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Course Description


  • To cater to this special category of unicorn Data Science professionals, we at ExcelR have formulated a comprehensive 6-month intensive training program that encompasses all facets of the Data Science and related fields that at Team Leader / Manager is expected to know and more. Under this program, the selected participants will be given adequate hands-on important ETL tools; Learn the nuances of setting up a Big Data virtual lab; harness the power of Amazon web services; work with live scenarios in the agile environment; make interactive and
    • Agile Project Management Methodology in handling Data Analytics Projects.

    • Data Science for analyzing data in the most disciplined manner

    • Machine Learning for building predictive modeling

    • Artificial Intelligence and Deep Learning to analyze Videos, Images, Audio files, Textual Data

    • Storing and manipulating structured data in RDBMS (MySQL)

    • Storing and manipulating the data in the NoSQL database (HBase)

    • Big Data Analytics using Spark (Also includes Pig, Hive, Scala)

    • Cloud Computing using AWS to store data on the cloud and build Machine Learning on Cloud

    • IoT sensors (Raspberry Pi, Arduino, Node MCU) and how to get the streaming data from sensors onto Cloud (Thing Cloud)

    • Business Intelligence and Data Visualization using Tableau

    • Six Sigma Green Belt

    • 15+ projects encompassing multiple industries from Kaggle or other Competition website so that results are updated on the LinkedIn profile as an accomplishment, 5 Projects and one project every month

  • One Capstone project (1 month) – India or Malaysia (Visa, Flight charges, Accommodation should be borne by students) Approximate cost will be 50K – 25 Days. Any unwarranted changes in the cost of flight or visa charges at the time of travel are beyond the control of ExcelR Solutions

Things You Will Learn


  • SQL Command
    • Data Query Language (DQL)
    • Data Manipulation Language (DML)
    • Data Definition Language (DDL)
    • Data Control Language (DCL)
  • SQL RDBMS Concept – Features & Advantages
    • Tables
    • Field
    • Record or a Row
    • Column
    • Constraints
  • RDBMS – Database Normalization
  • Primary and Foreign Keys
  • Index in RDBMS
  • SQL Data Types | Data Types in SQL Server
  • Clause in SQL 
    • WITH Clause 
    • SELECT Clause
    • FROM Clause
    • WHERE clause
    • GROUP BY clause
    • HAVING Clause
    • ORDER BY Clause
  • SQL Operators – Arithmetic, Comparison, & Logical
  • Operators in SQL – Alias, IN and Between
  • Create Database | SQL Drop & Select Database
  • SQL Join – Inner, Left, Right & Full Joins
  • SQL Index – Create, Unique, Composite Index
  • SQL Functions
    • Date
    • SUM
    • Count
  • Stored Procedure in SQL
  • Triggers in SQL

Description: You will get an introduction to the Python programming language and understand the importance of it. How to download and work with Python along with all the basics of Anaconda will be taught. You will also get a clear idea of downloading the various Python libraries and how to use them.


  • About ExcelR Solutions and Innodatatics
  • Do's and Don’ts as a participant
  • Introduction to Python
  • Installation of Anaconda Python
  • Difference between Python2 and Python3
  • Python Environment
  • Operators
  • Identifiers
  • Exception Handling (Error Handling)

Description: In this module, you will learn the basics such as assigning a variable, differences between dictionary, sets, tuple, and some decision making statements. Also, you will learn about working with different loops, data types and its usage.


  • Data Types
  • Conditional Statements
  • Functions
  • Loops

Description: This module helps you to learn and understand the different libraries used in Python. You will also get a clear idea about the NumPy library and how you can use it. NumPy is a Numeric Python library which helps in dealing with the numeric calculations with data frames.


  • NumPy Introduction
  • Arrays
  • Array Indexing
  • NumPy Data Types
  • Treating Missing and NA’s
  • Reshaping and combining Arrays

Description: In this module, you will learn how to download the Pandas package and syntax for the same. Pandas is also a library similar to Numpy which predominantly helps in working with series data and data frames. You will learn how to impute the data in the place of missing values called the missing value treatment done in the Pandas package itself.


  • Pandas Introduction
  • Basic Operations on Series
  • Dataframe
  • Working with Text Data
  • Working with Missing Data
  • Indexing and Selecting Data
  • Merge, Join and Concatenate

Description: In this module, you will learn where, how and when to use the Matplotlib library. This library is used to visualize the data. You will get an in-depth understanding of the importance of this library.


  • Introduction to Matplotlib
  • Matplotlib design and different visualizations

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.


  • Introduction to Seaborn Library
  • Visualizing the Distribution of the Datasets
  • Plotting the Categorical Data
  • Visualizing Linear Relationships
  • Visualizing Statistical Relationships

Description: In this module, you will understand the importance of both Scipy and Sklearn libraries which are predominantly used in building Machine Learning Algorithms working with Linear Equations. Sklearn also known as Scikit-learn, is a machine learning library for the Python programming language. You will get a clear idea of where you can use these libraries along with some examples.


  • Installing both SciPy and Sklearn Libraries
  • Introduction to SciPy (Mathematical Algorithms)
  • Introduction to Sklearn (Machine Learning Algorithms)

Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.


  • High-Level overview of Data Science / Machine Learning project management methodology
  • Videos for Data Collection - Surveys  and Design of Experiments will be provided
  • The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
  • Random Variable and its definition
  • Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts. Also, learn about some graphical techniques in Analytics.


  • Balanced vs Imbalanced datasets
  • Various sampling techniques for handling balanced vs imbalanced datasets
  • Videos for handling imbalanced data will be provided
  • What is Sampling Funnel, its application and its components
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • Measure of central tendency
    • Mean / Average
    • Median
    • Mode
  • Measure of Dispersion
    • Variance
    • Standard Deviation
    • Range
  • Expected value of probability distribution

Description: Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.


  • Measure of Skewness
  • Measure of Kurtosis
  • Various graphical techniques to understand data
    • Bar plot
    • Histogram
    • Box plot
    • Scatter plot
  • Introduction to R and RStudio  
  • Installation of Python IDE
  • Anaconda and Spyder
  • Working with Python and R with some basic commands

Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.


  • Normal Distribution
  • Standard Normal Distribution / Z distribution
  • Z scores and Z table
  • QQ Plot / Quantile-Quantile plot

Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.


  • Sampling Variation
  • Central Limit Theorem
  • Sample size calculator
  • T-distribution / Student's-t distribution
  • Confidence interval
    • Population parameter - Standard deviation known
    • Population parameter - Standard deviation unknown

Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.


  • Parametric vs Non-parametric tests
  • Formulating a Hypothesis
  • Choosing Null and Alternative hypothesis
  • Type I and Type II errors
  • Comparative study of sample proportions using Hypothesis testing
  • 2 sample t test

Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.


  • 1 sample t test
  • 1 sample z test
  • 2 Proportion test
  • Chi-Square test
  • Non-Parametric test

Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform tests using R and interpret the results.


  • Non-Parametric test continued
  • Hypothesis testing using Python and R

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.


