Microsoft Data Science Online Training

This Microsoft Data Science Online 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

DATA SCIENCE TRAINING - Plans

  Plan A Plan B Plan C
Courses Included T-SQL,
Power BI
Python
R DataScience
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-Yes 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 9 Weeks 12 Weeks
Total Course Fee INR 15000/-
USD 250
INR 20000/-
USD 300
INR 40000/-
USD 550

Training Schedules

      Schedule (IST) Start Date  
1 9 AM - 10 AM August 6th Register
2 11:30 AM - 12:30 PM July 30th Register
3 6:30 PM - 7:30 PM Started Register
4 8 AM - 10 AM (wkn) August 3rd Register

All Session Are Completely Practical & Real Time

 

Data Science Training Course Contents:

PART 1 Of 2: SQL Server Basics, Queries, Stored Procedures and Database Development

Module I: SQL Basics, SQL Server Concepts

(For Plan B)

Module II: T-SQL Queries

(For Plan B)

Module III: T-SQL Programming, Banking Project

(For Plan B)

DAY 1: INTRODUCTION, INSTALLATION

  • Data, Databases and RDBMS Software
  • Database Types : OLTP,DWH,OLAP,HTAP
  • Microsoft SQL Server Advantages, Use
  • DB Engine, BI, Data Science Components
  • SQL : Purpose, Real-time Usage Options
  • SQL Versus Microsoft T-SQL [MSSQL]
  • Microsoft SQL Server - Career Options
  • Real-time Projects & Job Responsibilities
  • SQL Server @ Cloud: Azure, AWS, G Cloud
  • Versions and Editions of SQL Server
  • SQL Server and SSMS Installation Plan
  • SQL Server Pre-requisites : S/W, H/W
  • System Configuration Checker (SCC) Tool
  • SQL Server 2019 : Installation [Overview]

DAY 7: 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
  • Using Joins for DB Metadata Audits
  • Joining more than 2 Tables in T-SQL
  • Joining Tables with Query Conditions
  • Joining Unrelated Tables, Join Options

DAY 14: STORED PROCEDURES - Level 2

  • Table Valued Parameters (TVP) - Usage
  • SQL Injection Attacks - Type Precautions
  • READONLY Parameters - Stored Procedures
  • OUTPUT Parameters - Stored Procedures
  • User Defined Data Types, Real-time Use
  • Dynamic Data Insertions with Stored Procs
  • Table Cloning, Data Inserts @ Table Variables
  • CTE : Common Table Expressions
  • Real-time Scenarios with CTEs - Usage
  • ROW_NUMBER() with CTE Queries
  • Using CTEs for Avoiding Self Joins
  • Using CTEs for Avoiding Sub Queries
  • Recursive CTEs and ANCHOR Element
  • Termination Checks in Recursive CTEs

Day 2: INSTALLATIONS [DETAILED]

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

DAY 8: JOINS, T-SQL QUERIES - Level 2

  • GROUP BY Queries and Aggregations
  • GROUP BY Queries with Having Clause
  • Group By Queries - Query Design Rules
  • ROLLUP( ) & CUBE( ) Summary Values
  • GROUPING() Function for Row Status
  • Replacing Nulls: ISNULL, COALESCE
  • Joining Tables with Group By, Having
  • Sub Queries and Alternatives to Joins
  • Using Joins with Group By Queries
  • Using Joins with Nested Sub Queries
  • Sub Queries with Joins and Group By
  • Using UNION and UNION ALL in Queries
  • Nested Sub Queries with Group By, Joins
  • Comparing WHERE, HAVING Conditions

DAY 15: STORED PROCEDURES - Level 3

  • Views on Tables - SCHEMABINDING
  • ENCRYPTION and CHECK OPTION
  • Cascaded Views, Encrypted Views
  • Updatable Views, Joins with Triggers
  • Error Handling in T-SQL: TRY & CATCH
  • Error Handling, THROW in Procedures
  • Stored Procedures - WITH RESULT SETS
  • Cursors - Benefits, Cursors in SProcs
  • ForwardOnly, Scroll & Local Cursors
  • Static, Dynamic & Global Cursors
  • Keyset Cursors and @@FetchStatus
  • Nesting of Stored Procedures - Dynamic
  • Data Formatting and WHILE Loops
  • Using Temporary Tables for Formatting

Day 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 Conditions
  • LIVE QUERY STATISTICS in SSMS
  • Table Scan Properties in SQL Server

DAY 9: JOINS, T-SQL QUERIES - Level 3

  • Cast, Convert, DateAdd, DateDiff Functions
  • Date & Time Styles, Data Formatting
  • Using Date and Time Formats in Queries
  • String Functions: SUBSTRING,REPLICATE
  • CHARINDEX, PATINDEX, LEFT, RIGHT
  • LEN, STUFF, LTRIM, RTRIM, REVERSE
  • DIFFERENCE, SOUNDEX, STRING_SPLIT
  • WHEN MATCHED and NOT MATCHED
  • Incremental Loads with MERGE Statement
  • IIF(), CASE with WHEN and ELSE, END
  • FETCH - OFFSET, NEXT ROWS, Order By
  • Using PIVOT Function and FOR Values
  • ROW_NUMBER() and RANK() Queries
  • Dense Rank and Partition By Queries

