Microsoft Data Science Classroom Training

This Microsoft Data Science Classroom Training Course includes the necessary skillset required for Data Scientists with Microsoft Platform. This Microsoft Data Science course inlcudes SQL Server & T-SQL, Excel, Power BI, with Python, R Language and Hadoop File System (HDFS), Azure Machine Learning, Spark and Scala. All our Data Science Trainings are completely practical and real-time. Resume Preperation, Job Guidance and Real-time project are a part of this course. Register Today


  Plan A Plan B Plan C
Courses Included T-SQL,
Power BI
Python, R
for ML
Azure ML, HDFS
Spark, Scala
Data Scientistis - Basics, Job Roles Check-Symbol-for-Yes Check-Symbol-for-Yes Check-Symbol-for-Yes
SQL and Database Basics Croos-symbol-for-Yes Check-Symbol-for-Yes Check-Symbol-for-Yes
SQL Server & T-SQL Basics Croos-symbol-for-Yes Check-Symbol-for-Yes Check-Symbol-for-Yes
Queries, Joins, Data Access Croos-symbol-for-Yes Check-Symbol-for-Yes Check-Symbol-for-Yes
Power BI For Big Data Analysis Croos-symbol-for-Yes Croos-symbol-for-Yes Check-Symbol-for-Yes
Power BI For Statistical Analysis Croos-symbol-for-Yes Croos-symbol-for-Yes Check-Symbol-for-Yes
Power BI with Excel - Data Analysis Croos-symbol-for-Yes Croos-symbol-for-Yes Check-Symbol-for-Yes
Power BI with Report Server, Cloud Croos-symbol-for-Yes Croos-symbol-for-Yes Check-Symbol-for-Yes
Power Query, DAX & Data Modelling Croos-symbol-for-Yes Croos-symbol-for-Yes Check-Symbol-for-Yes
Python for Machine Learning Croos-symbol-for-No Croos-symbol-for-Yes Croos-symbol-for-Yes
R for Machine Learnig Croos-symbol-for-No Croos-symbol-for-Yes Croos-symbol-for-Yes
Azure Machine Learning Croos-symbol-for-No Croos-symbol-for-No Croos-symbol-for-Yes
Hadoop Insight Croos-symbol-for-No Croos-symbol-for-No Croos-symbol-for-Yes
Spark, Scala Croos-symbol-for-No Croos-symbol-for-No Croos-symbol-for-Yes
Big Data Solutions to AML Croos-symbol-for-No Croos-symbol-for-No Croos-symbol-for-Yes
Course Duration 6 Weeks 13 Weeks 16 Weeks
Total Course Fee INR 11000/- INR 24000/- INR 35000/-

Training Schedules

      Schedule (IST) Start Date  
1 9 AM - 10 AM Oct 21st Register
2 11:30 AM - 12:30 PM Sept 17th Register
3 6:30 PM - 7:30 PM Oct 3rd Register

Data Science Training Highlights

✔ Database Basics ✔ Big Data Analysis
✔ Queries, Joins ✔ Statistical Analysis
✔ Query Tuning ✔ Report Server, Cloud
✔ Data Modelling ✔ Python for ML
✔ R for ML ✔ Azure ML
✔ Big Data Solutions ✔ Hadoop Insight
✔ Linked Servers ✔ Azure DB Tuning

All Session Are Completely Practical & Real Time


Data Science Training Course Contents:

Data Science Training Course Contents:


Need for Big Data and BI Technologies; Purpose of BI Tools & Power BI Suite; Power BI as a Self-Service BI, Scope; Comparing Power BI with Microsoft MSBI & Tableau; Power BI For Data Scientists & Business Analysts; Power BI Job Roles; MCSA Examination for Power BI: 70-778;

Data, Databases and RDBMS Software; Database Types : OLTP,DWH,OLAP,HTAP; Microsoft SQL Server Advantages; DB Engine, BI, Data Science Components; SQL : Purpose, Real-time Usage; SQL Versus Microsoft T-SQL [MSSQL];


SQL Server 2016 Installation, Guidance; SQL Server 2017 Installation, Guidance; Instance Types: Default, Named Instances; SQL Server Features; SQL Server Database Engine For OLTP; File Stream and Collation; SQL Server Authentication Modes; Windows Login & SQL Server sa Login; System Databases: Master, Model, MSDB, TempDB, Resource Database; SQL Server Management Studio. SSMS; Test Connection to Local Servers and Remote (Online) Servers;

Chapter  3:  DATABASE & SQL BASICS - Level 1

SQL : Purpose and Real-time Usage; DDL, DML, SELECT, DCL and TCL; SSMS Tool : Connections and Usage; SQL Versus T-SQL : Basic Difference; Server Connections, Session Creations; Creating Databases and DB Connections; Creating Tables. Int, Char Data Types; Single Row Inserts, Multi Row Inserts; INSERT and INSERT INTO Statements; SELECT Statement for Table Retrieval; WHERE Conditions with =, OR, IN; AND, OR, NOT, IN, NOT IN;