  • Scatter diagram
    • Correlation Analysis
    • Correlation coefficient
  • Ordinary least squares
  • Principles of regression
  • Splitting the data into training, validation and testing datasets
  • Understanding Overfitting (Variance) vs Underfitting (Bias)
  • Generalization error and Regularization techniques
  • Introduction to Simple Linear Regression
  • Heteroscedasticity / Equal Variance

Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.     


  • LINE assumption
    • Collinearity (Variance Inflation Factor)
    • Linearity
    • Normality
  • Multiple Linear Regression
  • Model Quality metrics
  • Deletion diagnostics

Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.


  • Principles of Logistic Regression
  • Types of Logistic Regression
  • Assumption and Steps in Logistic Regression
  • Analysis of Simple Logistic Regression result

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.     


  • Multiple Logistic Regression
  • Confusion matrix
    • False Positive, False Negative
    • True Positive, True Negative
    • Sensitivity, Recall, Specificity, F1
  • Receiver operating characteristics curve (ROC curve)
  • Lift charts and Gain charts 

Description: Learn about the Discrete probability distribution. Types of Discrete probability distribution viz Binomial distribution, Poisson distribution and working with the probability distribution formula.


  • Binomial Distribution
  • Negative Binomial Distribution
  • Poisson Distribution

Description: Get introduced to various advanced regression techniques, especially regression analysis of count data namely Poisson Regression, Negative binomial regression. Learn when to use Poisson regression and Negative binomial regression for predicting count data.


  • Poisson Regression
  • Poisson Regression with Offset
  • Negative Binomial regression
  • Treatment of data with excessive zeros
    • Zero-inflated Poisson
    • Zero-inflated Negative Binomial
    • Hurdle model

Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.


  • Logit and Log Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables

Description: As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchal clustering, K means clustering using clustering examples and know what clustering machine learning is all about.



  • Supervised vs Unsupervised learning
  • Data Mining Process
  • Measure of distance
    • Numeric - Euclidean, Manhattan, Mahalanobis
    • Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
    • Mixed - Gower’s General Dissimilarity Coefficient
  • Types of Linkages
    • Single Linkage / Nearest Neighbour
    • Complete Linkage / Farthest Neighbour
    • Average Linkage
    • Centroid Linkage
  • Hierarchical Clustering / Agglomerative Clustering

Description: In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.


  • Non-clustering
    • K-Means Clustering
    • Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
    • Choosing the ideal K value using Scree plot / Elbow Curve
  • Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)

Description: Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and SVD.


  • Why dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix algebra
  • SVD – Decomposition of matrix data

Description: Under data mining unsupervised techniques, learn about Network Analytics and the measures used. Get introduced to Network Analysis tools like NodeXL.


  • Definition of a network (the LinkedIn analogy)
  • Measure of Node strength in a Network
    • Degree centrality
    • Closeness centrality
    • Eigenvector centrality
    • Adjacency matrix
    • Betweenness centrality
    • Cluster coefficient
  • Introduction to Google Page Ranking

Description: Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.


  • What is Market Basket / Affinity Analysis
  • Measure of association
    • Support
    • Confidence
    • Lift Ratio
  • Apriori Algorithm
  • Sequential Pattern Mining

Description: Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.


  • User-based collaborative filtering
  • Measure of distance / similarity between users
  • Driver for recommendation
  • Computation reduction techniques
  • Search based methods / Item to item collaborative filtering
  • SVD in recommendation
  • Vulnerability of recommender systems

Description: Learn about Machine Learning modeling using KNN, the K nearest neighbour algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms.


  • Deciding the K value
  • Building a KNN model by splitting the data
  • Understanding the various generalization and regulation techniques to avoid overfitting and underfitting

Description: The aim of this course is to understand what Data Visualization is all about. You will understand what are the best practices of Data Visualization, creating data visualization charts and understanding which visualization tools can be considered. Further, you will look into why you need to consider Tableau. You will also get an understanding of products in Tableau You will get an understanding as to what Data Visualization Principles are. Our course content is designed as per Tableau Certification. Edward Tufte considered as the father of Data Visualization came up with Data Integrity rules that need to be followed to get beautiful pieces of evidence.


  • Why visualization came into Picture
  • Importance of Visualizing Data
  • How Data is getting generated
  • Poor Visualizations Vs.Perfect Visualizations
  • Principles of Visualizations
  • Examples of Perfect Visualization
  • Tufte’s Graphical Integrity Rule
  • Tufte’s Principles for Analytical Design Visual

Description: You will get to know about Tableau Desktop, Tableau Server, Tableau Online, Tableau Prep and get an understanding on 14-day trial option i.e. the free version of Tableau - Tableau Public and how Tableau Public login works. You will also look into Tableau Public vs Tableau Desktop and glance on Tableau free download for Students. Get an understanding of Tableau Interactive Dashboards. You will also see what Tableau Reader is all about. You will see different types of Data. You will see what Tableau Architecture is and how it works. What Tableau Data source page is and how to customize the data is learnt here. You will understand what Discrete data and Continuous data are and their differences. You will also see how Data Interpretation works and what exactly happens to the data after interpretation. You will also get to understand the user interface on Tableau.


  • Products of Tableau
  • Tableau Public in detail
  • About Viz of the day, Viz of the week
  • Start Page on Tableau Desktop Professional
  • Tableau Architecture
  • Connecting to Data Source
  • Understanding on Data Source Page
  • Pivot Tables
  • Difference between Discrete data and Continuous data
  • Data Interpretation
  • Tableau User Interface

Description:  You will get a know around of the Charts in Tableau from the Show me Panel that is available in the Tableau work area. Text Table, hands-on understanding is given. Get an understanding of how Heat map for websites works. Check the Highlight tables in Tableau. You will see how Pie Charts are created. Also, see some Pie chart example like which do we use these pie chart. You will also see how the Bar charts are created. You will also see what are the other bar charts. Eg bar chart stacked, Side by side bar chart.


  • Text Tables, Totals
  • Highlight Tables
  • Heat Maps
  • Copy and Exporting the Data
  • Pie Chart
  • Bar Chart
  • Arbitrary Formatting of Colors
  • Conditional Formatting
  • Stacked Bars
  • Side by Sidebars
  • Tree Chart
  • Circle Chart
  • Side by side Circle chart

Description: If say the data is present in different sheets or different Data sources, then the need to learn how joins, Unions, Cross database joins tableau and data blending in tableau help us in connecting them.


  • Joins
  • Cross-Database Connections
  • Unions
  • Data Blending

Description: Learn how to create Filters in Tableau. You will find out the types of filters in Tableau and understand the hierarchy of filters, other filters like quick filters and context filters in Tableau. Get an exposure to how Extracts, Extracts Filters and Live data works in Tableau.


  • Extract Filters
  • Extract and Live Connections
  • Data Source Filters
  • Dimension Filters
  • Measures Filters
  • Date Filters
  • Various Options on Filters

Description: Understand what are Sets and Groups in Tableau. You will also get an understanding of sets vs groups in Tableau. Understand about folders and Tableau hierarchy. Get hands-on exposure on creating Folders, Groups, Sets Hierarchy in Tableau. You will be able to understand the parameters in Tableau that makes the visualization dynamic. Hands-on exposure to how parameters in tableau help come up with dynamic or interactive Dashboards.


