Artificial Intelligence Engineer Master's Course

Module 01 - Introduction to Data Science with R

1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science, and its components
1.3 Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
1.4 Introduction to R programming and RStudio

Hands-on Exercise:

1. Installation of RStudio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases

Module 02 - Data Exploration

2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What are data exploratory analysis and data importing?
2.4 DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Hands-on Exercise:

1. Accessing individual elements of customer churn data
2. Modifying and extracting results from the dataset using user-defined functions in R

Module 03 - Data Manipulation

3.1 Need for data manipulation
3.2 Introduction to the dplyr package
3.3 Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
3.4 Combining different functions with the pipe operator and implementing SQL-like operations with sqldf

Hands-on Exercise:

1. Implementing dplyr
2. Performing various operations for manipulating data and storing it

Module 04 - Data Visualization

4.1 Introduction to visualization
4.2 Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
4.5 Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
4.6 Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
4.7 Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
4.8 Geographic visualization with ggmap() and building web applications with shinyR

Hands-on Exercise:

1. Creating data visualization to understand the customer churn ratio using ggplot2 charts
2. Using plotly for importing and analyzing data
3. Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot

Module 05 - Introduction to Statistics

5.1 Why do we need statistics?
5.2 Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
5.3 Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Hands-on Exercise:

1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data

Module 06 - Machine Learning

6.1 Introduction to Machine Learning
6.2 Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
6.3 Predicting results and finding the p-value and an introduction to logistic regression
6.4 Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
6.5 Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
6.6 Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables

Hands-on Exercise:

1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable

Module 07 - Logistic Regression

7.1 Introduction to logistic regression
7.2 Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
7.3 Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
7.4 Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
7.5 Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
7.6 Real-life applications of logistic regression

Hands-on Exercise:

1. Implementing predictive analytics by describing data
2. Explaining the relationship between one dependent binary variable and one or more binary variables
3. Using glm() to build a model, with ‘Churn’ as the dependent variable

Module 08 - Decision Trees and Random Forest

8.1 What is classification? Different classification techniques
8.2 Introduction to decision trees
8.3 Algorithm for decision tree induction and building a decision tree in R
8.4 Confusion matrix and regression trees vs classification trees
8.5 Introduction to bagging
8.6 Random forest and implementing it in R
8.7 What is Naive Bayes? Computing probabilities
8.8 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
8.9 Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics

Hands-on Exercise:

1. Implementing random forest for both regression and classification problems
2. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
3. Using ROCR for performance metrics

Module 09 - Unsupervised Learning

9.1 What is Clustering? Its use cases
9.2 what is k-means clustering? What is canopy clustering?
9.3 What is hierarchical clustering?
9.4 Introduction to unsupervised learning
9.5 Feature extraction, clustering algorithms, and the k-means clustering algorithm
9.6 Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
9.7 Dendograms, understanding hierarchical clustering, and implementing it in R
9.8 Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R

Hands-on Exercise:

1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data

Module 10 - Association Rule Mining and Recommendation Engines

10.1 Introduction to association rule mining and MBA
10.2 Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
10.3 Introduction to recommendation engines
10.4 User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
10.5 Recommendation engine use cases

Hands-on Exercise:

1. Deploying association analysis as a rule-based Machine Learning method
2. Identifying strong rules discovered in databases with measures based on interesting discoveries

Self-paced Course Content

Module 11 - Introduction to Artificial Intelligence

11.1 Introducing Artificial Intelligence and Deep Learning
11.2 What is an artificial neural network? TensorFlow: The computational framework for building AI models
11.3 Fundamentals of building ANN using TensorFlow and working with TensorFlow in R

Module 12 - Time Series Analysis

12.1 What is a time series? The techniques, applications, and components of time series
12.2 Moving average, smoothing techniques, and exponential smoothing
12.3 Univariate time series models and multivariate time series analysis
12.4 ARIMA model
12.5 Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis

Hands-on Exercise:

1. Analyzing time series data
2. Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series

Module 13 - Support Vector Machine (SVM)

13.1 Introduction to Support Vector Machine (SVM)
13.2 Data classification using SVM
13.3 SVM algorithms using separable and inseparable cases
13.4 Linear SVM for identifying margin hyperplane

Module 14 - Naïve Bayes

14.1 What is the Bayes theorem?
14.2 What is Naïve Bayes Classifier?
14.3 Classification Workflow
14.4 How Naive Bayes classifier works and classifier building in Scikit-Learn
14.5 Building a probabilistic classification model using Naïve Bayes and the zero probability problem

Module 15 - Text Mining

15.1 Introduction to the concepts of text mining
15.2 Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
15.3 Text mining algorithms and the quantification of the text
15.4 TF-IDF and after TF-IDF

Case Studies

Case Study 01: Market Basket Analysis (MBA)

1.1 This case study is associated with the modeling technique of Market Basket Analysis, where you will learn about loading data, plotting items, and running algorithms.
1.2 It includes finding out the items that go hand in hand and can be clubbed together.
1.3 This is used for various real-world scenarios like a supermarket shopping cart and so on.

Case Study 02: Logistic Regression

2.1 In this case study, you will get a detailed understanding of the advertisement spends of a company that will help drive more sales.
2.2 You will deploy logistic regression to forecast future trends.
2.3 You will detect patterns and uncover insight using the power of R programming.
2.4 Due to this, the future advertisement spends can be decided and optimized for higher revenues.

Case Study 03: Multiple Regression

3.1 You will understand how to compare the miles per gallon (MPG) of a car based on various parameters.
3.2 You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc.
3.3 The case study includes model building, model diagnostic, and checking the ROC curve, among other things.

