Data Science Master's Program

·      Introduction to Excel spreadsheet

·      Learning to enter data

·      Flling of series and custom fill list

·      Editing and deleting field

·      Practical Exercise              

·      Learning about relative and absolute referencing

·      The concept of relative formulae

·      The issues in relative formulae

·      Creating of absolute and mixed references

·      Practical Exercise              

·      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

·      Working with Name Manager

·      Practical Exercise              

·      The various logical functions in Excel

·      The If function for calculating values and displaying text

·      Nested If functions

·      VLookUp and IFError functions

·      Practical Exercise              

·      Learning about conditional formatting

·      The options for formatting cells

·      Various operations with icon sets

·      Data bars and color scales

·      Creating and modifying sparklines

·      Practical Exercise              

·      Multi-level drop down validation

·      Restricting value from list only

·      Learning about error messages and cell drop down

·      Practical Exercise              

·      Introduction to the various formulae in Excel

·      Sum, SumIF & SumIFs

·      Count, CountA, CountIF and CountBlank

·      Networkdays, Networkdays International

·      Today & Now function

·      Trim (Eliminating undesirable spaces)

·      Concatenate (Consolidating columns)

·      Practical Exercise              

·      Introduction to dynamic table in Excel

·      Data conversion

·      Table conversion

·      Tables for charts

·      VLOOKUP

·      Practical Exercise              

·      Sorting in Excel

·      Various types of sorting

·      Alphabetical

·      Numerical

·      Row

·      Multiple column

·      Working with paste special

·      Hyperlinking

·      Using subtotal

·      Practical Exercise              

·      The concept of data filtering

·      Understanding compound filter and its creation

·      Removing of filter

·      Using custom filter and multiple value filters

·      Working with wildcards

·      Practical Exercise              

·      Creation of Charts in Excel

·      Performing operations in embedded chart

·      Modifying

·      Resizing

·      Dragging of chart

·      Practical Exercise              

·      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

·      Practical Exercise              

·      The concept of Pivot tables in Excel

·      Report filtering

·      Shell creation

·      Working with Pivot for calculations

·      Formatting of reports

·      Dynamic range assigning

·      The slicers

·      Creating of slicers

·      Practical Exercise              

·      Data and file security in Excel

·      Protecting row, column, and cell

·      Different safeguarding techniques

·      Practical Exercise              

·      Learning about VBA macros in Excel

·      Executing macros in Excel

·      The macro shortcuts

·      Applications

·      The concept of relative reference in macros

·      Practical Exercise              

·      In-depth understanding of Visual Basic for Applications

·      The VBA Editor

·      Module insertion and deletion

·      Performing action with Sub

·      Ending Sub if condition not met

·      Practical Exercise              

·      Learning about the concepts of workbooks & 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

·      Practical Exercise              

·      Learning how the If condition works

·      How to apply it in various scenarios

·      Working with multiple Ifs in Macro

·      Practical Exercise              

·      Understanding the concept of looping

·      Deploying looping in VBA Macros

·      Practical Exercise              

·      Studying about debugging in VBA

·      The various steps of debugging

·      Understanding breakpoints and way to mark it

·      The code for debugging and code commenting

·      Practical Exercise              

·      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 Exercise              

·      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

·      Practical Exercise              

·      Introduction to powerful data visualization with Excel Dashboard

·      Loading the data

·      Managing data and linking the data to tables and charts

·      Creating Reports using dashboard features

·      Practical Exercise              

·      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

·      Practical Exercise              

·      Creation of Pivot Tables in Excel

·      Learning to change the Pivot Table layout

·      Generating Reports

·      The methodology of grouping and ungrouping of data

·      Practical Exercise              

·      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

·      Practical Exercise              

·      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

·      Practical Exercise              

·      Understanding data quality issues in Excel

·      Linking of data

·      Consolidating and merging data

·      Working with dashboards for Excel Pivot Tables

·      Practical Exercise              

·      What is statistics?

·      How is this useful

·      What is this course for

·      Practical Exercise              

·      Converting data into useful information

·      Collecting the data

·      Understand the data

·      Finding useful information in the data

·      Interpreting the data

·      Visualizing the data

·      Practical Exercise              

·      Descriptive statistics

·      Let us understand some terms in statistics

·      Variable

·      Practical Exercise              

·      Dot Plots

·      Histogram

·      Stemplots

·      Box and whisker plots

·      Outlier detection from box plots and Box and whisker plots

·      Practical Exercise              

·      What is probability?