Chapter  4:  DATABASE & SQL BASICS - Level 2

Creating Databases: Files [MDF, LDF]; Single Row Inserts, Multi Row Inserts; SELECT… WHERE Conditions, Operators; AND, OR, NOT, Mathematical Operators; IN, NOT IN, BETWEEN, NOT BETWEEN; IS NULL, LIKE, NOT LIKE. % and _; CHAR Vs VARCHAR Data Types; GO Statement, SQL BATCH; DISTINCT, TOP, FETCH, ORDER BY; Basic Sub Queries; UPDATE, DELETE, TRUNCATE, ALTER, ADD and DROP, UNION ALL & UNION;


Constraints and Keys - Data Integrity; NULL, NOT NULL Property on Tables; UNIQUE KEY Constraints: Importance; PRIMARY KEY Constraint: Importance; FOREIGN KEY Constraint: Importance; REFERENCES, CHECK and DEFAULT; Candidate Keys and Identity Property; Diagrams and ER Models; Indexes : Basic Types and Creation; Index Search Advantages; Clustered & Non Clustered Indexes; Primary Key and Unique Key Indexes;

Chapter  6: JOINS, T-SQL QUERIES - Level 1

JOINS - Table Comparisons Queries; INNER JOIN - Examples, WHERE, ON; OUTER JOIN - Examples, WHERE, ON; Left Outer Joins with Example Queries; Right Outer Joins with Example Queries; FULL Outer Joins - Real-time Scenarios; MERGE, LOOP, HASH Join Options; Big Table Versus Small Table Joins; Join Types Versus Join Options in T-SQL; CROSS JOIN Versus CROSS APPLY; Joining Unrelated Tables, Options;

 Chapter  7: JOINS, T-SQL QUERIES - Level 2

GROUP BY Queries and Aggregations; GROUP BY Queries with Having Clause; ROLLUP( ) & CUBE( ) Summary Values; GROUPING() Functions; Joining Tables with Group By, Having; Sub Queries; Nested Sub Queries with Group By, Joins; Comparing WHERE, HAVING Conditions; Cast, Convert, DateAdd, DateDiff Functions; Date & Time Styles; Date and Time Formats;

 Chapter  8: JOINS, T-SQL QUERIES - Level 3


 Chapter  9: Views, Functions, Procedures - Basics

Views : Usage in Real-time; System Predefined Views, Audits;  Listing Databases, Tables, Indexes; Functions : Types, Usage in Real-time; System Predefined Functions, Audits; Variables & Parameters in SQL Server; Procedures : Usage in Real-time;  Working with Parameters and Dynamic Joins in Real-time; Working with Data Access using Functions and Stored Procedures;

 Chapter  10: Triggers and Transactions - Basics

Triggers - Purpose, Types Of Usage; DML Triggers; Transactions; Auto Commit Transaction, ACID Options; Open Transactions & Query Blocking Scenarios @ Real-time; NOLOCK and READPAST Lock Hints; Schemas Creation, Use, Table; Real-time Usage; Synonyms : Creation, Real-time use;


SQL Server Architecture Components; Protocols, SQL Native Client (SNAC); Parser, Compiler, SQL Query Validations; Query Optimizer (QO) and SQL Manager; Storage Engine, File and DB Manager; Transaction Manager and Lock Manager; Buffer Manager, SQL OS and IO Buffer; Synchronization and Thread Scheduler; MDAC and CLR; Local & Global Temporary Tables;  


Normal Forms for Entity Relationships; First, Second, Third & Boycee-Codd Normal Form: BNCF: Usage; 4 NF, EKNF, ETNF. Functional Dependency; Multi-Valued, Transitive Dependencies; 1:1, 1:M, M:1, M:M Relationship Types; UPDATE/DELETE Types; Joining more than 2 Tables in T-SQL; Integration with Excel, Pivot Reports and Pivot Tables and Pivot Charts;

MODULE 2: Power BI Report Design and Visualizations


Power BI Architecture, Tools; Power BI - Integrations in Real-time; Need for Power BI Service & Cloud; Need for Power BI Report Server, SSDT; Power BI Report Server & Report Builder; Mobile Report Publisher; Report Types: Interactive; Analytical, Paginated & Mobile Reports; Cortana - Q&A; Configure Power BI Cloud; Install Power BI Desktop, Gateways;


Install and Configure Power BI Desktop; Understanding Power BI Desktop Tool; In-Memory, SMDL and Vertipaq Options; Visuals, Fields, Pages and Filters; Report, Data and Relationship Options; Concept & Use of PBIX and PBIT Files; Designing Simple / Basics Reports in PBI; Get Data from DAT Files, Basic Reports; Enter Data from Excel Files, Basic Reports; Creating PBIX Files and Re-Use; Creating PBIT Files; Table Reports; Interactive Visuals and Focus Mode;