Case Study 04: Receiver Operating Characteristic (ROC)

4.1 In this case study, you will work with various datasets in R.
4.2 You will deploy data exploration methodologies.
4.3 You will also build scalable models.
4.4 Besides, you will predict the outcome with highest precision, diagnose the model that you have created with real-world data, and check the ROC curve.

Data Science Projects Covered

Market Basket Analysis

This is an inventory management project where you will find the trends in the data that will help the company to increase sales. In this project, you will be implementing association rule mining, data extraction, and data manipulation for the Market Basket Analysis.

Credit Card Fraud Detection

The project consists of data analysis for various parameters of banking dataset. You will be using a V7 predictor, V4 predictor for analysis, and data visualization for finding the probability of occurrence of fraudulent activities.

Loan Approval Prediction

In this project, you will use the banking dataset for data analysis, data cleaning, data preprocessing, and data visualization. You will implement algorithms such as Principal Component Analysis and Naive Bayes after data analysis to predict the approval rate of a loan using various parameters.

Netflix Recommendation System

Implement exploratory data analysis, data manipulation, and visualization to understand and find the trends in the Netflix dataset. You will use various Machine Learning algorithms such as association rule mining, classification algorithms, and many more to create movie recommendation systems for viewers using Netflix dataset.

Case Study 1: Introduction to R Programming

In this project, you need to work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for various organizational needs.

Case Study 2: Solving Customer Churn Using Data Exploration

Use data exploration in order to understand what needs to be done to make reductions in customer churn. In this project, you will be required to extract individual columns, use loops to work on repetitive operations, and create and implement filters for data manipulation.

Case Study 3: Creating Data Structures in R

Implement numerous data structures for numerous possible scenarios. This project requires you to create and use vectors. Further, you need to build and use metrics, utilize arrays for storing those metrics, and have knowledge of lists.

Case Study 4: Implementing SVD in R

Utilize the dataset of MovieLens to analyze and understand single value decomposition and its use in R programming. Further, in this project, you must build custom recommended movie sets for all users, develop a collaborative filtering model based on the users, and for a movie recommendation, you must create realRatingMatrix.

Case Study 5: Time Series Analysis

This project required you to perform TSA and understand ARIMA and its concepts with respect to a given scenario. Here, you will use the R programming language, ARIMA model, time series analysis, and data visualization. So, you must understand how to build an ARIMA model and fit it, find optimal parameters by plotting PACF charts, and perform various analyses to predict values.

Module 01 - Python Environment Setup and Essentials

1.1 Introduction to Python Language
1.2 Features, the advantages of Python over other programming languages
1.3 Python installation – Windows, Mac & Linux distribution for Anaconda Python
1.4 Deploying Python IDE
1.5 Basic Python commands, data types, variables, keywords and more

Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac.

Module 02 - Python language Basic Constructs

2.1 Built-in data types in Python
2.2 Learn  classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug
2.3 Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise
2.4 Loop and control statements while, for, if, break, else, continue.

Hands-on Exercise –
1. Write your first Python program
2. Write a Python Function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. create an object
7. Write a for loop

Module 03 - OOP concepts in Python

3.1 How to write OOP concepts program in Python
3.2 Connecting to a database
3.3 Classes and objects in Python
3.4 OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation
3.5 Python functions, return types and parameters
3.6 Lambda expressions

Hands-on Exercise –
1. Creating an application which helps to check balance, deposit money and withdraw the money using the concepts of OOPS.

Module 04 - Database connection

4.1 Understanding the Database, need of database
4.2 Installing MySQL on windows
4.3 Understanding Database connection using Python.

Hands-on Exercise – Demo on Database Connection using python and pulling the data.

Module 05 - NumPy for mathematical computing

5.1 Introduction to arrays and matrices
5.2 Broadcasting of array math, indexing of array
5.3 Standard deviation, conditional probability, correlation and covariance.

Hands-on Exercise –
1. How to import NumPy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers
4. Calculating correlation between two variables.

Module 06 - SciPy for scientific computing

6.1 Introduction to SciPy
6.2 Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.

Hands-on Exercise –
1. Importing of SciPy
2. Applying the Bayes theorem on the given dataset.

Module 07 - Matplotlib for data visualization

7.1 How to plot graph and chart with Python
7.2 Various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.

Hands-on Exercise –
1. Deploying MatPlotLib for creating Pie, Scatter, Line, Histogram.

Module 08 - Pandas for data analysis and machine learning

8.1 Introduction to Python dataframes
8.2 Importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe
8.3 Various data operations like selecting, filtering, sorting, viewing, joining, combining

Hands-on Exercise –
1. Working on importing data from JSON files
2. Selecting record by a group
3. Applying filter on top, viewing records

Module 09 - Exception Handling

9.1 Introduction to Exception Handling
9.2 Scenarios in Exception Handling with its execution
9.3 Arithmetic exception
9.4 RAISE of Exception
9.5 What is Random List, running a Random list on Jupyter Notebook
9.6 Value Error in Exception Handling.

Hands-on Exercise –
1. Demo on Exception Handling with an Industry-based Use Case.

Module 10 - Multi Threading & Race Condition

10.1 Introduction to Thread, need of threads
10.2 What are thread functions
10.3 Performing various operations on thread like joining a thread, starting a thread, enumeration in a thread
10.4 Creating a Multithread, finishing the multithreads.
10.5 Understanding Race Condition, lock and Synchronization.