  • Hierarchy
  • Folders
  • Grouping
  • Sets
  • Parameters

Description: Time series charts can also be created. Listed below are some of the Intermediate level charts that can be analysed. This helps you to work with multiple dimensions and multiple measures on the view area.


  • Time Series Charts (Line Chart)
  • Area Chart
  • Dual Line Chart
  • Dual Combination
  • Combination Chart

Description: You will understand about maps in Tableau. Also, see what is tableau map layers are and how to see latitude and longitude on google maps and customize geocoding. You will also understand what symbol maps and filled maps are in Tableau.


  • Symbol Maps
  • Filled Maps
  • Background images
  • Polygon Maps
  • Connecting to WMS Server
  • Lasso, Radial and Rectangular selection

Description: You will learn about the box and whisker plot. We generally refer this as box plots. You will learn about scatter plots and then see what is trend analysis and different models. You will understand what is predictive analysis and see how you can use predictive analysis in forecasting in Tableau. Learn how to create Histogram. Get an understanding of Funnel chart. We will learn about donut chart and how to create donut chart in Tableau. You will also learn about the waterfall chart. The other name of the waterfall model is the Gantt Chart. You will get a look into how Pareto chart is created, get an understanding of what Pareto analysis is before you get into the working. Understand the concepts of bullet charts.


  • Scatter Plot
  • Clustering
  • Trendlines
  • Box Plot
  • Histogram
  • Bullet Chart
  • Forecasting
  • Packed Bubble
  • Funnel Chart
  • Donut Chart
  • Waterfall Chart / Gantt Chart
  • Pareto Chart

Description: Look at how the calculations can be done using “Create Calculated Fields” option. Understand of how various Logical, String, Numerical, Ad-hoc Calculations and Quick Table Calculations can be done here. You will work on LoD in Tableau. Which means Level of Detail. This helps in build little more advanced calculations.


  • Logical Calculations
  • String Calculations
  • Numerical Calculations
  • Quick Table Calculations
  • Ad-hoc Calculation
  • LOD Expressions

Description: You will learn about Actions in Tableau and different actions like Filters in Tableau Dashboards. You will get an understanding of the Tableau server. You will also learn the concepts done on R tool implement in Tableau and see how the integration between these tools take place.


  • Integration between R and Tableau
  • Integration between Hadoop and Tableau
  • Dashboards and Actions
  • Story
  • Connecting Data to Tableau Server

Description: The key to any successful project accomplishment including analytics consulting projects would be to understand the business problem. Also, you should understand the initial activities to be performed in Data Science projects for solving business problems using Data Analytics.


  • Business Objectives
  • Business Constraints
  • Creating a Business Case
  • Components of Business Case
  • Creating Project Charter
  • Components of Business Case

Description: Understanding the various forms of collecting data and collecting the right data is of paramount importance for developing interesting insights in solving analytics problems. Deciding on the various market research techniques and ways of collecting data is pivotal to the success of Data Science projects.


  • Market Research using Secondary Data Sciences
  • Data Collection from Primary Data Sources
  • Performing Surveys and Questionnaire
  • Performing Experiments
  • Validating Data Quality

Description: Gathering the data alone is not sufficient, Data Scientists need to ensure that it is in a clean format. Exploring the data while performing data cleansing consumes a significant amount of time and allocating the right amount of effort towards these activities is very important.


  • Data cleansing including Outlier Analysis, Imputation, etc.
  • EDA to bring interesting Descriptive Analytics for actioning
  • Feature Engineering to get Derived Variables
  • Applying Domain Knowledge
  • Getting the final data for Predictive Modeling

Description: Determine whether Data Mining supervised learning or unsupervised learning is applicable for solving the business problem or do you need to implement a combination of both to solve the problem. Understanding what process has to be followed from selecting the right variables and algorithms required for solving a problem is learnt in this module.


  • Decide statistically on what are the most important variables
  • How to decide on which is the right technique / algorithm
  • Deciding on how to deal with balanced / imbalanced dataset
  • Deciding on highest accuracy model and high-performance model

Description: Learn how to close a Data Science project or Artificial Intelligence project and determine whether the purpose of the project success criteria is met or not. Deciding on how to deploy the solution at the client side is very important because all the hard work will be meaningless if customers do not get an easy way of viewing the solution and results.


  • Decide on the model deployment strategy - Web / Mobile / Etc.
  • How to gauge the project closure criteria
  • Performing Review and Retrospection
  • Deciding upon model maintenance and upgradation strategy

Description: Learn about how data is playing a key role in an organization. Data is the new oil that is the driving force for all industry, sectors and domains. With big data in the current world, organizations need to take leverage from Data to gain a competitive edge in real-time. Understand the need for Big Data tools, various components of Big Data, the architecture and the Big Data tools for processing.


  • Introduction to Big Data
  • Data, Data, Data Everywhere
  • 3 V’s of Big Data (Volume, Variety and Velocity)
  • Challenges with Big data
  • Need and significance of innovative technologies
  • What is Hadoop
  • History of Hadoop and its uses
  • Different components of Hadoop
  • Various Hadoop Distributions

Description: Learn about the three main components of Big Data Hadoop. Understand the Master / Slave architecture of Hadoop. Learn about the Demons of Storage component – HDFS and Processing component – MapReduce and finally learn about the resource manager which manages all the operations in the Hadoop Cluster.


  • Significance of HDFS in Hadoop
  • HDFS Features
  • Daemons of Hadoop and functionalities
  • Data Storage in HDFS
  • Accessing HDFS
  • Data Flow
  • HDFS commands hands-on
  • Introduction to MapReduce
  • MapReduce Architecture
  • Data Types
  • Input Splits and Records
  • Basic MapReduce Program
  • The MapReduce Web UI

Description: Learn about the first multi-user operating system - Linux and file system of Linux OS, Kernel, Interactive Shells, etc. Understand the usage of the Terminal and its commands. Learn about virtualization softwares like VMware and VirtualBox. Creation of a virtual Linux machine for Pseudo Hadoop Cluster setup


  • Virtualization
  • VMware Workstation
  • VirtualBox
  • Setup of Linux Virtual Machine
  • What is Linux OS
  • Flavours of Linux Os
  • Linux File System
  • Advantages of Linux Os
  • Hands-on Linux Terminal Commands

Description: Introduction to SQL like programming language on Big Data Hadoop over MapReduce.  Components of the Hive execution engine and the flow of the execution. Learn how different Data Warehousing tool - Apache Hive is with respect to SQL language.


  • Hive Engine and its Components
  • RDBMS Hive Metastore
  • Comparison with Traditional Databases
  • HiveQL
  • Hive Tables
  • Querying Data
  • User-Defined Functions

Description: Introduction to traditional Database system – RDBMS and its SQL programming language. Learn about NoSQL database (HBase) and its advantages. Learn how to move data from traditional Database to Big Data Hadoop system and vice versa using Apache Sqoop.