Visual Interactions in Power BI; Edit Interactions - Format Options; Filter and Slice Control Options; Interactions with Related Data; Slicer : Number, Text, Date Data; Slicer Orientation, List, Dropdowns; Visual Sync Property - Limitations; Grouping and Binning with Fields; Grouping Static / Fixed Data Values; Grouping Dynamic / Changing Data; Bin Size and Biz Limits (Max, Min); Bin Count and Grouping;  Min and Max Value; Value Distributions and Ranges;


Creating Hierarchies in Power BI; Hierarchies with Field Groups; Hierarchies in Table Visuals; Hierarchies in Matrix, Charts; Hierarchies with Date, Time; Built-In Hierarchy Labels in Visuals; Inline Hierarchy Labels in Visuals; Hierarchies: Issues and Solutions; Independent Drill-Down; Dependant Drill-Down; Conditional Drilldowns, Data Points; Drill Up, Include & Exclude; Drill Thru Reports, Conditional Filters; Show Data, Export Data, See Records;


Filters : Types and Usage in Real-time; Visual Filter, Page Filter, Report Filter; Basic, Advanced and TOP N Filters; Category Level, Summary Level Filters; Slicer Versus Filters - Comparisons; DrillThru Filters and Drill Thru Reports; Keep All Filters" Options in DrillThru; Drill-thru Options with Page Navigations; Bookmarks : Navigations, Real-time Use; Using Bookmarks for Visual Filters; Using Bookmarks for Page Navigations; Using Selection Pane with Bookmarks; Using Buttons, Images with Actions; Buttons, Actions and URLs; Image Actions, Selection Pane;


Power BI Reports with OLTP Databases; Power BI Reports with Big Data Sources; Azure Cloud Database Access, Reports; Import and Direct Query with Power BI; Using SQL Queries in Power BI Desktop; Using Views, Tables, Functions in PBI; Data Modeling : Summary, Format; Data Modeling : Currency, Relations; Relationship Management with OLTP; 1:1, 1:M, M:1 Relations in Power BI;  Synonyms : Creation and Usage; Connection with MS Access Databases; 


Fields, Formats & Analytics; Table Visuals & Properties, Filters; Data Bar, Data Scaling; Divergent Property, Data Labels; Matrix : Sub Totals, Grand Totals; Drilldown: Row, Column; Stacked Bar Chart, Clustered Colum Chart, Stacked, Clustered Colum Chart; 100% Stacked Bar, Column Charts; Area, Stacked Area Chart. Ribbon Chart; Waterfall Chart, Scatter Chart, Pie Chart; Line, Doughnut, TreeMap, Funnel, Gauge; Labels, Values, Legend; Axis, Title, Filters.


Map Reports and Filled Map Reports; ArcGIS Maps and Real-time Usage. Options; Longitudes and Latitudes. Bing Maps; Color Analytical Maps, Styles, Options; KPI Reports - Target, Value and Trend; Plot Area & Responsiveness Properties; Sentiment Colors, Color Saturations; Breakdowns, Title, Axis Properties; Category & Data Labels, Constant Lines; Working with SLICER Visual : Value Filters; Horizontal and Vertical Slicer Filters; Single Select and Multi Select Options; Frown (Errors), Details. Lock Aspects; Custom Visuals and PBIVIZ File; PBI & VM;



Power Query [M Language] - Purpose; Power Query Usage & Operation Types; Power Query Architecture and Usage; QUERY Concept, Properties, Validations; Power Query for Data Mashup Operations; Basic Data Types, Literals and Values; Expressions and Primitives in M Language; LIST : Syntax, Examples and Usage; RECORD : Syntax, Examples and Usage; TABLE : Syntax, Examples and Usage; Power Query Connection Formats, Settings; let, source & in M Lang; Power BI Canvas: Edits, Applied Steps; Queries and Applied Steps, Edit Queries;


Data Sources with Excel, File Formats; Data Sources with Database Connections; BLANK Data Sources Creation & Scope; Creating LIST, RECORD& TABLE Queries; Functions in Power Query and Usage; Defining Functions, Invoke Options; Mashup Operations in Power Query Editor; Row Filters, Column Filters, Renames; Promoting Headers and Query Settings; MERGE Queries For Combining Queries; Inner Join and Left/Right Outer Joins; Left Anti Join, Right Anti Join, Full Join; UNION All, Group By and Aggregations; Close and Apply Options. Data Imports;


Creating Parameters in Power Query; Intrinsic & Query Parameters, Usage; Parameter Data Types, Default Value Lists;;  Static Lists, Dynamic Lists For Parameters; Reports with Range Values - Usage; Parameters with PBIT Reports, Prompts ; New Queries from Dataset Fields, Usage; Removing Duplicate Rows and Columns; Column Delimiters and Field Formatting; Change Type and Transform Operations; Replace Values & Errors, Transpose; Group By Aggregations, Fill, Reverse Rows; Format Options in M Language; Pivot and Unpivot Options in Power Query; Data Type Conversions & Format Options; Visual Sync Features and Bookmarks;