·      Set & rules of probability

·      Bayes Theorem

·      Practical Exercise              

·      Probability Distributions

·      Few Examples

·      Student T- Distribution

·      Sampling Distribution

·      Student t- Distribution

·      Poison distribution

·      Practical Exercise              

·      Stratified Sampling

·      Proportionate Sampling

·      Systematic Sampling

·      P – Value

·      Stratified Sampling

·      Practical Exercise              

·      Cross Tables

·      Bivariate Analysis

·      Multi variate Analysis

·      Dependence and Independence tests ( Chi-Square )

·      Analysis of Variance

·      Correlation between Nominal variables

·      Practical Exercise              

·      Python Environment Setup and Essentials

·      The Installation of Anaconda Python Distribution

·      What Is A Programming Language

·      Python Interpreter

·      How To Run Python Code

·      Our First Python Program

·      Learning Python

·      Python Data Types

·      Numbers

·      Math Functions

·      Operator Precedence

·      Optional: bin() and complex

·      Variables

·      Expressions vs Statements

·      Augmented Assignment Operator

·      Strings

·      String Concatenation

·      Type Conversion

·      Escape Sequences

·      Formatted Strings

·      String Indexes

·      Immutability

·      Built-In Functions + Methods

·      Booleans

·      Lists

·      List Slicing

·      Matrix

·      List Methods

·      Common List Patterns

·      List Unpacking

·      Dictionaries

·      Practical Exercise              

·      Pandas Introduction

·      Series, Data Frames and CSVs

·      Data from URLs

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Manipulating Data

·      Practical Exercise              

·      Mathematical Computing with Python (NumPy)

·      NumPy Introduction

·      NumPy DataTypes and Attributes

·      Creating NumPy Arrays

·      NumPy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Store Sales

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into NumPy Arrays

·      Practical Exercise              

·      What Is Machine Learning?

·      AI/Machine Learning/Data Science

·      Practical Exercise              

·      Machine Learning Framework

·      Types of Machine Learning

·      Types of Data

·      Types of Evaluation

·      Features In Data

·      Modelling - Splitting Data

·      Modelling - Picking the Model

·      Modelling - Tuning

·      Modelling – Comparison

·      Practical Exercise              

·      Introducing Our Tools

·      Windows Environment Setup

·      Linux Environment Setup

·      Jupyter Notebook Walkthrough

·      Practical Exercise              

·      Matplotlib Introduction

·      Importing And Using Matplotlib

·      Anatomy Of A Matplotlib Figure

·      Scatter Plot And Bar Plot

·      Histograms And Subplots

·      Subplots Option 2

·      Plotting From Pandas DataFrames

·      Customizing Your Plots

·      Saving And Sharing Your Plots

·      Practical Exercise              

·      Scikit-learn Introduction

·      Scikit-learn Cheatsheet

·      Typical scikit-learn Workflow

·      Debugging Warnings In Jupyter

·      Splitting Your Data

·      Clean, Transform, Reduce

·      Convert Data To Numbers

·      Handling Missing Values With Pandas

·      Handling Missing Values With Scikit-learn

·      Choosing The Right Model For Your Data

·      Practical Exercise              

·      Types of Regression Algorithms

·      Simple Linear Regression

·      Multiple Linear Regression

·      Logistic Regression

·      Polynomial Regression

·      Support Vector Regression

·      Ridge Regression

·      Lasso Regression

·      ElasticNet Regression

·      Bayesian Regression

·      Decision Tree Regression

·      Random Forest Regression

·      Practical Exercise              

·      Types of Classification Algorithms

·      Logistic Regression/Classification

·      K-Nearest Neighbours

·      Support Vector Machines

·      Kernel Support Vector Machines

·      Naive Bayes

·      Decision Tree Classification

·      Random Forest Classification

·      Case Studies

·      Practical Exercise              

·      K-Means Clustering

·      A Simple Example of Clustering

·      Clustering Categorical Data

·      How to Choose the Number of Clusters

·      Pros and Cons of K-Means Clustering

·      To Standardize or not to Standardize

·      Relationship between Clustering and Regression

·      Market Segmentation with Cluster Analysis

·      Market Segmentation with Cluster Analysis

·      Species Segmentation with Cluster Analysis

·      Advanced Statistical Methods - Other Types of Clustering

·      Types of Clustering

·      Dendrogram

·      Heatmaps

·      Practical Exercise              

·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Practical Exercise              

·      What is time series?