  • Introduction to MySQL
  • Basics of traditional RDBMS concepts
  • Difference between SQL and NoSQL (HBase)
  • Introduction to Sqoop
  • Benefits of Sqoop
  • Sqoop Architecture and Internals
  • MySQL client and server installation
  • How to connect to the relational database using Sqoop
  • Sqoop Commands

Description: Introduction to super-fast, memory based, cluster computing framework - Apache Spark. Components of the Unified Stack Apache Spark. Learn how Spark attains super speed over the Big Data residing in HDFS. Comparison between distributed frameworks - Hadoop and Spark. Learn what is RDD and its creation. Difference between Dataframe, Datasets and RDD in Apache Spark 2.X and their applications. Start writing Spark functions using multiple programming languages.


  • Introduction to Apache Spark
  • Apache Spark vs Hadoop
  • Spark Architecture
  • Spark Execution Environment - SparkContext, SQLContext, SparkSession
  • RDD and Operations on RDD’s
  • Spark Unified Stack
  • Spark Core
  • Spark SQL
  • Spark MLlib
  • Spark Streaming
  • PySpark (Spark using Python)
    • Introduction
    • SparkContext
    • RDDs
    • Broadcast & Accumulator
    • SparkConf
    • SparkFiles
    • StorageLevel
    • MLlib
    • Serializers


  • Understand the core Azure architectural components
    • describe Regions
    • describe Availability Zones
    • describe Resource Groups
    • describe Azure Resource manager
    • describe the benefits and usage of core Azure architectural components
  • Core products available in Azure
    • products available for Compute such as Virtual Machines, Virtual Machine Scale Sets, App Service and Functions
    • products available for Storage such as Blob Storage, Disk Storage, File Storage, and Archive Storage
    • products available for Databases such as CosmosDB, Azure SQL Database, Azure Database Migration service, and Azure SQL Data Warehouse
  • solutions available on Azure
    • Big Data and Analytics and products that are available for Big Data and Analytics such as SQL Data Warehouse, HDInsight and Data Lake Analytics
    • Artificial Intelligence (AI) and products that are available for AI such as Azure Machine Learning Service and Studio
  • Azure management tools
    • Azure CLI, PowerShell, and the Azure Portal
  • Overview of Azure Machine Learning studio

Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.


  • Probability – Recap    
  • Bayes Rule
  • Naive Bayes Classifier
  • Text Classification using Naive Bayes

Description: Bagging and Boosting is an ensemble technique which is a part of the random forest algorithm. Learn about Bagging and Boosting examples under this tutorial.


  • Boosting / Bootstrap Aggregating
  • AdaBoost / Adaptive Boosting
  • Stacking
  • Gradient Boosting
  • Extreme Gradient Boosting (XGB)

Description: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples and Random Forest algorithms.


  • Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information Gain
  • Ensemble techniques
  • Decision Tree C5.0 and understanding various arguments
  • Random Forest and understanding various arguments

Description: Artificial Neural Network and Support Vector Machines are the two powerful Deep learning algorithms. Get introduced to Neural Net, Convolutional Neural Network, Recurrent Neural Network. Learn how to work with Support Vector Machine, SVM classifiers and SVM regression.


  • Artificial Neural Network
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Activation function
  • Network Topology
  • Support Vector Machines
  • Classification Hyperplanes
  • Best fit “boundary”
  • Kernel Trick

Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.


  • Sources of data
  • Bag of words
  • Pre-processing, corpus Document-Term Matrix (DTM) and TDM
  • Word Clouds
  • Corpus level word clouds
    • Sentiment Analysis
    • Positive Word clouds
    • Negative word clouds
    • Unigram, Bigram, Trigram
  • Semantic network
  • Clustering

Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.     


  • Extract Tweets from Twitter
  • Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor

Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and  


  • Install Libraries from Shell
  • Extraction and text analytics in Python

Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.


  • LDA
  • Topic Modeling 
  • Sentiment Extraction
  • Lexicons and Emotion Mining

Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.


  • Introduction to time series data
  • Steps of forecasting
  • Components of time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naive forecast methods
  • Errors in forecast and its metrics
  • Model Based approaches
    • Linear Model
    • Exponential Model
    • Quadratic Model
    • Additive Seasonality
    • Multiplicative Seasonality
  • Model-Based approaches
  • AR (Auto-Regressive) model for errors
  • Random walk
  • ARMA (Auto-Regressive Moving Average), Order p and q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
  • Data-driven approach to forecasting
  • Smoothing techniques
    • Moving Average
    • Exponential Smoothing
    • Holts / Double Exponential Smoothing
    • Winters / HoltWinters
  • De-seasoning and de-trending
  • Econometric Models
  • Forecasting Best Practices
  • Forecasting using Python
  • Forecasting using R


  • SLR
  • MLR
  • SVM
  • PCA
  • KNN
    • Amazon Review Extraction
    • TripAdvisor
    • IMDB Review Extraction
    • Snapdeal Review Extraction

Project 1: How To Identify Fraudulent And Ilegal Transactions Due To Insider Trading
Project Related To:
Finance Service Insurance
Problem Description: In spite of the mature regulatory norms, the act of insider trading is on the rise. More robust the regulatory norms become, more intelligent the insider traders become. This is forcing the firms to always be on toes and keep developing better ways of identifying the fraud. Insider trading gives away the secrets of the organizations, which are strictly not to be disclosed outside the boardroom. The ugly politics of companies, which cannot get head-on with the ethical businesses are heavily resorting to these ways of mending the rules to make this way to success. How do you identify the sheep in wolves’ clothing?


Project 2: Learn On How To Predict The Deposits Churn And Reduce The Risk Of Losing Customers
Project Related To:
Finance Service
Problem Description: Considerably, alongside growing the customer base, not maintaining sufficient funds as deposit amounts could lead to levying penalty and this could, in turn, lead to customer churn.
a) How to devise strategies in retaining customers and also ensuring that they maintain required funds in deposits or increase the funds in deposits
b) How to predict on who is the most probable customer to churn
c) How to find out about customers who will continue to stay despite levying penalty for maintaining an amount below the par in the deposit accounts
d) How to segment customers and devise business strategies for each of these segments
e) These are the challenges for which banks need an immediate solution.


Project 3: Want To Know On How Sentiment Analysis Is Performed From Twitter’s Unstructured Data
Project Related To: Social Media Analytics
Project Description: With the increase in digitization, the amount of accessibility to social media for a common person has increased manifold. The Advent of technology not only comes with the advantages but also the disadvantages. Many people who have access to the internet do not restrain from giving to-the-point feedbacks and are not at all shying away. But sometimes, these reviews and feedbacks are given only because of the unhealthy competition.

At times, this is creating a lot of trouble to the genuine products and manufacturers, risking them to drop the plans of manufacturing those products. It also results in dropping of rating of those products.