Data Modeling Options with Power Query; Modeling Operations - Custom Columns; Query Transforms, Sort Direction, Statistics; Enabling / Disabling Loads. ToList Options; Creating Parameters & Variables. Functions; Date and Time Columns. String Functions; Advanced Edit Options, Custom Queries; Custom Functions in Power Query M Lang; Grouping and Sub Groups with Queries; Binning with Groups and Query Formats; SubString Functions, Splits (Row, Column); Parsing XML and JSON Files. Formatting;



DAX : Importance and Real-time Usage; DAX Data Types and DAX Calculations; Syntax, Functions, Context Options; ROW Context and Filter Context in DAX;DAX Functions. Aggregates and Usage; Creating and Using Measures with DAX; Creating and Using Columns with DAX; Vertipaq Engine & Special Characters; DAX Real-time Usage; Quick Measures in DAX;


In-Memory Processing: DAX Performance; Date and Time & Text Functions; Time Intelligence Functions in DAX; Logical & Mathematical Functions; PowerPivot xVelocity, Vertipaq Store; Data Modeling with DAX. Creating Roles; Currency Conversions, Field Formatting; SUM and SUMX Functions: Differences; CALCULATE(), CALCULATEX() Functions; CALCULATETABLE Functions;


VAR, VARP; DATESMTD, DATESQTD, DATESYTD; ENDOF(), FirstDay(), LastDay(); SAMEPERIODLASTYEAR, STARTOF(); Other DAX Functions and Examples; DAX Expressions with Quick Measure; DAX Usage for Row Level Security; Creating Roles with Power BI Desktop; DAX Filters & Multi Value Conditions; Manage Roles and Membership Options; VIEW AS ROLE;


DAX Variables, Joins and Usage; Value Assignments with DAX; CONCATENATE Function in DAX; HOUR, NOW, SWITCH, TRUE; RETURN Values with DAX Functions; Numeric and Other DAX Options; DAX Modeling Components - use; TABLES, COLUMNS, RELATIONS; MEASURES and HIERARCHIES; INTELLISENSE with DAX Editors; Comments in DAX - Syntax Options; DISTINCTCOUNT and DIVIDE; SAMEPERIODLASTYEAR and IF; Revenue and Budget with DAX; VARIANCE Calculations in DAX; TOTALYTD, YTD, QTD and MTD;

MODULE 5: Power BI Service (CLOUD), Report Server, Microsoft Excel


Power BI Cloud & Power BI Workspace; App Workspace Creation in Real-time; Publish Reports from Power BI Desktop; Reports and Datasets in Power BI Cloud; Pin Visuals and Pin LIVE Report Pages; Dashboard Creation and Tiles, Media; Images, Web Content, Videos, Q& A; Mobile View, Web View, QR Codes, Shares; Enabling Interactive Visuals. Embed Codes; Report Shares and Dashboard Shares; App Publish, Updates; Power BI Report Edits, Visuals; Download PBIX;


Dashboard Properties and Security; Report properties and Security; Dataset properties and Security; Report Insights, Related Items & Metrics; Quick Insights. Publishing App Workspace; Power BI LIVE Report Edits, Downloads; Content Packs : Creation; Organizational and Service Level; Content Packs; Personal and Enterprise Gateways; NT SERVICE\PBIEgwSERVICE Account; Dataset Refresh Options and Schedules; Azure Databus Integration, ODG Logs


Using Excel with Power BI Reports; Using Excel Analyser in Power BI; Using Excel Publisher in Cloud; Creating Datasets with Excel, Office 365; Excel Uploads & Cloud Imports, Office 365; Cell Selection and PINS in Power BI; Excel Online Service - Edits and Pins; Excel ODC Connections and Real-time Use; Excel Power Pivot and Power BI Cloud; Excel Drilldowns and Drillthru Reports; KPI Reports; Excel for Big Data Analysis, Reporting; Worksheets and Dashboards with Excel; Power Pivot Reports in with Excel; Power View Report Options in Excel;


Settings and Power BI Admin Console; Configuring Power BI Report Server; Report Server Database TempDB Database; Webservice URL, Webportal URL - Usage; Report Builder Installation & Usage; Designing Paginated Reports (RDL), Tests; Deploy to Power BI Report Server, Settings; Data Source Connections, Report Options; Register Power BI Report Server to Cloud; Tenant IDs Generation and Real-time Usage; Publish RDL Reports to Power BI Cloud;


REST APIs with Power BI Cloud Service; Streaming Datasets Creation in Power BI; API : Usage. SubKeys and Channels; Push and Pubnub Datasets in Power BI; Azure Stream for Real-time Data Reports; Real-time Data Tracking Visuals (Interactive); Power BI Mobile Publisher and Mobile Reports; Power BI Vs Tableau, SSRS, Share Point; Licensing & Pricing Plans in Power BI; MSBI SSAS and SSRS with Power BI; Custom Visuals in Power BI; Alerts in Power BI; App Publish;