Hands-on Exercise –
1. Demo on Starting a Thread and a Multithread and then perform multiple operations on them.

Module 11 - Packages and Functions

11.1 Intro to modules in Python, need of modules
11.2 How to import modules in python
11.3 Locating a module, namespace and scoping
11.4 Arithmetic operations on Modules using a function
11.5 Intro to Search path, Global and local functions, filter functions
11.6 Python Packages, import in packages, various ways of accessing the packages
11.7 Decorators, Pointer assignments, and Xldr.

Hands-on Exercise –
1. Demo on Importing the modules and performing various operation on them using arithmetic functions
2. Importing various packages and accessing them and then performing different operations on them.

Module 12 - Web scraping with Python

12.1 Introduction to web scraping in Python
12.2 Installing of beautifulsoup
12.3 Installing Python parser lxml
12.4 Various web scraping libraries, beautifulsoup, Scrapy Python packages
12.5 Creating soup object with input HTML
12.6 Searching of tree, full or partial parsing, output print

Hands-on Exercise –
1. Installation of Beautiful soup and lxml Python parser
2. Making a soup object with input HTML file
3. Navigating using Py objects in soup tree.

Analyzing the Naming Pattern Using Python

In this Python project, you will work with the United States Social Security Administration (SSA) which has made data on the frequency of baby names from 1880 to 2016 available. The project requires analyzing the data considering different methods. You will visualize the most frequent names, determine the naming trends and come up with the most popular names for a certain year.

Performing Analysis on Customer Churn Dataset

Using the powers of data science and data visualization you will be performing analysis on the reliability of the employees of a telecom industry. This real time analysis of data will be done through multiple labels and the final outcomes will be reflected through multiple reports.

Python Web Scraping for Data Science

Through this project you will be introduced to the process of web scraping using Python. It involves installation of Beautiful Soup, web scraping libraries, working on common data and page format on the web, learning the important kinds of objects, Navigable String, deploying the searching tree, navigation options, parser, search tree, searching by CSS class, list, function and keyword argument.

Module 01 - Introduction to Machine Learning

1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning

Module 02 - Supervised Learning and Linear Regression

2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
2.2 Introduction to regression
2.3 Simple linear regression
2.4 Multiple linear regression and assumptions in linear regression
2.5 Math behind linear regression

Hands-on Exercise:

1. Implementing linear regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
3. Implementing train–test split and predicting the values on the test set

Module 03 - Classification and Logistic Regression

3.1 Introduction to classification
3.2 Linear regression vs logistic regression
3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR

Hands-on Exercise:

1. Implementing logistic regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
3. Building a confusion matrix to find out accuracy, true positive rate, and false positive rate

Module 04 - Decision Tree and Random Forest

4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest

Hands-on Exercise:

1. Implementing a decision tree from scratch in Python
2. Using Python library Scikit-Learn to build a decision tree and a random forest
3. Visualizing the tree and changing the hyper-parameters in the random forest

Module 05 - Naïve Bayes and Support Vector Machine (self-paced)

5.1 Introduction to probabilistic classifiers
5.2 Understanding Naïve Bayes and math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM and math behind SVM

Hands-on Exercise:

1. Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier

Module 06 - Unsupervised Learning

6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA

Hands-on Exercise:

1. Using Python library Scikit-Learn to implement k-means clustering
2. Implementing PCA (principal component analysis) on top of a dataset

Module 07 - Natural Language Processing and Text Mining (self-paced)

7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 Language Toolkit (NLTK) environment
7.7 Text mining: Its cleaning, pre-processing, and text classification

Hands-on Exercise:

1. Learning Natural Language Toolkit and NLTK Corpora
2. Reading and writing .txt files from/to a local drive
3. Reading and writing .docx files from/to a local drive

Module 08 - Introduction to Deep Learning

8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural networks vs artificial neural networks
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders

Module 09 - Time Series Analysis (self-paced)

9.1 What is time series? Its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, and exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model and time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis

Hands-on Exercise:

1. Analyzing time series data
2. The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
3. Forecasting the future values in the series

What projects and case studies will I be working on in this Machine Learning course Online?

Project 01: Analyzing the Trends of COVID-19 with Python

Industry: Analytics

Problem Statement: Understanding the trends of COVID-19 spread and checking if restrictions imposed by governments around the world have helped us curb COVID-19 cases and by what degree

Topics: In this project, we will use Data Science and Python and perform visualizations to better understand the data on COVID-19. We will also use time series analysis to make predictions about future cases.

Highlights:

• Using Pandas to accumulate data from multiple data files
• Using plotly to create interactive visualizations
• Using Facebook’s Prophet library to make time series models
• Visualizing the prediction by combining these technologies

Project 02: Customer Churn Classification

Topics: This is a real-world project that gives you hands-on experience in working with most of the Machine Learning algorithms.

Highlights:

• Manipulating data to gain meaningful insights
• Visualizing data to figure out trends and patterns among different factors
• Implementing these algorithms: linear regression, decision tree, and Naïve Bayes

Project 03: Creating a Recommendation System for Movies

Topics: This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project requires you to deeply understand information filtering, recommender systems, user ‘preference’, and more. You will exclusively work on data related to user details, movie details, and others.