Project 4: How To Increase The Probability Of ‘Click-Through Rate’ Of Ads Posted On Social Media
Project Related To: Social Media Analytics
Problem Description: The world is now experiencing the highest internet penetration ever. Companies without proper online presence hardly survive. In this context, increasing online visibility, especially when netizens perform a search on search engines is at its prime. There is fierce competition among companies to feature on the first page and being on the top of the search results; this is because people hardly ever move to pages beyond the first page to explore the results. Both top-line and bottom-line of companies are now greatly dependent on Social Media Presence.
a) How prominently your website appears in search results.
b) What should be done to be on the first page


Project 5: Analytics On Political Party Representatives
Project Related To: Social Media Analytics
Project Description: Citizens are resorting to posting messages on social media and the web to vent out the frustration or happiness associated with the daily activities going around. There is no transparency on how many promises were done by political party members at the time of the election. Lack of clarity on the performance of the elected representatives leading to some sections misguiding the society with false claims.


Project Related To: Retail Organizations
Project Description: There is an ever-increasing focus on effective recruitment. An organization invests a lot of its time and resources in search of the potential candidates. The investment become loses if the selected candidates do not join the organization in the end.


Project 7: Warranty Claims
Project Related To: Retail sector
Project Description: Analysis to predict an item when sold, what is the probability that a customer would file for warranty and to understand important factors associated with them.


Project 8: Performance Prediction For Teachers and Students
Project Related To: Retail Sector
Project Description: Educational Data Mining (EDM) aims at knowledge discovery by applying mining techniques to identify hidden knowledge and patterns about students and teachers performance. The idea is to help improve performance by taking appropriate action based on the prediction. Early prediction helps in devising appropriate solutions to draw better results for both students and teachers.


Project 9: Students School Dropouts
Project Related To: Retail Sectors
Project Description: Educational Data Mining (EDM) aims at knowledge discovery by applying mining techniques to identify hidden knowledge and patterns about students dropouts from primary schools. The idea is to help improve the overall quality of primary education by taking appropriate action based on the prediction in school dropouts. Early prediction helps in devising appropriate solutions to help schools address students dropout.


Project 10: Chat Bot
Project Related To: Retail Sector
Project Description: Digitization is penetrating into the remote parts of even the third world in recent times. With the advent of advanced technology and digitization, the data that is being generated is very huge and the number of hands asking for queries on customer product and services is increasing at a rapid pace. Keeping the current and future demand in mind, it will and is becoming a challenging task for the clients to satisfy their customers in responding to their queries.


Project 11: How To Bring Data From Varied Sources To Generate Reports For Businesses To Draw Insights To Devise Strategies
Project Related To: Business Intelligence and Reporting
Project Description:

  • Analytical capability of the reporting tool (Tableau) helps in drawing significant insights to make swift decisions
  • Aesthetic visual pop coupled with analytics feature helps in knowing the potential of the data
  • The extremely easy-to-integrate feature of reporting and analytical tool helps in collaborating data from varied sources, giving scope for robust visualization.
  • The development of visually attractive reports of dashboard combines many sheets in single place giving room for faster analysis


Project 12: How To Generate A Single Report Personalized To Various Departments Using View Security Settings On Server End
Project Related To: Business Intelligence and Reporting
Project Description:

  • Increased efficiency among departments
  • Reduced Data leakages resulted in huge cost savings
  • Parallel reports enhanced the resolution capability at a low time
  • Actionable Insights are derived at a faster rate, resulting in profit generation


Project 13: How To Connect Big Data Source Engines To Tableau And Establish Dashboard Reporting Through Streaming Data
Project Related To: Business Intelligence and Reporting
Project Description: Want to know how to connect big data source engines to Tableau and establish dashboard reporting through streaming data.


Module 1 - Introduction to AI and DL

Description: Understand what is Artificial Intelligence and Deep Learning. Understand about the various job opportunities for AI and DL. You will learn about the most important Python libraries for building AI applications. These MUST KNOW Python libraries for Deep Learning are being upgraded on an extremely rapid pace and keeping abreast of the changes is pivotal for the success of AI experts.


  • Introduction to Artificial Intelligence and Deep Learning
  • Applications of AI in various industries
  • Introduction to the installation of Anaconda
  • Creating of Environment with stable Python version
  • Introduction to TensorFlow, Keras, OpenCV, Caffe, Theano
  • Installation of required libraries

Description: Understand about the various mathematical concepts which are important to learn AI implementations. These concepts will help to understand Deep Learning concepts in detail. It will also serve as a refresher for learning various Neural Network algorithms, which are synonymous to Deep Learning.


  • Introduction to Data Optimization
  • Calculus and Derivatives Primer
  • Finding Maxima and Minima using Derivatives in Data Optimization
  • Data Optimization in Minimizing errors in Linear Regression
  • Gradient Descent Optimization
  • Linear Algebra Primer
  • Probability Primer

Description: Understand about the basics of the first algorithm – Perceptron Algorithm, its drawbacks and how we can overcome those challenges using Artificial Neural Network or Multilayer Perceptron Algorithm. The various activation functions will be understood in detail and practical exposure to R programming and Python programming is the highlight of this module.


  • Understand the history of Neural Networks
  • Learn about Perceptron algorithm
  • Understand about Backpropagation Algorithm to update weight
  • Drawbacks of Perceptron Algorithm
  • Introduction to Artificial Neural Networks or Multilayer Perceptron
  • Manual calculation of updating weights of final layer and hidden layers in MLP
  • Understanding of various Activation Functions
  • R code and Python code to understand about practical model building using MNIST dataset

Description: Learn about the various Error functions, which are also called Cost functions or Loss functions. Also, understand about the entropy and its use in measuring error. Understand the various optimization techniques, drawbacks and ways to overcome the same. This you will learn alongside various terms in implementing neural networks.


  • Understand about challenges in Gradient
  • Introduction to various Error, Cost, Loss functions
  • ME, MAD, MSE, RMSE, MPE, MAPE, Entropy, Cross Entropy
  • Vanishing / Exploding Gradient
  • Learning Rate (Eta), Decay Parameter, Iteration, Epoch
  • Variants of Gradient Descent
    • Batch Gradient Descent (BGD)
    • Stochastic Gradient Descent (SGD)
    • Mini-batch Stochastic Gradient Descent (Mini-batch SGD)
  • Techniques to overcome challenges of Mini-batch SGD
    • Momentum
    • Nesterov Momentum
    • Adagrad (Adaptive Gradient Learning)
    • Adadelta (Adaptive Delta)
    • RMSProp (Root Mean Squared Propagated)
    • Adam (Adaptive Moment Estimation)

Description: Learn about practical applications of MLP when output variable is continuous and discrete in two categories and multi-category. Understand also about handling balanced vs imbalanced datasets. Learn about techniques to avoid overfitting and various weight initialization techniques.