·      Techniques and applications

·      Time series components

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      Sentiment analysis in Python (Twitter sentiment analysis)

·      Text analysis

·      Rolling Mean For Detecting Temporal Variation

·      Simple Exponential Smoothing (SES)

·      Holt extended simple exponential smoothing

·      Holt Winters

·      Auto Regression Model (AR): Consider Previous Time Steps

·      Implement a Basic ARIMA Model

·      Automated ARIMA & Account for Seasonality (SARIMA)

·      Practical Exercise              

·      Primer

·      Measurement

·      Assumptions

·      Applied PCA work flow

·      Analysis of performance

·      Dimensionality reduction with (PCA)

·      Practical Exercise              

·      History of  R

·      Introduction to R

·      The R environment

·      What is Statistical Programming?

·      Why use a command line? 

·      Your first R session

·      Practical Exercise              

·      Recording your work 

·      Basic features of R

·      Calculating with R 

·      Named storage

·      Functions 

·      Exact or approximate? 

·      R is case-sensitive 

·      Listing the objects in the workspace 

·      Vectors

·      Extracting elements from vectors 

·      Vector arithmetic 

·      Simple patterned vectors 

·      Missing values and other special values

·      Character vectors 

·      Factors 

·      More on extracting elements from vectors 

·      Matrices and arrays 

·      Data frames

·      Dates and times

·      Practical Exercise              

·      Importing data in to R

·      CSV File

·      Excel File

·      Import data from text table

·      SAS and SPSS datasets

·      Exporting Data from R

·      CSV File

·      Text Table

·      Excel File

·      SAS dataset

·      Practical Exercise              

·      Inner Join

·      Left Join

·      Right Join

·      Full Join

·      Anti Join

·      Semi Join

·      Practical Exercise              

·      High-level plots

·      Bar charts and dot charts

·      Pie charts

·      Histograms

·      Box plots

·      Scatterplots

·      QQ plots

·      Density Plot

·      Choosing a high-level graphic

·      Low-level graphics functions

·      The plotting region and margins

·      Adding to plots

·      Setting graphical parameters

·      Practical Exercise              

·      Flow control

·      The for() loop

·      The if() statement

·      The while() loop

·      The repeat loop, and the break and next statements

·      Apply

·      Sapply

·      Lapply

·      Managing complexity through functions • What are functions? 

·      Scope of variables

·      Practical Exercise              

·      Modes and Classes

·      Data Storage in R 

·      Testing for Modes and Classes

·      Structure of  R Objects

·      Conversion of Objects

·      Missing Values 

·      Working with Missing Values

·      Practical Exercise              

·      Reading Vectors and Matrices

·      Data Frames: read.table

·      Comma- and Tab-Delimited Input Files

·      Fixed-Width Input Files 

·      Extracting Data from R Objects 

·      Connections 

·      Reading Large Data Files

·      Generating Data

·      Sequences

·      Random Numbers 

·      Permutations 

·      Random Permutations

·      Enumerating All Permutations 

·      Working with Sequences  v Spreadsheets 

·      The RODBC Package on Windows 

·      The gdata Package (All Platforms)