Highlights:

• Recommendation for movies
• Two types of predictions: Rating prediction and item prediction
• Important approaches: Memory-based and model-based
• Knowing user-based methods in k-nearest neighbor
• Understanding the item-based method
• Matrix factorization
• Decomposition of singular value
• Data Science project discussion
• Collaboration filtering
• Business variables overview

Case Study 01: Decision Tree

Topics: Understand the structure of a dataset (PIMA Indians Diabetes database) and create a decision tree model based on it by using Scikit-Learn

Case Study 02: Insurance Cost Prediction (Linear Regression)

Topics: Understand the structure of a medical insurance dataset, implement both simple and multiple linear regressions, and predict values.

Case Study 03: Diabetes Classification (Logistic Regression)

Topics: Understand the structure of a dataset (PIMA Indians Diabetes dataset); implement multiple logistic regressions and classify; fit your model on the test and train data for prediction; evaluate your model using confusion matrix, and then visualize it

Case Study 04: Random Forest

Topics: Create a model that would help in classifying whether a patient ‘is normal,’ ‘is suspected to have a disease,’ or in actuality ‘has the disease’ using the ‘Cardiotocography’ dataset

Case Study 05: Principal Component Analysis (PCA)

Topics: Read the sample Iris dataset; use PCA to figure out the number of most important principal features, and then reduce the number of features using PCA; train and test the random forest classifier algorithm to check if reducing the number of dimensions is causing the model to perform poorly, and figure out the most optimal number that produces good quality results and predicts accurately

Case Study 06: K-means Clustering

Topics: Analyze data; extract useful columns from the dataset; visualize the data; find out the appropriate number of groups or clusters for the data to be segmented (using the elbow method); using k-means clustering, segment the data into k groups (k is found in the previous step); visualize a scatter plot of clusters, and a lot more

Module 01 - Introduction to Deep Learning and Neural Networks

1.1 Field of machine learning, its impact on the field of artificial intelligence
1.2 The benefits of machine learning w.r.t. Traditional methodologies
1.3 Deep learning introduction and how it is different from all other machine learning methods
1.4 Classification and regression in supervised learning
1.5 Clustering and association in unsupervised learning, algorithms that are used in these categories
1.6 Introduction to ai and neural networks
1.7 Machine learning concepts
1.8 Supervised learning with neural networks
1.9 Fundamentals of statistics, hypothesis testing, probability distributions, and hidden markov models.

Module 02 - Multi-layered Neural Networks

2.1 Multi-layer network introduction, regularization, deep neural networks
2.2 Multi-layer perceptron
2.3 Overfitting and capacity
2.4 Neural network hyperparameters, logic gates
2.5 Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions
2.6 Back propagation, forward propagation, convergence, hyperparameters, and overfitting.

Module 03 - Artificial Neural Networks and Various Methods

3.1 Various methods that are used to train artificial neural networks
3.2 Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques
3.3 Stochastic process, vanishing gradients, transfer learning, regression techniques,
3.4 Lasso l1 and ridge l2, unsupervised pre-training, xavier initialization.

Module 04 - Deep Learning Libraries

4.1 Understanding how deep learning works
4.2 Activation functions, illustrating perceptron, perceptron training
4.3 multi-layer perceptron, key parameters of perceptron;
4.4 Tensorflow introduction and its open-source software library that is used to design, create and train
4.5 Deep learning models followed by google’s tensor processing unit (tpu) programmable ai
4.6 Python libraries in tensorflow, code basics, variables, constants, placeholders
4.7 Graph visualization, use-case implementation, keras, and more.

Module 05 - Keras API

5.1 Keras high-level neural network for working on top of tensorflow
5.2 Defining complex multi-output models
5.3 Composing models using keras
5.3 Sequential and functional composition, batch normalization
5.4 Deploying keras with tensorboard, and neural network training process customization.

Module 06 - TFLearn API for TensorFlow

6.1 Using tflearn api to implement neural networks
6.2 Defining and composing models, and deploying tensorboard

Module 07 - Dnns (deep neural networks)

7.1 Mapping the human mind with deep neural networks (dnns)
7.2 Several building blocks of artificial neural networks (anns)
7.3 The architecture of dnn and its building blocks
7.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Module 08 - Cnns (convolutional neural networks)

8.1 What is a convolutional neural network?
8.2 Understanding the architecture and use-cases of cnn
8.3‘What is a pooling layer?’ how to visualize using cnn
8.4 How to fine-tune a convolutional neural network
8.5 What is transfer learning?
8.6 Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow.

Module 09 - Rnns (recurrent neural networks)

9.1 Introduction to the rnn model
9.2 Use cases of rnn, modeling sequences
9.3 Rnns with back propagation
9.4 Long short-term memory (lstm)
9.5 Recursive neural tensor network theory, the basic rnn cell, unfolded rnn,  dynamic rnn
9.6 Time-series predictions.

Module 10 - Gpu in deep learning

10.1 Gpu’s introduction, ‘how are they different from cpus?,’ the significance of gpus
10.2 Deep learning networks, forward pass and backward pass training techniques
10.3 Gpu constituent with simpler core and concurrent hardware.

Module 11- Autoencoders and restricted boltzmann machine (rbm)

11.1 Introduction  rbm and autoencoders
11.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering
11.3 Autoencoders features and applications of autoencoders.

Module 12 - Deep learning applications

12.1 Image processing
12.2 Natural language processing (nlp) – Speech recognition, and video analytics.

Module 13 - Chatbots

13.1 Automated conversation bots leveraging any of the following descriptive techniques:  Ibm watson, Microsoft’s luis, Open–closed domain bots,
13.2 Generative model, and the sequence to sequence model (lstm).

What projects I will be working on during this AI online course?