  • Binary classification problem using MLP on IMDB dataset
  • Multi-class classification problem using MLP on Reuters dataset
  • Regression problem using MLP on Boston Housing dataset
  • Types of Machine Learning outcomes – Self-supervised, Reinforcement Learning, etc.
  • Handling imbalanced datasets and avoiding overfitting and underfitting
  • Simple hold-out validation
    • K-Fold validation
    • Iterated K-fold validation with shuffling
    • Adding weight regularization
      • L1 regularization
      • L2 regularization
    • Drop Out and Drop Connect
    • Early Stopping
    • Adding Noise – Data Noise, Label Noise, Gradient Noise
    • Batch Normalization
    • Data Augmentation
  • Weight initialization techniques
    • Xavier, Glorot, Caffe, He

Description: Though CNN has replaced most of the computer vision and image processing concepts, a few application require the knowledge of Computer vision. We will learn about the application using the defacto library OpenCV for image processing. How to build machine learning models when we have limited data is explained as part of this module.


  • Understanding about Computer Vision related applications
  • Various challenges in handling Images and Videos
  • Images to Pixel using Gray Scale and Color images
  • Color Spaces – RGB, YUV, HSV
  • Image Transformations – Affine, Projective, Image Warping
  • Image Operations – Point, Local, Global
  • Image Translation, Rotation, Scaling
  • Image Filtering – Linear Filtering, Non-Linear Filtering, Sharpening Filters
  • Smoothing / Blurring Filters – Mean / Average Filters, Gaussian Filters
  • Embossing, Erosion, Dilation
  • Convolution vs Cross-correlation
  • Boundary Effects, Padding – Zero, Wrap, Clamp, Mirror
  • Template Matching and Orientation of image
  • Edge Detection Filters – Sobel, Laplacian, LoG (Laplacian of Gaussian)
  • Bilateral Filters
  • Canny Edge Detector, Non-maximum Suppression, Hysteresis Thresholding
  • Image Sampling – Sub-sampling, Down-sampling
  • Aliasing, Nyquist rate, Image pyramid
  • Image Up-sampling, Interpolation – Linear, Bilinear, Cubic
  • Detecting Face and eyes in the Video
  • Identifying the interest points, key points
  • Identifying corner points using Harris and Shi-Tomasi Corner Detector
  • Interest point detector algorithms
    • Scale-invariant feature transform (SIFT)
    • Speeded-up robust features (SURF)
    • Features from accelerated segment test (FAST)
    • Binary robust independent elementary features (BRIEF)
    • Oriented FAST and Rotated Brief (ORB)
  • Reducing the size of images using Seam Carving
  • Contour Analysis, Shape Matching and Image segmentation
  • Object Tracking, Object Recognition

Description: Understand about the various layers of CNN and understand how to build the CNN model from scratch as well as how to leverage upon the CNN model which is pre-trained. Understand about the best practices in building CNN algorithm and variants of convolution neural network.


  • Understand about various Image related applications
  • Understanding about Convolution Layer and Max-Pooling
  • Practical application when we have small data
  • Building the Convolution Network
  • Pre-processing the data and Performing Data Augmentation
  • Using pre-trained ConvNet models rather than building from scratch
  • Feature Extraction with and without Data Augmentation
  • How to Visualize the outputs of the various Hidden Layers
  • How to Visualize the activation layer outputs and heatmaps

Description: Understand about how to deal with sequence data including textual data as well as time series data and audio processing. Understand about advanced RNN variant models including LSTM algorithm and GRU algorithm. Also learn about bidirectional RNN, LSTM and deep bidirectional RNN and LSTM. Learn about various unstructured textual pre-processing techniques.


  • Understand about textual data
  • Pre-processing data using words and characters
  • Perform word embeddings by incorporating the embedding layer
  • How to use pre-trained word embeddings
  • Introduction to RNNs – Recurrent layers
  • Understanding LSTM and GRU networks and associated layers
  • Hands-on use case using RNN, LSTM, and GRU
  • Recurrent dropout, Stacking recurrent layers, Bidirectional recurrent layers
  • Solving forecasting problem using RNN
  • Processing sequential data using ConvNets rather than RNN (1D CNN)
  • Building models by combining CNN and RNN

Description: Understand about unsupervised learning algorithm such as GAN as well as Autoencoders. GANs are used extensively in artificially generating speech, images which can be used in computer games. Deep Dream is such an algorithm which using GAN to generate images. Autoencoders will take input as an image and traverse through the network and then regenerates the same image. Learn about how these intermediate layer representations can be used in other neural network deep learning models.


  • Text generation using LSTM and generative recurrent networks
  • Understanding about DeepDream algorithm
  • Image generation using variational autoencoders
  • GANs theory and practical models
  • The Generator, the Discriminator, the Adversarial network
  • Deep Convolution Generative Adversarial networks
  • Producing audio using GAN
  • Unsupervised learning using Autoencoders

Description: Reinforcement learning is majorly used in AI-based games. Q-learning is one such Reinforcement machine learning algorithm which is using in game theory. Finally, any of the ongoing Kaggle competition will be the prime focus and to be in the top 100 will be of prime importance. This will bring optimal visibility of the profiles to the employers and participants can be directly hired.