Module 6: Python Training


What is Script in Python?; What is a program in Python?; Types of Scripts in Python?; Difference between Script; programming languages list; main features of scripting Lang.; limitation of client side scripting; Programming Language Paradigms; Basic understanding of Python; Is Python a compiled language?; where is python used in real life?;Why is Python called Python?;


What is Python Programming?; Why Python is used in DS?; Where is python Mostly used?; Characteristics of Python Programming; History of Python Programming Language; What is PSF Python Programming?; Python Versions - Python Application; How to Download Python,print to the screen; How to Install Python , Creating Program; Install Python with Diff IDEs; Features of Python Programming; Limitations of Python Programming;

CHAPTER 3: Data Analytics

Introduction to data Big Data?; Introduction to NumPY and SciPY; Introduction to Pandas and MatPlotLib

Data Science;

What is Data Science in Python; Data Science Life Cycle in python; what is data analysis using python; what is Data Mining in Python; Analytics vs Data Science in python; How Python is used in big data?; Is Python or R better for data science?; Why is Python used in data science?;

CHAPTER 4 : String Handling for Data Science

what is String ? - String Operations - String indices; String Functions - len , upper, lower,join,Split; SwapCase(), Title(),find(),isupper(),islower(); Delete a string - Python Keywords; String Multiplication and concatenation; Python Identifiers - Python Literals; string formatting operator in python; Built-in String Methods - Data Structures; Structuring with indentation in Python; Define Data Structure in Python Language; Reverse words - Reverse Characters Examples; How do you split a string in Python?;

CHAPTER 5: Python Conditional for Data Science

Control Structures - Sequential Control Structure; Selective and Repetative Control Structure; How to use “if condition” in conditional; control Structures in python; if statement (One-Way Decisions); if .. else statement (Two-way Decisions); How to use “else condition”; if .. elif .. else statement (Multi-way); When “else condition” does not work; How to use “elif” condition; How to execute conditional statement with; minimal code - Nested IF Statement; Nested IF Statement in python;

CHAPTER: 6 Python LOOPS for Data Science

How to use While loop and For loop; Break and Continue Statements in For loop; Python Enumerate function for For Loop;

Sequence or Collections and Lists for Data Science

Strings - Unicode Strings; Lists - Tuples - Sets - Dictionary - Xrange; Lists are mutable - Accessing the List; Updating a List - Deleting a List; List indices - Traversing a list; List operations - List Slices - List Methods; Map, filter and reduce - Deleting elements; What is list of list in python?; What is Python list function?; How do you add to a list in Python?;

CHAPTER 7: Python TUPLE for Machine Learning

Advantages of Tuple over List; Packing and Unpacking - Tuples; Creating Nested tuple -Examples; Deleting Tuples - Slicing of Tuples; Comparing Tuple Membership Test; Built in Functions ,Dotted Charts;

Python Sets for Machine Learning

how to create/declare a set in python; Iteration Over Sets - Python Methods; Python Set Operations - Union of Sets; Built-in Functions with Set; python frozenset get element;

CHAPTER 8 : Python Dictionary for Machine Learning

How to create a dictionary?; PYTHON HASHING - Dictinary Methods; Copying dictionary - Updating Dictionary; Delete Keys from the dictionary; Sorting the Dictionary - Dictionary len(); Python Dictionary in-built Functions; Variable Types - python List Cmp(); Python List cmp() Method; Python Dictionary Str(dict); How do you create a dictionary in Python?; Can Python dictionary have multiple values?; How do you add to a dictionary in python?;

CHAPTER 9 : Python Functions for DataScience

What is a function? - Types of Function;  How to define and call a function in Python; Significance of Indentation (Space) in Python; How Function Return Value?;  Types of Arguments in Functions; Default Arguments - Non Default Arg.; Keyword Arguments - Non Keyword Arg.; Arbitrary Arguments in python; Various Forms of Function Arguments; Scope and Lifetime of variables - Nested Fun; Call By Value, Call by Reference in python; Anonymous Functions/Lambda functions;

CHAPTER 10 : Python Modules for Data Science

What is a Module? - Types of Modules; The import Statement - The from… import st; ..import * Statement - Underscores in python; The Dir() Function in python; Creating User defined Modules; Command line Arguments in python; Getting Python Module Search Path; What are modules and packages in Python?; What is Python import statement?; How do you import random in Python?; import <module_name> string python; from <module_name> import <name(s)>; from <module> import <name> as <name>;

CHAPTER 11 : Packages in Python for Data Science

What is a Package in Python?; Introduction to Packages?; py file - Creating a package; Importing module from a package; Creating Sub Package in Python; Importing from Sub-Packages; Most Popular Python Packages; How many libraries are there in Python?; What are libraries in Python?; What is the difference between NumPy & SciPy?; Why is SciPy and NumPy used?; Python what is Seaborn? - Examples; Is NumPy a Python framework?;