·      Saving and Loading R Data Objects

·      Working with Binary Files 

·      Writing R Objects to Files in ASCII Format 

·      The write Function 

·      The write.table function

·      Reading Data from Other Programs 

·      Practical Exercise              

·      as.Date

·      The chron Package 

·      POSIX Classes

·      Working with Dates

·      Time Intervals

·      Time Sequences

·      Current time

·      Present date

·      Practical Exercise              

·      Using Factors

·      Numeric Factors  vs.  Manipulating Factors 

·      Creating Factors from Continuous Variables

·      Practical Exercise              

·      Basics of Subscripting 

·      Numeric Subscripts 

·      Character Subscripts 

·      Logical Subscripts

·      Subscripting Matrices and Arrays

·      Specialized Functions for Matrices 

·      Lists

·      Subscripting Data Frames

·      Practical Exercise              

·      Basics of Character Data

·      Displaying and Concatenating Character 

·      Working with Parts of Character Values

·      Regular Expressions in R

·      Basics of Regular Expressions

·      Breaking Apart Character Values

·      Using Regular Expressions in R

·      Substitutions and Tagging

·      Practical Exercise              

·      Modifying Data Frame Variables 

·      Recoding Variables 

·      The recode Function

·      Reshaping Data Frames 

·      The reshape Package

·      Combining Data Frames

·      Practical Exercise              

·      Random Selection of rows and columns

·      Summarization

·      Sort, Arrange

·      Group by

·      Filter

·      Practical Exercise              

·      Identify Missing values

·      Impute missing values

·      Identify Outliers

·      Capping outliers

·      Practical Exercise              

·      Types of Statistics

·      Types of Data

·      Practical Exercise              

·      Measures of Central Tendency

·      Measures of Central Tendency – Usage Chart

·      Measures of Dispersion / Variability

·      Measures of Shape

·      Application of Variance/Std Deviation

·      Practical Exercise              

·      Applications of Hypothesis Testing (Called T Test or Z Test)

·      Steps in Hypothesis Testing

·      Practical Exercise              

·      What is Anova

·      Anova Steps

·      Simple One-Way Anova

·      Simple Two-Way Anova With Multiple Variables

·      Practical Exercise              

·      What is Chi-Square

·      Applications of Chi-Square

·      Practical Exercise              

·      Types of Correlation

·      Properties of Correlation

·      Methods of Calculating Correlation

·      Steps to Calculate Correlation

·      Practical Exercise              

·      What is Regression

·      Types of Regression Analysis

·      Properties of The Regression Line

·      Validating the Model

·      Regression Assumptions

·      Data Transformation for Regression

·      Practical Exercise              

·      Forward Selection Procedure

·      Backward Elimination Procedure

·      Stepwise Regression Method

·      Dummy Variable Analysis

·      Practical Exercise              

·      Likelihood Profiling

·      Assumption

·      Variable Selection Method :- Woe And Iv

·      Model Validation

·      Model Performance

·      Prediction

·      Practical Exercise              

·      What is decision Tree

·      How decision tree works

·      Cart

·      Pruning

·      Overfitting

·      Underfitting

·      Model validation

·      Model performance

·      Practical Exercise              

·      What is MBA

·      Application of MBA

·      Support

·      Confidence

·      Lift

·      Rules

·      Practical Exercise              

·      What is random forest

·      Application of random forest

·      Tune parameters

·      How to tune parameters

·      Model validation

·      Model performance

·      Practical Exercise              

·      What is support vector machine

·      Why to use SVM

·      Hyperplane

·      Kernel

·      Cost

·      Gamma

·      Model validation

·      Model performance

·      Practical Exercise              

·      What is Naïve bayes

·      Bayes theorem

·      Conditional probability

·      Prior probability

·      Posterior probability

·      Application of Naïve bayes

·      Model validation

·      Model performance

·      Practical Exercise              

·      What is time series

·      What is Arima

·      Stationary

·      Seasonality

·      Trend

·      How to find p,d,q

·      What are p,d,q

·      Find best model

·      Forecasting

·      Practical Exercise              

·      An introduction to the business analytics

·      The types of analytics

·      The areas of analytics

·      About analytical tools

·      About analytical techniques

·      Practical Exercise              

·      An overview of the sas

·      Installation and introduction to sas,

·      Understanding different sas windows,

·      How to work with data sets,

·      Various sas windows like output,

·      Search, editor, log and explorer

·      Understanding the sas functions,

·      Which are various library types & programming files

·      The navigation in the sas console

·      About the sas language input files

·      What is data step? 

·      The proc step and data step

·      All about data step processing

·      The sas libraries

·      How to import and export raw data files,

·      How to read and subset the data sets       

·      Practical Exercise  

·      Necessity of combining or modifying the data

·      Concatenating the datasets

·      About interleaving method

·      What is data manipulation?

·      Modifying the variable attributes  

·      Practical Exercise  

·      Logical operator

·      Comparison operator

·      Arithmetic operator

·      Deploying different sas

·      Conditional statements

·      If/else, do while, do until  

·      Practical Exercise  

·      Input buffer,

·      Pdv (backend)

·      Missover                

·      Practical Exercise

·      Understanding the delimiter,

·      Dataline rules,

·      Dlm,

·      Delimiter dsd,

·      Raw data files and execution

·      List input for standard data

·      Practical Exercise              

·      What is proc sql?