  • Q-learning
  • Exploration and Exploitation
  • Experience Replay
  • Model Ensembling
  • Final project using a live Kaggle competition
  • What is Big Data
  • Need and significance of innovative technologies
  • 3 Vs (Characteristics)
  • Forms of Data & Sources
  • Various Hadoop Distributions
  • Significance of HDFS in Hadoop
  • HDFS Features
  • Daemons of Hadoop and functionalities
  • Data Storage in HDFS
  • Accessing HDFS
  • Data Flow
  • Hadoop Archives
  • Introduction to MapReduce
  • MapReduce Architecture
  • MapReduce Programming Model
  • MapReduce Algorithm and Phases
  • Data Types
  • Input Splits and Records
  • Basic MapReduce Program
  • Introduction to Apache Pig
  • MapReduce Vs. Apache Pig
  • SQL Vs. Apache Pig
  • Different Data types in Apache Pig
  • Modes of Execution in Apache Pig
  • Execution Mechanism
  • Data Processing Operators
  • How to write a simple PIG Script
  • UDFs in PIG
  • The Metastore
  • Comparison with Traditional Databases
  • HiveQL
  • Tables
  • Querying Data
  • User-Defined Functions
  • Introduction to HBase
  • HBase Vs HDFS
  • Use Cases
  • Basics Concepts
  • HBase Architecture
  • Zookeeper
  • Clients
  • MapReduce integration
  • MapReduce over HBase
  • Schema definition
  • Basics of MySQL database
  • Install and Configuration
  • Load/Update/Delete – DML transactions on database
  • Import and Export data
  • Other MySQL functions
  • Introduction to Sqoop
  • Sqoop Architecture and Internals
  • MySQL client and server installation
  • How to connect relational database using Sqoop
  • Sqoop Commands
  • overview
  • Installation
  • The basic syntax 
  • Data types 
  • Programming practice
  • Basics of Python
  • Variables, expressions and statements
  • Functions, Structures, Strings
  • Strings and Files
  • Basic visualizations
  • Basic Statistics
  • Spark Architecture (Eco System)
  • SparkR setup
  • Pyspark and Spark-Shell (scala) interfaces
  • Spark SQL
  • Spark MLLib
  • Spark Streaming
  • Why Visualization came into Picture?
  • Importance of Visualizing Data
  • Poor Visualizations Vs. Perfect Visualizations
  • Principles of Visualizations
  • Tufte’s Graphical Integrity Rule
  • Tufte’s Principles for Analytical Design
  • Visual Rhetoric
  • Goal of Data Visualization
  • Introduction to Tableau
  • What is Tableau? Different Products and their functioning
  • Architecture Of Tableau
  • Pivot Tables
  • Split Tables
  • Hiding
  • Rename and Aliases
  • Data Interpretation
  • Understanding about Data Types and Visual Cues
  • Text Tables, Highlight Tables, Heat Map
  • Pie Chart, Tree Chart
  • Bar Charts, Circle Charts
  • Time Series Charts
  • Time Series Hands-On
  • Dual Lines
  • Dual Combination
  • Bullet Chart
  • Scatter Plot
  • Introduction to Correlation Analysis
  • Introduction to Regression Analysis
  • Trendlines
  • Histograms
  • Bin Sizes in Tableau
  • Box Plot
  • Pareto Chart
  • Donut Chart, Word Cloud
  • Forecasting ( Predictive Analysis)
  • Types of Maps in Tableau
  • Polygon Maps
  • Connecting with WMS Server
  • Custom Geo coding
  • Data Layers
  • Radial & Lasso Selection
  • How to get Background Image and highlight the data on it
  • Creating Data Extracts
  • Filters and their working at different levels 
  • Usage of Filters on at Extract and Data Source level
  • Worksheet level filters
  • Context, Dimension Measures Filter
  • Joins
  • Unions
  • Data Blending
  • Cross Database Joins
  • Sets
  • Groups
  • Parameters
  • Logical Functions
  • Case-If Function
  • ZN Function
  • Else-If Function
  • Ad-Hoc Calculations
  • Quick Table Calculations
  • Level of Detail (LoD)
  • Fixed LoD
  • Include LoD
  • Exclude LoD
  • Dashboards
  • Actions at Sheet level and Dashboard level
  • Story
  • Publishing our Workbooks in Tableau Server
  • Publishing dataset on to Tableau Server
  • Setting Permissions on Tableau Server
  • What is R?
  • How to integrate Tableau with R?
  • Tableau Prep
  • Concepts & Definitions 
  • Myth with IoT
  • Business with IoT
  • Carrier in IoT
  • IoT Applications
  • IoT system overview 
  • Node, Gateway, Clouds 
  • Why IoT is essential
  • Machine learning
  • Artificial Intelligence
  • IoT Network Architecture
  • IoT Device Architecture
  • IoT Device Architecture
  • Publish-Subscribe architecture
  • Sensors – Classification & selection criteria based on the nature, frequency and amplitude of the signal
  • Embedded Development Boards – Arduino, Raspberry Pi, Intel Galileo, ESP8266
  • Wired Communication Protocols
  • Wireless Communication Protocols 
  • Application Protocols – MQTT, CoAP, HTTP, AMQP
  • Transport layer protocols – TCP vs UDP
  • IP- IPv4 vs IPv6
  • Concept & Architecture of Cloud
  • Public cloud vs Private cloud
  • Different Services in cloud (IAAS / PAAS / SAAS)
  • Importance of Cloud Computing in IOT
  • Leveraging different Cloud platforms.
  • Interfacing peripherals & Programming GPIOs – Input/output peripherals, Sensor modules
  • Design Considerations – Cost, Performance & Power Consumption tradeoffs
  • Embedded C
  • Python
  • Arduino
  • Setting up board
  • Booting up Raspberry Pi
  • Running python on Raspberry Pi, GPIO programming
  • Interfacing sensors and LED (Input and output devices)
  • Making a few projects
  • Sending data to cloud 2 using Raspberry Pi board
  • Sending data to cloud 3 using Raspberry Pi board
  • Making raspberry Pi web server
  • Making raspberry PI TCP client and server
  • Making raspberry Pi UDP client and server
  • A cloud-based temperature monitoring system using Arduino and Node MCU
  • Esp8266 WIFI controlled Home automation
  • Obstacle detection using IR sensor and Arduino
  • Remote controlling with Node MCU
  • Temperature monitoring using a Raspberry Pi as local server
  • Raspberry Pi controlling Esp8266 using MQTT
  • weather monitoring system using Raspberry Pi and Microsoft Azure cloud
  • Existing Product in Market
  • Barrier in IoT


Module 1 - About Cloud Technology
  • Cloud Computing Technology & its Concepts
  • Comparison between On-Premise & Cloud Infrastructure
  • Various Advantages of Cloud Technology
  • Types of Cloud Services being offered.
  • Evolution of Amazon Web Services
  • E Chronology & Events of AWS Cloud
  • EGlobal Clients of AWS Cloud
  • A Region and Availability Zone
  • About Edge Locations
  • AWS Cloud Legal & Compliance Overview
  • Elastic Compute Cloud Essentials
  • Configure and Deploy EC2 instances.
  • Types of instances offered by AWS in EC2
  • Working with Amazon Machine Image
  • Elastic Block Store Volumes Use Cases
  • EBS based Snapshot
  • Elastic IP Addressing
  • Feature of Elastic Compute Cloud
  • AWS Pricing & Calculating
  • About Autoscaling & Use Cases
  • Introduction to AWS Cloud Networking services
  • Virtual Private Cloud Setup
  • Public & Private Subnets Creation within a VPC
  • Configuring Internet Gateway
  • Network Address Translation (NAT) Gateway
  • Use Case of NAT Gateway
  • Establishing Connection between two VPCs through VPC Peering
  • About Cloud Front and ways to Configure it.
  • Simple Storage Service (S3)
  • Creating S3 Bucket.
  • Storages Classes in S3 Bucket
  • Versioning in S3
  • Static Website Hosting
  • Cross Region Replication of Data through S3
  • AWS Elastic File System & its Advantages
  • Configuring EFS and its Use Case
  • AWS Glacier
  • About RDS
  • Deploying RDS Instance & Configuring it.
  • Amazon Dynamo DB
  • Identity and Access Management (IAM)
  • Creation of Users & Groups in IAM
  • Authorization & Authentication for Users & Groups
  • Multi-Factor Authentication using MFA Device
  • Features of Route 53
  • Configuring AWS Route 53
  • AWS Cloud Watch
  • Simple Notification Service (SNS)
  • Amazon Simple Queue Service (SQS)
  • What is SIX SIGMA
  • About SIX SIGMA Exam and Pre-requisites
  • Enterprise-wide view
  • Leadership
  • Overview of Six Sigma
  • Overview of DMAIC Methodology
  • Impact on stakeholders
  • Critical to x (CTx) requirements C -Benchmarking
  • Business performance measures E -Financial measures
  • Team formation
  • Team facilitation
  • Team dynamics
  • Time management for teams E -Team decision-making tools
  • Management and planning tools
  • Team performance evaluation and reward
  • Voice of the customer
  • Project charter
  • Project tracking
  • Process characteristics
  • Data collection
  • Measurement systems
  • Basic statistics
  • Probability
  • Process capability
  • Measuring and modeling relationships between variables
  • Hypothesis testing
  • Failure mode and effects analysis (FMEA)
  • Additional analysis methods
  • Design of experiments (DOE)
  • Waste elimination
  • Cycle-time reduction
  • Kaizen and kaizen blitz
  • Theory of constraints (TOC)
  • Implementation
  • Risk analysis and mitigation
  • Statistical process control (SPC)
  • Other control tools
  • Maintain controls
  • Sustain improvements
  • Common DFSS methodologies
  • Design for X (DFX)
  • Robust design and process
  • Special design tools
  • Linkages of Six Sigma to CMMI
  • Agile Manifesto and Principles
  • Project Charter for Agile Project
  • Agile Methodology
  • Agile Principles
  • Agile Frameworks and Terminology 
  • Team Space
  • Information Radiator 
  • Agile Tooling
  • Daily Stand-ups
  • Osmotic Communication 
  • Iteration and Release Planning
  • Progressive Elaboration
  • Time Boxing
  • Cumulative Flow Diagram
  • Kanban Boards 
  • WIP Limits
  • Burn Charts  
  • Retrospectives
  • Innovation Games 
  • Relative Sizing 
  • Story Points
  • Wideband Delphi Technique
  • Planning Poker 
  • Affinity Diagram
  • Ideal time
  • Velocity
  • Cycle Time 
  • EVM
  • Escaped Defects
  • Product Roadmap
  • Backlog 
  • Story Maps
  • Agile Modeling
  • Wireframes
  • Charting
  • Personas
  • Agile Modeling
  • Charting
  • Wireframes
  • Personas
  • Frequent Verification and Validation–
  • Test Driven Development
  • Definition of Done
  • Continues Integration
  • Feedback Techniques
  • Incremental Delivery
  • Continuous Improvement
  • Customer Valued Prioritization
  • Compliance
  • Relative Prioritization
  • Value Stream Mapping
  • Minimum Marketable Feature
  • Motivational Theories
  • What is Human Resource Management?
  • Plan Human Resource Management
  • Acquire Project Team
  • Develop Project Team
  • Manage Project Team
  • Mock Test
  • Risk Adjusted backlog
  • Risk Burn down charts
  • Risk based spike
  • Vendor Management
  • Failure Mode analysis
  • Level 1, Level 2, Level 3
  • Certification Overview
  • Exam Preparation Tips
  • Discussion or Recommended Books