CHAPTER 12 : Python Date and Time for Machine Learning

How to Use Date & DateTime Class; How to Format Time Output; How to use Timedelta Objects; Calendar in Python; datetime classes in Python; How to Format Time Output?; Python Calendar Module,Time Module; Python Text Calendar; Python HTML Calendar Class; Unix Date and Time Commands; Python strftime(); How strftime() works?;

CHAPTER 13 : Machine Learning Basics

Gain an in-depth understanding of data structure and data manipulation; What Converting business problems to data problems; Understanding supervised and unsupervised learning with examples; Understanding biases associated with any machine learning algorithm; Ways of reducing bias and increasing generalization capabilities; Drivers of machine learning algorithms; Cost functions; Brief introduction to gradient descent; Importance of model validation; Methods of model validation; Cross validation & average error; Understand and use linear and non-linear regression models andclassificationtechniquesfordataanalysis; Gain expertise in mathematical computing using the NumPy and Scikit-Learn packages; Learn to analyze data using Tableau and become proficient in building interactive dashboards;

CHAPTER 14 : Generalized Linear Models in Python

Linear Regression; What is linear regression used for?; How does a linear regression work?; What is a simple linear regression model?; What is linear regression for dummies?; Regularization of Generalized Linear Models; Ridge and Lasso Regression; Logistic Regression; Methods of threshold determination and; performance measures for classification score models; What is logistic regression in Python?; What is penalty in logistic regression?; What is solver in logistic regression?; What is logistic regression in ML?;

Tree Models using Python

Introduction to decision trees; Tuning tree size with cross validation; Introduction to bagging algorithm; Random Forests; Grid search and randomized grid search; Extra Trees (Extremely Randomized Trees); Partial dependence plots;

CHAPTER15 : Boosting Algorithms using Python

Concept of weak learners; Introduction to boosting algorithms; Adaptive Boosting; Extreme Gradient Boosting (XGBoost); What is a strong learner?; What is boosting in ML?; What is bagging and boosting in machine learning?; What is learner in machine learning?; What is the difference between GBM and XGBoost?; What is linear regression used for?; How does a linear regression work?; What is a simple linear regression model?;


Support Vector Machines (SVM) & kNN in Python

Introduction to idea of observation based learning; Distances and similarities; k Nearest Neighbors (kNN) for classification; Brief mathematical background on SVM/li>; Regression with kNN & SVM; Can Knn be used for regression?; Which is better KNN or SVM?; Is Knn generative or discriminative?; Is SVM better than random forest?; What is SVM Python?; How SVM is used for classification?; Can SVM be used for multiclass classification?; What is SVM in machine learning?; Can SVM be used for regression?; What is Sklearn in Python?; How is SVM trained?; What is SVM score?;

CHAPTER 16 : Unsupervised learning in Python

Need for dimensionality reduction; Principal Component Analysis (PCA); Difference between PCAs and Latent Factors; Factor Analysis; Hierarchical, K-means & DBSCAN Clustering; What is PCA algorithm?; What is PCA used for?; What is PCA in neural network?; How does a support vector machine work?; What is the purpose of factor analysis?; What is factor analysis with example?; Why is factor analysis done?; What are the types of factor analysis?;

CHAPTER 17: Artificial Neural Networks in Python

Introduction to Neural Networks; Single layer neural network; Multiple layer Neural network; Back propagation Algorithm; Neural Networks Implementation in Python; Are neural networks machine learning?; What is neural network explain in detail?; What is neural networks in deep learning?; How do artificial neural networks work?; What is the difference between neural networks and machine learning?; Does machine learning require neural networks?; What is the difference between deep learning and neural networks?; Is artificial neural network and neural network same?; What are the advantages of neural networks in machine learning?; What is back propagation algorithm in neural network?; What is the back propagation algorithm?; What is back propagation algorithm in machine learning?;

CHAPTER 18 : Text Mining in Python

Gathering text data using web scraping with urllib; Processing raw web data with Beautiful Soup; Interacting with Google search using urllib with custom user agent; Collecting twitter data with Twitter API; Naive Bayes Algorithm; Feature Engineering with text data; Sentiment analysis; What is text mining in python?; What is preprocessing in text mining?; How do you do text mining?; What is text mining in data science?; What is naive Bayes classification algorithm?; What is naive Bayes used for?; What is naive Bayes algorithm in data mining?; Is naive Bayes supervised or unsupervised?;

Essential to R programming

  • Introduction to the R language
  • Programming statistical graphics
  • Programming with R
  • Simulation
  • Computational linear algebra
  • Numerical optimization


  • Introduction to functions
  • Function documentation
  • Use a function
  • Create own function
  • Nested functions
  • Function scoping

Data Manipulation Techniques using R programming

  • Data in R
  • Reading and Writing Data
  • R and Databases
  • Dates, Factors, Subscribing
  • Character Manipulation
  • Data Aggregation, Reshaping Data