Project 01: Image Recognition with TensorFlow

Industry: Internet Search

Problem Statement: Creating a Deep Learning model to identify the right object on the Internet as per the user search for the corresponding image

Description: In this project, you will learn how to build a convolutional neural network using Google TensorFlow. You will do the visualization of images using training, providing input images, losses, and distributions of activations and gradients. You will learn to break each image into manageable tiles and input them to the convolutional neural network for the desired result.

Highlights:

• Constructing a convolutional neural network using TensorFlow
• Convolutional, dense, and pooling layers of CNNs
• Filtering images based on user queries

Project 02: Building an AI-based Chatbot using IBM watson LAB

Industry: Ecommerce

Problem Statement: Building a chatbot using Artificial Intelligence

Description: In this project, by understanding the customer needs, you will be able to offer the right services through Artificial Intelligence chatbots. You will learn how to create the right artificial neural network with the right amount of layers to ensure that the customer queries are comprehensible to the Artificial Intelligence chatbot. This will help you understand Natural Language Processing, going beyond keywords, data parsing, and providing the right solutions.

Highlights:

• Breaking user queries into components
• Building neural networks with TensorFlow
• Understanding Natural Language Processing

Overview of Natural Language Processing and Text Mining

Introduction to Natural Language Processing (NLP), Introduction to Text Mining, importance and applications of Text Mining, how NPL works with Text Mining, writing and reading to word files, OS Module, Natural Language Toolkit (NLTK) Environment.

Hands-on Exercise: Learning Natural Language Toolkit and NLTK Corpora, reading and writing .txt files to/from local drive, reading and writing .docx Files to/from local drive.

Text Mining, Cleaning, and Pre-processing

Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Hands-on Exercise: Learning Word Tokenization with Python regular expressions, Sentence Tokenizers, Stopword Removal, Bigrams, Trigrams, and Ngrams, Named Entity Recognition, and POS Tagging.

Text Classification

Overview of Machine Learning, Words, Term Frequency, Count Vectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naiive Bayes Classifier.

Hands-on Exercise: Demonstration of Count Vectorizer, Words, Term Frequency, Inverse Document Frequency, Text conversion, text classification, and Confusion Matrix.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

Hands-on Exercise: Demonstration of Syntax Trees, Chunking, Automatic Paraphrasing of Texts, and Chinking.

Introduction to Semantics and Vector Space Models

Distributional Semantics, Traditional Models, Tools for sentence and word embeddings, an overview of Topic Models.

Hands-on Exercise: Embedding word and sentence.

Dialog Systems

Introduction to task-oriented Dialog Systems, Natural Language Understanding, Dialog Manager.

Hands-on Exercise: Design your own Dialog System.

What projects I will be working on this NLP Training Using Python?

Project: Analyze Movie Review Data with NLP

Industry: Entertainment

Problem Statement: Perform sentiment analysis on a given dataset to analyze movie reviews

Project Description: In this project, as an NLP engineer, your job is to pre-process the data using tokenization and lemmatization and then develop an understanding of the different components of the data by identifying different parts of the speech and named entities in the text. After having a sufficient understanding of the attributes and syntactic structure of the text, perform a sentimental analysis task on the data by classifying whether movie reviews are positive or negative.

Highlights:

In this project, you will edit the dataset “Movie Reviews“, which is included in NLTK Corpus. The dataset contains multiple positive and negative reviews retrieved from the imdb.com.

Module 01 - RBM and DBNs & Variational AutoEncoder

1.1 Introduction rbm and autoencoders
1.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering
1.3 Autoencoders features and applications of autoencoders.

Module 02 - Object Detection using Convolutional Neural Net

2.1 Constructing a convolutional neural network using TensorFlow
2.2 Convolutional, dense, and pooling layers of CNNs
2.3 Filtering images based on user queries

Module 03 - Generating images with Neural Style and Working with Deep Generative Models

3.1 Automated conversation bots leveraging
3.2 Generative model, and the sequence to sequence model (lstm).

Module 04 - Distributed & Parallel Computing for Deep Learning Models

4.1 Parallel Training
4.2 Distributed vs Parallel Computing
4.3 Distributed computing in Tensorflow
4.4 Introduction to tf.distribute
4.5 Distributed training across multiple CPUs
4.6 Distributed Training
4.7 Distributed training across multiple GPUs
4.8 Federated Learning
4.9 Parallel computing in Tensorflow

Module 05 - Reinforcement Learning

5.1 Mapping the human mind with deep neural networks (dnns)
5.2 Several building blocks of artificial neural networks (anns)
5.3 The architecture of dnn and its building blocks
5.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Module 06 - Deploying Deep Learning Models and Beyond

6.1 Understanding model Persistence
6.2 Saving and Serializing Models in Keras
6.3 Restoring and loading saved models
6.4 Introduction to Tensorflow Serving
6.5 Tensorflow Serving Rest
6.6 Deploying deep learning models with Docker & Kubernetes
6.7 Tensorflow Serving Docker
6.8 Tensorflow Deployment Flask
6.9 Deploying deep learning models in Serverless Environments
6.10 Deploying Model to Sage Maker
6.11 Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Introduction to SAS

Installation and introduction to SAS, how to get started with SAS, understanding different SAS windows, how to work with data sets, various SAS windows like output, search, editor, log and explorer and understanding the SAS functions, which are various library types and programming files

SAS Enterprise Guide

How to import and export raw data files, how to read and subset the data sets, different statements like SET, MERGE and WHERE