·      How to retrieve data from a table?

·      How to select columns in a table?

·      Retrieving the data from multiple tables

·      Selecting the data from multiple tables

·      Concatenating query results                      

·      Practical Exercise

·      Reading standard and non-standard numeric 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

·      Comparing single trailing

·      Double trailing      

·      Practical Exercise  

·      Sas format statements: standard and user-written,

·      Associating a format with a variable,

·      Working with sas format,

·      Deploying it on proc data sets

·      Comparing attrib

·      Format statements 

·      Practical Exercise  

·      Understanding proc gchart,

·      Various graphs,

·      Bar charts pie,

·      Bar and 3d and plotting variables with proc gplot           

·      Practical Exercise

·      Need for sas macros

·      Macro functions

·      Sql clauses for macros

·      The % macro statement

·      The conditional statement             

·      Practical Exercise

·      Introduction to the statistics

·      The statistical terms

·      Procedures in the sas for descriptive statistics

·      About hypothesis testing

·      About variable types

·      The hypothesis testing - process

·      The parametric and non - parametric tests

·      What are parametric tests?

·      What are non - parametric tests?

·      Parametric tests – the advantages and the disadvantages            

·      Practical Exercise

·      The statistical procedures

·      What do proc means?

·      What is proc freq?

·      About proc univariate

·      About proc corr

·      About proc corr options

·      About proc reg

·      The proc reg options

·      The proc anova      

·      Practical Exercise  

·      Data exploration: an overview

·      What is data preparation? 

·      The general comments and observations on data cleaning

·      Data type conversion

·      Character functions

·      What is scan function?

·      About date/time functions

·      Missing value treatment

·      Various functions to handle missing value

·      Data summarization          

·      Practical Exercise  

·      An introduction to the advanced statistics

·      Introduction to the cluster

·      The clustering methodologies

·      What is k means clustering?

·      About the decision tree

·      The regression

·      The logistic regression       

·      Practical Exercise  

·      An introduction to the working with time series data

·      Need for time series analysis

·      About the time series analysis — options

·      Reading date and datetime values

·      What is the white noise process?

·      The stationarity of a time series

·      The plot transform transpose and interpolating time series data   

·      Practical Exercise  

·      Introduction to the designing optimization models

·      About the need for the optimization

·      About optimization problems

·      What is proc optmodel?                

·      Practical Exercise

·      Field of machine learning, its impact on the field of artificial intelligence

·      The benefits of machine learning w.r.t. Traditional methodologies

·      Deep learning introduction and how it is different from all other machine learning methods

·      Classification and regression in supervised learning

·      Clustering and association in unsupervised learning, algorithms that are used in these categories

·      Introduction to ai and neural networks

·      Machine learning concepts

·      Supervised learning with neural networks

·      Fundamentals of statistics, hypothesis testing, probability distributions, and hidden markov models

·      Practical Exercise              

·      Multi-layer network introduction, regularization, deep neural networks

·      Multi-layer perceptron

·      Overfitting and capacity

·      Neural network hyperparameters, logic gates

·      Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions

·      Back propagation, forward propagation, convergence, hyperparameters, and overfitting

·      Practical Exercise              

·      Various methods that are used to train artificial neural networks

·      Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques

·      Stochastic process, vanishing gradients, transfer learning, regression techniques,

·      Lasso l1 and ridge l2, unsupervised pre-training, xavier initialization.

·      Practical Exercise              

·      Understanding how deep learning works

·      Activation functions, illustrating perceptron, perceptron training

·      multi-layer perceptron, key parameters of perceptron;

·      Tensorflow introduction and its open-source software library that is used to design, create and train

·      Deep learning models followed by google’s tensor processing unit (tpu) programmable ai

·      Python libraries in tensorflow, code basics, variables, constants, placeholders

·      Graph visualization, use-case implementation, keras

·      Practical Exercise              

·      Keras high-level neural network for working on top of tensorflow

·      Defining complex multi-output models

·      Composing models using keras

·      Sequential and functional composition, batch normalization

·      Deploying keras with tensorboard, and neural network training process customization

·      Practical Exercise              

·      Using tflearn api to implement neural networks

·      Defining and composing models, and deploying tensorboard

·      Practical Exercise              

·      Mapping the human mind with deep neural networks (dnns)

·      Several building blocks of artificial neural networks (anns)

·      The architecture of dnn and its building blocks

·      Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions

·      Practical Exercise              

·      What is a convolutional neural network?