  • Available R Packages
  • Packages installation
  • Default packages
  • Create package
  • Attach package...etc
  • Load Package to Library

Statistical Applications using R programming

  • The R Environment
  • Probability and distributions
  • Descriptive statistics and graphics
  • One- and two-sample tests
  • Regression and correlation
  • Analysis of variance and the Kruskal–Wallis test
  • Tabular data
  • Power and the computation of sample size
  • Advanced data handling
  • Multiple Regression
  • Linear models
  • Logistic regression
  • Survival analysis
  • Rates and Poisson regression
  • Nonlinear curve fitting

Graphics systems in R

  • Base graphics, Plot
  • Histogram, Scatter
  • Bar plot, Qqplot
  • Sunflowerplot, Boxplot
  • Add more detail to graphs
  • Grid graphics
  • Lattice graphics
  • ggplot2 graphics
  • Data layer
  • Aesthetics layer
  • Geometries layer
  • Facets layer
  • Statistics layer
  • Coordinates layer
  • Themes layer

Data Science with R

  • Introduction
  • About S, About R, About CRAN
  • Installation of R
  • About working directory
  • Changing working directory temporarily
  • Changing working directory Permanently
  • Installation of R studio
  • Atomic Datatypes in R

Cleaning data( equal to ETL work)

  • gather function
  • spread() function
  • separate() function
  • unite() function
  • Working with lubridate package
  • Working with stringr package
  • Working with Missing values
  • Working with Special values


  • Creating vector
  • Naming Vector
  • Vector selections
  • Adding elements to vector
  • Update elements of vector
  • Delete elements of vector
  • Functions (c(), names() ...etc)


  • What is matrix
  • Create matrix
  • Naming a matrix
  • Arithmetic with matrix
  • Adding row
  • Adding column
  • Selection of matrix elements
  • Insert /delete/update matrix elements
  • Transpose matrix
  • Combine rows of matrix
  • Combine columns of matrix

Machine learning& Artificial intelligence

  • What is machine learning?
  • What is ETP?
  • Types of machine learning
  • Supervised learning
  • Unsupervised learning
  • Semi-supervised Learning
  • Reinforcement learning
  • Algorithms or Model for Machine Learning
  • Linear Regression, Logistic Regression
  • Jackknife Regression *
  • Density Estimation, Confidence Interval
  • Test of Hypotheses
  • Pattern Recognition
  • Supervised Learning
  • Time Series & Decision Trees
  • Random Numbers
  • Monte-Carlo Simulation
  • Bayesian Statistics
  • Naive Bayes, Principal Component


  • Categorical variables
  • Continuous variables
  • What is factor
  • Factors in Data Frame
  • Factor Levels in customized format
  • Nominal factors
  • Ordinal factors
  • compoments of a factor
  • How to modify a factor?
  • Updating Factors
  • methods for handling factors

Analysis - (PCA)

  • Ensembles, Neural Networks
  • Support Vector Machine - (SVM)
  • Nearest Neighbours - (k- NN)
  • Feature Selection - (aka Variable Reduction)
  • Indexation / Cataloguing * and Collaborative Filtering *
  • (Geo-) Spatial Modelling, Graphs
  • Recommendation Engine *
  • Search Engine * and Attribution Modelling *
  • Rule System,Linkage Analysis
  • Association Rules,Scoring Engine
  • Segmentation, Predictive Modelling

Chapter 1: Introduction

Familiarity with Azure HDInsight, Familiarity with databases and SQL, introduction to Data Science with Sparki, Get started with Spark clusters in Azure HDInsight, and use Spark to run Python or Scala code to work with data, Getting Started with Machine Learning, Learn how to build classification and regression models using the Spark ML library., Evaluating Machine Learning Models, Learn how to evaluate supervised learning models, and how to optimize model parameters, Recommenders and Unsupervised Models, Learn how to build recommenders and clustering models using Spark ML

Chapter 2: Apache Hadoop

Apache Hadoop, Apache Spark,  Apache Kafka, Apache HBase, Apache Hive LLAP, Apache Storm, Machine Learning

Chapter 3: Apache Spark

Run interactive queries | Visualize data | Machine learning

Chapter 4: Apache Kafka

Structured streaming with Kafka | Use with Storm on HDInsight | Use Kafka Producer and Consumer APIs

Chapter 5: Apache HBase

Create HBase clusters in a VNET | Use Apache Phoenix | Connect to Spark

Chapter 6: Interactive Query:

Connect with Power BI using Direct Query

Chapter 7: Apache Storm

Create Storm topology in Java | Deploy Storm topologies on HDInsight | Write from Storm to Data Lake Storage

Chapter 8: ML Services :

Use R Tools for Visual Studio

Chapter 9'; Hive ODBC

Open Database Connectivity (ODBC) API, a standard for the Hive database management system, enables ODBC compliant applications to interact seamlessly with Hive through a standard interface.