Hands-on Exercise: How to import the Excel file in the workspace and how to read data and export the workspace to save data

SAS Operators and Functions

Different SAS operators like logical, comparison and arithmetic, deploying different SAS functions like Character, Numeric, Is Null, Contains, Like and Input/Output, along with the conditional statements like If/Else, Do While, Do Until and so on

Hands-on Exercise: Performing operations using the SAS functions and logical and arithmetic operations

Compilation and Execution

Understanding about input buffer, PDV (backend) and learning what is Missover

Using Variables

Defining and using KEEP and DROP statements, apply these statements and formats and labels in SAS

Hands-on Exercise: Use KEEP and DROP statements

Creation and Compilation of SAS Data Sets

Understanding the delimiter, dataline rules, DLM, delimiter DSD, raw data files and execution and list input for standard data

Hands-on Exercise: Use delimiter rules on raw data files

SAS Procedures

Various SAS standard procedures built-in for popular programs: PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, PROC CORR, etc.

Hands-on Exercise: Use SORT, FREQ, SUMMARY, EXPORT and other procedures

Input Statement and Formatted Input

Reading standard and non-standard numeric inputs with formatted inputs, column pointer controls, controlling while a record loads, line pointer control/absolute line pointer control, single trailing, multiple IN and OUT statements, dataline statement and rules, list input method and comparing single trailing and double trailing

Hands-on Exercise:  Read standard and non-standard numeric inputs with formatted inputs, control while a record loads, control a line pointer and write multiple IN and OUT statements

SAS Format

SAS Format statements: standard and user-written, associating a format with a variable, working with SAS Format, deploying it on PROC data sets and comparing ATTRIB and Format statements

Hands-on Exercise: Format a variable, deploy format rule on PROC data set and use ATTRIB statement

SAS Graphs

Understanding PROC GCHART, various graphs, bar charts: pie, bar and 3D and plotting variables with PROC GPLOT

Hands-on Exercise: Plot graphs using PROC GPLOT and display charts using PROC GCHART

Interactive Data Processing

SAS advanced data discovery and visualization, point-and-click analytics capabilities and powerful reporting tools

Data Transformation Function

Character functions, numeric functions and converting variable type

Hands-on Exercise: Use functions in data transformation

Output Delivery System (ODS)

Introduction to ODS, data optimization and how to generate files (rtf, pdf, html and doc) using SAS

Hands-on Exercise: Optimize data and generate rtf, pdf, html and doc files

SAS Macros

Macro Syntax, macro variables, positional parameters in a macro and macro step

Hands-on Exercise: Write a macro and use positional parameters

PROC SQL

SQL statements in SAS, SELECT, CASE, JOIN and UNION and sorting data

Hands-on Exercise: Create SQL query to select and add a condition and use a CASE in select query

Advanced Base SAS

Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval and descriptive statistics

Hands-on Exercise: Use web UI to do statistical operations

Summarization Reports

Report enhancement, global statements, user-defined formats, PROC SORT, ODS destinations, ODS listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT and PROC PRINT

Hands-on Exercise: Use PROC SORT to sort the results, list ODS, find mean using PROC Means and print using PROC PRINT

What projects I will be working on this SAS training?

Project 1: Categorization of Patients Based on the Count of Drugs for Their Therapy

Domain: Healthcare

Objective: This project aims to find out descriptive statistics and subset for specific clinical data problems. It will give them brief insight about Base SAS procedures and data steps.

Problem Statement:

Count the number of patients,

1. Who were ever on at least one of the four drugs
2. Who were ever on each of the four drugs
3. Who had never been on any drug

Output should be four datasets

1. TYPA – Contains the list of patients from problem 1
2. TYPB – Contains the list of patients from problem 2
3. TYPC – Contains the list of patients from problem 3
4. SUMMARY – Contains the summary of counts for each of three problems

Project 2: Build Revenue Projections Reports

Domain: Sales

Objective: This project will give you hands-on experience in working with the SAS data analytics and business intelligence tool. You will be working on the data entered in a business enterprise setup and will aggregate, retrieve and manage that data. You will learn to create insightful reports and graphs and come up with statistical and mathematical analysis to scientifically predict the revenue projection for a particular future time frame. Upon the completion of the project, you will be well-versed in the practical aspects of data analytics, predictive modeling and data mining.

Project 3: Impact of Pre-paid Plans on the Preferences of Investors

Domain: Finance Market

Objective: The project aims to find the most impacting factors in preferences of pre-paid model; it also identifies which all are the variables highly correlated with impacting factors.

Problem Statement:

• The project aims to identify various reasons for pre-paid model preference and non-preference among the investors, to understand the penetration of the pre-paid model in the brokerage firms and, to identify the pre-paid scheme advantages and disadvantages and also to identify brand-wise market share. In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale.

Project 4:K-Means Cluster Analysis on Iris Dataset

Domain: Analytics

Objective: K-Means cluster analysis on Iris dataset to predict about the class of a flower using its petal’s dimensions

Requirements:

• Using the famous Iris dataset, predict the class of a flower
• Perform K-Means cluster analysis

Entering Data

Introduction to Excel spreadsheet, learning to enter data, filling of series and custom fill list, editing and deleting fields.

Referencing in Formulas

Learning about relative and absolute referencing, the concept of relative formulae, the issues in relative formulae, creating of absolute and mixed references and various other formulae.

Name Range

Creating names range, using names in new formulae, working with the name box, selecting range, names from a selection, pasting names in formulae, selecting names and working with Name Manager.

