployment

16. Advance Methods

• Support Vector Machine
• Neural Networks
• Introduction of Deep Learning

17. Working in Cloud machine Leaning Tools

• Introduction of Azure ML
• Introduction to AWS ML

18. Project implementation in real data

19. Building Web application with shiny.

• Shiny review
• Make the perfect plot using shiny
• Explore a dataset interactively with shiny
• Interview Questions.

20. Model Deployment and Cross validation

• Need Cross Validation and k –fold cross validation
• Model Deployment

21. Advance Methods

• Support Vector Machine
• Neural Networks
• Introduction of Deep Learning

22. Working in Cloud machine Leaning Tools

• Introduction of Azure ML
• Introduction to AWS ML

23. Project implementation in real data

14. Building Web application with shiny.

• Shiny review
• Make the perfect plot using shiny
• Explore a dataset interactively with shiny
• Interview Questions.

15. Model Deployment and Cross validation

• Need Cross Validation and k –fold cross validation
• Model Deployment

16. Advance Methods

• Support Vector Machine
• Neural Networks
• Introduction of Deep Learning

17. Working in Cloud machine Leaning Tools

• Introduction of Azure ML
• Introduction to AWS ML

18. Project implementation in real data

19. Building Web application with shiny.

• Shiny review
• Make the perfect plot using shiny
• Explore a dataset interactively with shiny
• Interview Questions.

20. Model Deployment and Cross validation

• Need Cross Validation and k –fold cross validation
• Model Deployment

21. Advance Methods

• Support Vector Machine
• Neural Networks
• Introduction of Deep Learning

22. Working in Cloud machine Leaning Tools

• Introduction of Azure ML
• Introduction to AWS ML

23. Project implementation in real data Inventateq

Data Science with Python

1. Python for Data Science

2. Introduction to Statistics

• Types of Statistics
• Analytics Methodology and Problem-Solving Framework
• Populations and samples
• Parameter and Statistics
• Uses of variable: Dependent and Independent variable
• Types of Variable: Continuous and categorical variable

3. Descriptive Statistics

4. Probability Theory and Distributions

5. Picturing your Data

• Histogram
• Normal Distribution
• Skewness, Kurtosis
• Outlier detection

6. Inferential Statistics

7. Hypothesis Testing

8. Analysis of variance (ANOVA)

• Two sample t-Test
• F-test
• One-way ANOVA
• ANOVA hypothesis
• ANOVA Model
• Two-way ANOVA

9. Regression

• Exploratory data analysis
• Hypothesis testing for correlation
• Outliers, Types of Relationship, Scatter plot
• Missing Value Imputation
• Simple Linear Regression Model
• Multiple Regression
• Model Building and Evaluation

10. Model post fitting for Inference

• Examining Residuals
• Regression Assumptions
• Identifying Influential Observations
• Detecting Collinearity

11. Categorical Data Analysis

• Describing categorical Data
• One-way frequency tables
• Association
• Cross Tabulation Tables
• Test of Association
• Logistic Regression
• Model Building
• Multiple Logistic Regression and Interpretation

12. Model Building and scoring for Prediction

• Introduction to predictive modelling
• Building predictive model
• Scoring Predictive Model
• Introduction to Machine Learning and Analytics

13. Introduction to Machine Learning

• What is Machine Learning?
• Fundamental of Machine Learning
• Key Concepts and an example of ML
• Supervised Learning
• Unsupervised Learning

14. Linear Regression with one variable

• Model Representation
• Cost Function
• Parameter Learning
• Gradient Descent

15. Linear Regression with Multiple Variable

• Computing parameter analytically
• Ridge, Lasso, Polynomial Regression

16. Logistic Regression

• Classification
• Hypothesis Testing
• Decision Boundary
• Cost Function and Optimization

17. Multiclass Classification

18. Regularization

• Overfitting, Under fitting

19. Model Evaluation and Selection

• Confusion Matrix
• Precision-recall and ROC curve
• Regression Evaluation

20. Support Vector Machine

21. Decision Tree, Random Forest

22. Unsupervised Learning

• Clustering
• K-mean Algorithm

23. Dimensionality Reduction

• Principal Component Analysis and applications

24. Introduction to text analytics

25. Introduction to Neural Network

Machine Learning with Python

1. Introduction to Machine Learning

• What is Machine Learning?
• Fundamental of Machine Learning
• Key Concepts and an example of ML
• Supervised Learning
• Unsupervised Learning

2. Python

• Basics of Python
• Machine Learning Libraries
• Data Pre-processing/Exploration in Python
• Handling Missing Values
• Handling Outliers
• One Hot Encoder & Feature Scaling

3. Regression

• Assumptions of Linear Regression
• Simple Linear Regression Model
• Cost Function & Gradient Descent
• Multiple Regression
• Model Building and Evaluation
• Ridge, Lasso and Polynomial Regression
• Identifying Influential Features
• Regularization: Overfitting and underfitting
• Cross-Validation

4. Categorical Data Analysis

• Describing categorical Data
• Association
• Cross Tabulation Tables
• Test of Association
• Logistic Regression
• Decision Boundary
• Cost Function and Optimization
• Model Building
• Multiple Logistic Regression and Interpretation

5. Model Building and scoring for Prediction

• Introduction to predictive modelling
• Building predictive model
• Scoring Predictive Model

6. Multiclass/Multi-Label Classification

7. Imbalanced Dataset

8. Model Evaluation and Selection

• Accuracy
• Confusion Matrix
• Precision-recall and ROC curve
• Regression Evaluation