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Why ExcelR?


  • The all new and exclusive JUMBO PASS is the latest initiative taken by ExcelR to offer you access to attend unlimited batches over the course of 365 days. The JUMBO PASS is course specific. You will be able to attend unlimited number of classes for the course of your choice.
  • 100% job guarantee.
  • Option of International Immersion program
  • Certification exams part of the curriculum
  • Paid internship opportunities
  • Training in Technical as well as managerial skills
  • Exposure to an amazing talent pool of trainers and consultants
  • Academic qualification
  • Minimum 2 years of relevant work experience
  • An original statement of purpose (500 to 800 words)
  • Performance in entrance test (reasoning skills)
  • Personal interview
  • Keeping in view that the richness of communication is in the face to face mode of learning, ExcelR will be offering the course in the classroom mode at our 3 major locations Hyderabad, Bangalore, Pune and 17 franchise centres, however, participants from a geographically distant location can connect live to the online classes. The trainer shall enable the webcam so as to provide a better interactive experience.
  • We understand that the human brain can grasp only so much. Hence, we record all the sessions and provide access to the e-learning to the trainees for the rest of their life.
  • Yes and No. Yes in the sense programming skills would be required & No in the sense one need not have extremely strong programming skills. However, we at ExcelR ensure that you get sufficient exposure to the statistical programming tool called ‘R’. We start right from the basics assuming you do not have any exposure towards programming.
  • R has approximately 50% market share & it is open source (free of cost). Hence, R is very lucrative in the analytics space. Almost all the jobs are asking for experience & exposure in R. Demand for other statistical tools is decreasing steadily & hence it is recommended to be futuristic and invest time in learning R.
  • Salaries range varies based on experience, industry, domain, geography & various other parameters. However, as a general thumb rule, we can apply the following formula:
  •  Salary = No. of years of experience * 3 Lakhs per annum (India – INR)
  •  Salary = No. of years of experience * $1200 to $1500 per annum (Overseas – USD)
  • The training is a live instructor-led interactive session done at a specific time where participants and trainer will log in at the same time. The same session will be also recorded and access will be provided to revise, recap or watch a missed session.
  • You can reach out to us by visiting our website and interact with our live chat support team. Our customer service representatives will assist you with all your queries. You can also send us an email at [email protected] with your query and our Subject Matter Experts / Sales Team will clarify your queries or call us on 1800-212-2120 (Toll-Free number – India), 608-218-3798 (USA), 800 800 9706 (India), 203-514-6638 (United Kingdom), 128-520-3240 (Australia).
  • Yes, after successfully completing the course you will be awarded a course completion certificate from ExcelR.
  • It is the science of developing intelligent computer programs which can understand human intelligence.
  • Intelligence is the computational part of the ability to achieve goals in the world.
  • You can expect jobs both in both the public and private sectors.
  • ExcelR provides real-world skills that keep pace with AI industry and gives you the flexibility to master skills at own pace.
  • Basic knowledge of mathematics, programming concepts and a sense of curiosity and willingness to learn AI.
  • We offer this course in the below formats
    • Live Virtual / Online Classroom
    • Online Self-Learning
    • Classroom Training
  • Instructor-led online training is an interactive mode of training where participants and trainer will log in at the same time and live sessions will be done virtually. These sessions will provide scope for active interaction between you and the trainer.
  • ExcelR offers a blended model of learning. In this model, you can attend classroom, instructor-led live online and e-learning (recorded sessions) with a single enrolment. A combination of these 3 will produce a synergistic impact on the learning. You can attend multiple Instructor-led live online sessions for one year from different trainers at no additional cost with the all new and exclusive JUMBO PASS.
  • Not a problem even if you miss a live Artificial Intelligence session for some reason. Every session will be recorded and access will be given to all the videos on ExcelR’s state-of-the-art Learning Management System (LMS). You can watch the recorded Artificial Intelligence sessions at your own pace and convenience.
  • Yes, for our online training programs we do offer group discounts. For further details, please reach out to us at [email protected]
  • Yes, after successfully completing the course you will be awarded a course completion certificate from ExcelR.
  • You can reach out to us by visiting our website and interact with our live chat support team. Our customer service representatives will assist you with all your queries. You can also send us an email at [email protected] with your query. Our Subject Matter Experts / Sales Team will clarify your queries or call us on 1800-212-2120 (Toll-Free number – India), 608-218-3798 (USA), 800 800 9706 (India), 203-514-6638 (United Kingdom), 128-520-3240 (Australia).
  • The different payment methods accepted by us are
    • Cash
    • Net Banking
    • Cheque
    • Debit Card
    • Credit Card
    • PayPal
    • Visa
    • Mastercard
    • American Express
    • Discover

Global Presence

ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.

ExcelR's Global Presence


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