Understanding Logical Functions

the various logical functions in Excel, the If function for calculating values and displaying text, nested If functions, VLookUp and IFError functions.

Getting started with Conditional Formatting

Learning about conditional formatting, the options for formatting cells, various operations with icon sets, data bars and color scales, creating and modifying sparklines.

Advanced-level Validation

multi-level drop down validation, restricting value from list only, learning about error messages and cell drop down.

Important Formulas in Excel

Introduction to the various formulae in Excel like Sum, SumIF & SumIFs, Count, CountA, CountIF and CountBlank, Networkdays, Networkdays International, Today & Now function, Trim (Eliminating undesirable spaces), Concatenate (Consolidating columns)

Working with Dynamic table

Introduction to dynamic table in Excel, data conversion, table conversion, tables for charts and VLOOKUP.

Data Sorting

Sorting in Excel, various types of sorting including, alphabetical, numerical, row, multiple column, working with paste special, hyperlinking and using subtotal.

Data Filtering

The concept of data filtering, understanding compound filter and its creation, removing of filter, using custom filter and multiple value filters, working with wildcards.

Chart Creation

Creation of Charts in Excel, performing operations in embedded chart, modifying, resizing, and dragging of chart.

Various Techniques of Charting

Introduction to the various types of charting techniques, creating titles for charts, axes, learning about data labels, displaying data tables, modifying axes, displaying gridlines and inserting trendlines, textbox insertion in a chart, creating a 2-axis chart, creating combination chart.

Pivot Tables in Excel

The concept of Pivot tables in Excel, report filtering, shell creation, working with Pivot for calculations, formatting of reports, dynamic range assigning, the slicers and creating of slicers.

Ensuring Data and File Security

Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.

Getting started with VBA Macros

Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros.

Core concepts of VBA

In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.

Ranges and Worksheet in VBA

Learning about the concepts of workbooks and worksheets in Excel, protection of macro codes, range coding, declaring a variable, the concept of Pivot Table in VBA, introduction to arrays, user forms, getting to know how to work with databases within Excel.

IF condition

Learning how the If condition works and knowing how to apply it in various scenarios, working with multiple Ifs in Macro.

Loops in VBA

Understanding the concept of looping, deploying looping in VBA Macros.

Debugging in VBA

Studying about debugging in VBA, the various steps of debugging like running, breaking, resetting, understanding breakpoints and way to mark it, the code for debugging and code commenting.

Messaging in VBA

The concept of message box in VBA, learning to create the message box, various types of message boxes, the IF condition as related to message boxes.

Practical Projects in VBA

Mastering the various tasks and functions using VBA, understanding data separation, auto filtering, formatting of report, combining multiple sheets into one, merging multiple files together.

Best Practices of Dashboards Visualization

Introduction to powerful data visualization with Excel Dashboard, important points to consider while designing the dashboards like loading the data, managing data and linking the data to tables and charts, creating Reports using dashboard features.

Principles of Charting

Learning to create charts in Excel, the various charts available, the steps to successfully build a chart, personalization of charts, formatting and updating features, various special charts for Excel dashboards, understanding how to choose the right chart for the right data.

Getting started with Pivot Tables

Creation of Pivot Tables in Excel, learning to change the Pivot Table layout, generating Reports, the methodology of grouping and ungrouping of data.

Creating Dashboards

Learning to create Dashboards, the various rules to follow while creating Dashboards, creation of dynamic dashboards, knowing what is data layout, introduction to thermometer chart and its creation, how to use alerts in the Dashboard setup.

Creation of Interactive Components

How to insert a Scroll bar to a data window?, Concept of Option buttons in a chart, Use of combo box drop-down, List box control Usage, How to use Checkbox Control?

Data Analysis

Understanding data quality issues in Excel, linking of data, consolidating and merging data, working with dashboards for Excel Pivot Tables.

What projects I will be working on this Excel certification training?

Project – if Function

Data – Employee

Problem Statement – It describes about if function and how to implement this if function. It includes following actions:

Calculates Bonus for all employee at 10% of their salary using if Function, Rate the salesman based on the sales and the rating scale., Find the number of times “3” is repeated in the table and find the number of values greater than 5 using Count Function, Uses of Operators and nested if function

Introduction to Data Visualization and Power of Tableau

What is data visualization?, comparison and benefits against reading raw numbers, real use cases from various business domains, some quick and powerful examples using Tableau without going into the technical details of Tableau, installing Tableau, Tableau interface, connecting to DataSource, Tableau data types, and data preparation.

Architecture of Tableau

Installation of Tableau Desktop, architecture of Tableau, interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.) how to start with Tableau, and the ways to share and export the work done in Tableau.

Hands-on Exercise: Play with Tableau desktop, learn about the interface, and share and export existing works.

Working with Metadata and Data Blending

Connection to Excel, cubes and PDFs, management of metadata and extracts, data preparation, Joins (Left, Right, Inner, and Outer) and Union, dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract , etc.

Hands-on Exercise: Connect to Excel sheet to import data, use metadata and extracts, manage NULL values, clean up data before using, perform the join techniques, execute data blending from multiple sources , etc.

Creation of Sets

Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies), constant sets, computed sets, bins, etc.

Hands-on Exercise: Use marks to create and edit sets, highlight the desired items, make groups, apply sorting on results, and make hierarchies among the created sets.

Working with Filters

Filters (addition and removal), filtering continuous dates, dimensions, and measures, interactive filters, marks card, hierarchies, how to create folders in Tableau, sorting in Tableau, types of sorting, filtering in Tableau, types of filters, filtering the order of operations, etc.