9. Support Vector Machine

10. K-Nearest Neighbours(K-NN)

11. Decision Tree, Random Forest

12. Unsupervised Learning

• Clustering
• K-means Algorithm

13. Dimensionality Reduction

• Principal Component Analysis and applications

14. Introduction to text analytics/Natural Language Processing

• Bag of Words
• TF-IDF
• LDA (Latent Discriminant Analysis)

15. Model Selection, Ensemble models

16. XG-Boost

17. Introduction to Neural Network

18. Recommender Systems

• Collaborative Filtering
• Content-Based Filtering
• SVD (Singular value Decomposition)

Artificial Intelligence

• Introduction to AI
• Agents and Search
• A* Search and Heuristics
• Constraint Satisfaction Problems
• CSPs II
• Game Trees: Minimax
• Game Trees: Expectimax; Utilities
• Markov Decision Processes
• Markov Decision Processes II
• Reinforcement Learning
• Reinforcement Learning II
• Probability
• Bayes' Nets: Representation
• Bayes' Nets: Independence
• Bayes' Nets: Inference
• Bayes' Nets: Sampling
• Decision Diagrams / VPI
• HMMs: Filtering
• HMMs: Wrap-up / Speech
• ML: Naive Bayes
• ML: Perceptron
• ML: Kernels and Clustering
• ML: Neural Networks and Decision Trees
• Robotics / Language / Vision
• Miscellaneous Topics

Deep Learning and Image Recognition

1. Training Neural Networks, part I

• Activation functions, initialization, dropout,
• batch normalization

2. Course Introduction

• Computer vision overview
• Historical context
• Course logistics

3. Image Classification

• The data-driven approach
• K-nearest neighbour
• Linear classification I

4. Loss Functions and Optimization

• Linear classification II
• Higher-level representations, image features
• Optimization, stochastic gradient descent

5. Introduction to Neural Networks

• Backpropagation
• Multi-layer Perceptrons
• The neural viewpoint

6. Convolutional Neural Networks

• History
• Convolution and pooling
• ConvNets outside vision

7. Training Neural Networks, part II

• Update rules, ensembles, data
• augmentation, transfer learning

8. Deep Learning Software

• Caffe, Torch, Theano, TensorFlow,
• Keras, PyTorch, etc

9. CNN Architectures

• AlexNet, VGG, GoogLeNet, ResNet, etc

10. Recurrent Neural Networks

• RNN, LSTM, GRU
• Language modelling
• Image captioning, visual question
• answering Soft attention

11. Detection and Segmentation

• Semantic segmentation
• Object detection
• Instance segmentation

12. Visualizing and Understanding

• Feature visualization and inversion
• Adversarial examples
• DeepDream and style transfer

13. Generative Models

• PixelRNN/CNN
• Variational Autoencoders
• Generative Adversarial Networks

14. Deep Reinforcement Learning

• Policy gradients, hard attention
• Q-Learning, Actor-Critic

Tableau 10 Visualization

1. Introduction to Tableau Desktop

• Overview of Business Intelligence
• Introduction to Tableau Desktop
• Use and benefits of Tableau Desktop
• Tableau's Offerings
• Guide to Install Tableau Desktop 10.5

2. Tableau Desktop Interface

• Start Page
• Data Source Page
• Worksheet Interface
• Creating a Basic View
• Show Me
• Shelves, cards , Marks and pills

3. Connecting Data Sources

• Data Types
• Data Roles
• Visual Cues for Fields
• Data Preparation
• Data Source optimization
• Joins
• Cross Database Joins
• Data Blending
• Joining vs. Blending
• Union
• Creating Data Extracts
• Writing Custom SQL

4. Organizing Data

• Filtering Data
• Sorting Data
• Creating Combined Fields
• Creating Groups and Defining Aliases
• Working with Sets and Combined Sets
• Drilling and Hierarchy
• Adding Grand Totals and Subtotals
• Changing Aggregation Functions
• Creating Bin
• Cross Data Source Filter

5. Formatting Data

• Effectively use Titles, Captions, and Tooltips
• Format Results with the Edit Axes
• Formatting your View
• Formatting results with Labels and Annotations
• Enabling Legends per Measure

6. Calculations

• Use Strings, Date, Logical, and Arithmetic Calculations
• Create Table Calculations
• Discover Ad-hoc Analytics
• Perform LOD Calculations

7. Visualizations

• Creating Basic Charts such as Heat Map, Tree Map, Bullet Chart, and so on
• Creating Advanced Chart as Waterfall, Pareto, Gantt, Market Basket analysis, and Mekko Chart Embed Views

8. Analysis using Desktop

• Reference lines
• Reference bands
• Reference distributions
• Trend lines
• Statistical summary card
• Instant Analytics
• Forecasting
• Clustering

9. Mapping

• Modify locations within Tableau
• Import and manage custom geocoding
• Explore Geographic Search
• Perform Pan/Zoom, Lasso, and Radial Selection
• Measure Distance
• WMS server
• Use a background image
• Custom Territories

10. Fields in Tableau

• Tableau generated fields
• Measure values and names
• When to use measure values and names
• Number of records
• Generated latitude and longitude
• Special fields
• Date hierarchies
• Discrete and continuous date parts
• Custom dates

11. Parameters

• Create a parameter
• Explore Parameter Controls
• Using Parameters in Calculations
• Using Parameters and Reference Lines
• Using Parameters with Filters

12. Create Dashboards and Stories

• Dashboard Interface
• Build Interactive Dashboards
• Explore Dashboard Actions
• Best Practices for Creating Effective Dashboards
• Story Interface
• Creating Stories
• Share Your Work

13. Tableau Online

• Creating Tableau online account
• Administering Tableau Online
• Publish data source
• Publish Reports

14. Tableau Project

• Industry based Project