Chapter 10: HD Insight with Excel

Microsoft Excel is the most popular data analysis tool and connecting it with big data is even more interesting for our customers. Azure HDInsight Interactive Query cluster can be integrated with Excel with ODBC connectivity

Chapter 11: HD Insight with Power BI

Microsoft Power BI Desktop has a native connector to perform direct query against HDInsight Interactive Query cluster. You can explore and visualize the data in interactive manner. To learn more see Visualize Interactive Query Hive data with Power BI in Azure HDInsight and Visualize big data with Power BI in Azure HDInsight.

Chapter 12: Apache Zeppelin

Apache Zeppelin interpreter concept allows any language/data-processing-backend to be plugged into Zeppelin. You can access Interactive Query from Apache Zeppelin using a JDBC interpreter.


Chapter 1: Basics of Machine Learning

  • Basics of Machine Learning
  • What You Will Learn in This Section
  • The course slides for all sections
  • Important Message About Udemy Reviews
  • Why Machine Learning is the Future?
  • What is Machine Learning?
  • Understanding various aspects of data - Type, Variables, Category
  • Common Machine Learning Terms - Probability, Mean, Mode, Median, Range
  • Types of Machine Learning Models - Classification, Regression, Clustering etc

Chapter 2: Started with Azure ML

  • Getting Started with Azure ML
  • What You Will Learn in This Section?
  • What is Azure ML and high level architecture.
  • Creating a Free Azure ML Account
  • Azure ML Studio Overview and walk-through
  • Azure ML Experiment Workflow
  • Azure ML Cheat Sheet for Model Selection

Chapter 3: Data Processing

  • Data Processing
  • Data Input-Output - Upload Data
  • Data Input-Output - Convert and Unpack
  • Data Input-Output - Import Data
  • Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns
  • Apply SQL Transformation, Clean Missing Data, Edit Metadata
  • Sample and Split Data - Partition or Sample, Train and Test Data

Chapter 4: Classification

  • Classification
  • Logistic Regression - What is Logistic Regression?
  • Logistic Regression - Build Two-Class Loan Approval Prediction Model
  • Logistic Regression - Understand Parameters and Their Impact
  • Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score
  • Logistic Regression - Model Selection and Impact Analysis
  • [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model
  • Decision Tree - What is Decision Tree?
  • Decision Tree - Ensemble Learning - Bagging and Boosting
  • Decision Tree - Parameters - Two Class Boosted Decision Tree
  • Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction
  • Decision Forest - Parameters Explained
  • Two Class Decision Forest - Adult Census Income Prediction
  • Preview
  • Decision Tree - Multi Class Decision Forest IRIS Data
  • SVM - What is Support Vector Machine?
  • SVM - Adult Census Income Prediction

Chapter 5: Hyperparameter Tuning

  • Hyperparameter Tuning
  • [Hands On] - Tune Hyperparameter for Best Parameter Selection
  • Hyperparameter Tuning

Chapter 6:

  • Deploy Webservice
  • Azure ML Webservice - Prepare the experiment for webservice
  • Deploy Machine Learning Model As a Web Service
  • Use the Web Service - Example of Excel
  • AzureML Web Service

Chapter 7:

  • Regression Analysis
  • What is Linear Regression?
  • Regression Analysis - Common Metrics
  • Linear Regression model using OLS
  • Linear Regression - R Squared
  • Gradient Descent
  • Linear Regression: Online Gradient Descent
  • Experiment Online Gradient
  • What is Regression Tree?
  • What is Boosted Decision Tree Regression?
  • Decision Tree - Experiment Boosted Decision Tree
  • Regression Analysis

Chapter 8:

  • Clustering
  • What is Cluster Analysis?
  • Cluster Analysis Experiment 1
  • Cluster Analysis Experiment 2 - Score and Evaluate
  • Clustering or Cluster Analysis

Chapter 9:

  • Data Processing - Solving Data Processing Challenges
  • Section Introduction
  • How to Summarize Data?
  • Summarize Data - Experiment
  • Outliers Treatment - Clip Values
  • Outliers Treatment - Clip Values
  • Clean Missing Data with MICE
  • Clean Missing Data with MICE
  • SMOTE - Create New Synthetic Observations
  • Preview
  • [Hands On] - SMOTE
  • Data Normalization - Scale and Reduce
  • Data Normalization
  • PCA - What is PCA and Curse of Dimensionality?
  • Principal Component Analysis
  • Join Data - Join Multiple Datasets based on common keys
  • Join Data - Experiment

Chapter 10:

  • Feature Selection - Select a subset of Variables or features with highest impact
  • Feature Selection - Section Introduction
  • Pearson Correlation Coefficient
  • Chi Square Test of Independence
  • Kendall Correlation Coefficient
  • Spearman's Rank Correlation
  • omparison Experiment for Correlation Coefficients
  • Filter Based Selection - AzureML Experiment
  • Fisher Based LDA - Intuition
  • Fisher Based LDA - Experiment
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