·      Understanding the architecture and use-cases of cnn

·      ‘What is a pooling layer?’ how to visualize using cnn

·      How to fine-tune a convolutional neural network

·      What is transfer learning?

·      Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow

·      Practical Exercise              

·      Introduction to the rnn model

·      Use cases of rnn, modeling sequences

·      Rnns with back propagation

·      Long short-term memory (lstm)

·      Recursive neural tensor network theory, the basic rnn cell, unfolded rnn,  dynamic rnn

·      Time-series predictions

·      Practical Exercise              

·      Gpu’s introduction, ‘how are they different from cpus?,’ the significance of gpus

·      Deep learning networks, forward pass and backward pass training techniques

·      Gpu constituent with simpler core and concurrent hardware

·      Practical Exercise              

·      Introduction  rbm and autoencoders

·      Deploying rbm for deep neural networks, using rbm for collaborative filtering

·      Autoencoders features and applications of autoencoders.

·      Practical Exercise              

·      Image processing

·      Natural language processing (nlp) – Speech recognition, and video analytics

·      Practical Exercise              

·      Automated conversation bots leveraging

·      Ibm Watson

·      Microsoft’s luis

·      Open–closed domain bots

·      Generative model

·      The sequence to sequence model (lstm)

·      Practical Exercise              

·      RDBMS

·      Types of relational databases

·      Challenges of RDBMS

·      NoSQL database

·      Its significance

·      How NoSQL suits Big Data needs

·      Introduction to MongoDB and its advantages

·      MongoDB installation

·      JSON features

·      Data types

·      Practical Exercise              

·      Installing MongoDB

·      Basic MongoDB commands and operations

·      MongoChef (MongoGUI) installation and MongoDB data types

·      Install MongoDB and install MongoChef (MongoGUI)

·      Practical Exercise              

·      The need for NoSQL,

·      Types of NoSQL databases

·      OLTP

·      OLAP

·      Limitations of RDBMS

·      ACID properties

·      CAP Theorem

·      Base property

·      Learning about JSON/BSON

·      Database collection and documentation

·      MongoDB uses

·      MongoDB write concern—acknowledged, replica acknowledged, unacknowledged, journaled—and Fsync

·      Write a JSON document

·      Practical Exercise              

·      Understanding CRUD and its functionality

·      CRUD concepts

·      MongoDB query and syntax and read and write queries and query optimization

·      Use insert query to create a data entry

·      Use find query to read data

·      Use update and replace queries to update and use delete query operations on a DB file

·      Practical Exercise              

·      Concepts of data modelling

·      Difference between MongoDB and RDBMS modelling

·      Model tree structure

·      Operational strategies

·      Monitoring and backup

·      Use shard key and hashed shard keys

·      Perform backup and recovery of a dummy dataset

·      Import data from a CSV file and export data to a CSV file

·      Write a data model tree structure for a family hierarchy

·      Practical Exercise              

·      MongoDB® Administration activities

·      Health check

·      Backup

·      Recovery

·      Database sharding and profiling

·      Data import/export

·      Performance tuning

·      Use shard key and hashed shard keys

·      Perform backup and recovery of a dummy dataset

·      Import data from a CSV file and export data to a CSV file

·      Practical Exercise              

·      Concepts of data aggregation and types

·      Data indexing concepts, properties and variations

·      Do aggregation using pipeline, sort, skip and limit and create index on data using single key and using multi-key      

·      Practical Exercise  

·      Understanding database security risks

·      MongoDB security concept

·      Security approach

·      MongoDB integration with Java and Robomongo

·      Practical Exercise              

·      Implementing techniques to work with variety of unstructured data

·      Understanding GridFS MongoDB file system for storing data

·      Practical Exercise              

·      Various types of databases

·      Introduction to Structured Query Language

·      Distinction between client server and file server databases

·      Understanding SQL Server Management Studio

·      SQL Table basics

·      Data types and functions

·      Transaction-SQL

·      Authentication for Windows

·      Data control language

·      The identification of the keywords in T-SQL, such as Drop Table

·      Practical Exercise

·      Data Anomalies

·      Update Anomalies