Hands-on Exercise: Use the data set by date/dimensions/measures to add filter, use interactive filter to view the data, customize/remove filters to view the result, etc.

Organizing Data and Visual Analytics

Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste; formatting data using labels and tooltips, edit axes and annotations, k-means cluster analysis, trend and reference lines, visual analytics in Tableau, forecasting, confidence interval, reference lines, and bands.

Hands-on Exercise: Apply labels and tooltips to graphs, annotations, edit axes’ attributes, set the reference line, and perform k-means cluster analysis on the given dataset.

Working with Mapping

Working on coordinate points, plotting longitude and latitude, editing unrecognized locations, customizing geocoding, polygon maps, WMS: web mapping services, working on the background image, including add image, plotting points on images and generating coordinates from them; map visualization, custom territories, map box, WMS map; how to create map projects in Tableau, creating dual axes maps, and editing locations.

Hands-on Exercise: Plot longitude and latitude on a geo map, edit locations on the geo map, custom geocoding, use images of the map and plot points, find coordinates, create a polygon map, and use WMS.

Working with Calculations and Expressions

Calculation syntax and functions in Tableau, various types of calculations, including Table, String, Date, Aggregate, Logic, and Number; LOD expressions, including concept and syntax; aggregation and replication with LOD expressions, nested LOD expressions; levels of details: fixed level, lower level, and higher level;  quick table calculations, the creation of calculated fields, predefined calculations, and how to validate.

Working with Parameters

Creating parameters, parameters in calculations, using parameters with filters, column selection parameters, chart selection parameters, how to use parameters in the filter session, how to use parameters in calculated fields, how to use parameters in reference line, etc.

Hands-on Exercise: Creating new parameters to apply on a filter, passing parameters to filters to select columns, passing parameters to filters to select charts, etc.

Charts and Graphs

Dual axes graphs, histograms: single and dual axes; box plot; charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts; maps: tree and heat maps; market basket analysis (MBA), using Show me; and text table and highlighted table.

Hands-on Exercise: Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset and also perform market basket analysis (MBA) on the same dataset.

Dashboards and Stories

Building and formatting a dashboard using size, objects, views, filters, and legends; best practices for making creative as well as interactive dashboards using the actions; creating stories, including the intro of story points; creating as well as updating the story points, adding catchy visuals in stories, adding annotations with descriptions; dashboards and stories: what is dashboard?, highlight actions, URL actions, and filter actions, selecting and clearing values, best practices to create dashboards, dashboard examples; using Tableau workspace and Tableau interface; learning about Tableau joins, types of joins; Tableau field types, saving as well as publishing data source, live vs extract connection, and various file types.

Hands-on Exercise: Create a Tableau dashboard view, include legends, objects, and filters, make the dashboard interactive, and use visual effects, annotations, and description s to create and edit a story.

Tableau Prep

Introduction to Tableau Prep, how Tableau Prep helps quickly combine join, shape, and clean data for analysis, creation of smart examples with Tableau Prep, getting deeper insights into the data with great visual experience, making data preparation simpler and accessible, integrating Tableau Prep with Tableau analytical workflow, and understanding the seamless process from data preparation to analysis with Tableau Prep.

Integration of Tableau with R and Hadoop

Introduction to R language, applications and use cases of R, deploying R on the Tableau platform, learning R functions in Tableau, and the integration of Tableau with Hadoop.

Hands-on Exercise: Deploy R on Tableau, create a line graph using R interface, and also connect Tableau with Hadoop to extract data.

Tableau Projects Covered

Understanding the global covid-19 mortality rates

Analyze and develop a dashboard to understand the covid-19 global cases.Compare the global confirmed vs. death cases in a world map. Compare the country wise cases using logarithmic axes. Dashboard should display both a log axis chart and a default axis chart in an alternate interactive way. Create a parameter to dynamically view Top N WHO regions based on cumulative new cases and death cases ratio. Dashboard should have a drop down menu to view the WHO region wise data using a bar chart, line chart or a map as per user’s requirement.

Understand the UK bank customer data

Analyze and develop a dashboard to understand the customer data of a UK bank. Create an asymmetric drop down of Region with their respective customer names and their Balances with a gender wise color code. Region wise bar chart which displays the count of customers based on High and low balance. Create a parameter to let the users’ dynamically decide the limit value of balance which categorizes it into high and low. Include interactive filters for Job classifications and Highlighters for Region in the final dashboard.

Understand Financial Data

Create an interactive map to analyze the worldwide sales and profit. Include map layers and map styles to enhance the visualization. Interactive analysis to display the average gross sales of a product under each segment, allowing only one segment data to be displayed at once. Create a motion chart to compare the sales and profit through the years. Annotate the day wise profit line chart to indicate the peaks and also enable drop lines. Add go to URL actions in the final dashboard which directs the user to the respective countries Wikipedia page.

Understand Agriculture Data

Create interactive tree map to display district wise data. Tree maps should have state labels. On hovering on a particular state, the corresponding districts data are to be displayed. Add URL actions, which direct users’ to a Google search page of the selected crop. Web page is to be displayed on the final dashboard. Create a hierarchy of seasons, crop categories and the list of crops under each. Add highlighters for season. One major sheet in the final dashboard should be unaffected by any action applied. Use the view in this major sheet to filter data in the other. Using parameters color code the seasons with high yield and low yield based on its crop categories. Rank the crops based on their yield