DAY 16: FUNCTIONS - Level 2

  • Functions : Types, Real-world Usage
  • Inline Functions, Multi Line Functions
  • Looping Concepts in SQL Server
  • WHILE Loop Queries and UNPIVOT
  • GROUPING SETS and OUTPUT Function
  • EXISTS and RAISEERROR Functions
  • TRY_CONVERT, TRY_PARSE Functions
  • Using BULK INSERT & BULKCOLUMN
  • OPENROWSET For Data Import, CAST
  • OPENJSON For JSON Data Formats
  • JSON Files - Data Import into SQL DB
  • Json $Tag Notations, SELECT .. INTO
  • XML Options in T-SQL Queries, Joins
  • XML AUTO, XML RAW and XML PATH

DAY 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 Versus VARCHAR Data Types
  • GO Statement, SQL BATCH Concept
  • DISTINCT, TOP, FETCH, ORDER BY
  • Basic Sub Queries with SELECT
  • UPDATE and DELETE Statements
  • TRUNCATE, ALTER, ADD and DROP
  • Table Scans, Measuring Query Time
  • CLIENT STATISTICS and Query Trails

DAY 10: Views, Functions, Procedure Basics

  • Views : Types, Usage in Real-time
  • System Predefined Views and Audits
  • Listing Databases, Tables, Indexes
  • Functions : Types, Usage in Real-time
  • Scalar, Inline and Multi-Line Functions
  • System Predefined Functions, Audits
  • DBId, DBName, ObjectID, ObjectName
  • Variables & Parameters in SQL Server
  • Procedures : Types, Usage in Real-time
  • User & System Predefined Procedures
  • Parameters and Dynamic SQL Queries
  • Sp_help, Sp_helpdb and sp_helptext
  • Sp_recompile, sp_pkeys, sp_rename
  • Compare Views, SPs and Functions

Day 17: Database, Index Architecture

  • Database Architecture - Detailed
  • Primary File, Secondary Files [mdf, ndf]
  • Database Log Files (T-LOG) For Audits [ldf]
  • Data Files, Log Files, LSN & VLF
  • Transaction Log File [LDF] & LSN
  • Filegroups : ReadWrite & Read Only
  • Indexes: Architecture and Types
  • Clustered and Non Clustered Indexes
  • Included and ColumnStore Indexes
  • FILTERED and COVERING Indexes
  • UNIQUE Indexes, Online Indexes
  • B Tree Structure, IAM Page [Root]
  • Indexed Views / Materialized Views
  • Pages, Extents, and Checkpoints

DAY 5: SQL Basics 3, Server Architecture

  • SQL Server Architecture Components
  • TDS Packets (N/W) in Client - Server
  • 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 Components in SQL OS
  • Temporary Tables : Real-time Use
  • Local and Global Temporary Tables
  • Schemas : Real-time Usage, Creation
  • Schema - Table Transfer. 2P, 3P Naming

Day 11: Triggers, Transactions, DTC

  • Triggers - Purpose, Real-world Usage
  • FOR/AFTER Triggers - Real time Use
  • INSTEAD OF Triggers - Real time Use
  • INSERTED, DELETED Memory Tables
  • Enable Triggers and Disable Triggers
  • Database Level, Server Level Triggers
  • Auditting Triggers and Real World Use
  • Transactions : Types, ACID Properties
  • EXPLICIT & IMPLICIT Transactions
  • COMMIT and ROLLBACK Statements
  • Query Blocking Scenarios @ Real-time
  • Open Transctions in Real-world, Impact
  • NOLOCK and READPAST Lock Hints
  • Lock Hints, Joins @ T-SQL Queries

DAY 18 - 20: REAL-TIME PROJECT (BANKING)
Includes 2500 Lines of Code (COMPLETELY SOLVED).

Phase 1: DATABASE DESIGN
  • Understanding Project Requirements
  • End to End Project Work Flow
  • Naming Conventions in Real-time
  • Table Schemas : Creation and Use
  • Implementing Normal Forms (OLTP)
  • Computed Columns and Data Types
  • SQL_Variant, Bit, sysname Data Types
  • Email and Phone Number Validations
  • Data Types Conversions, Validations

Phase 2: QUERY DESIGN
  • Joining Tables for Reports
  • Views with JOIN Options
  • Implementing Indexed Views
  • Using PIVOT Tables in Queries
  • Using Functions for Queries
  • Dynamic Conditions in Queries
  • Parameterized Queries in T-SQL

Phase 3: PROGRAMMING
  • Event Handling , Error Handling
  • Stored Procedures with Transactions
  • Error Handling, Event Handling Options
  • Transaction Nesting, Save Points
  • Stored Procedures with Tables
  • Stored Procedures with Views
  • Stored Procedures with Functions
  • Automating DML with Triggers
  • Project Deployments, Project FAQ

   Project Solution Explanation
   Resume Points from the Project
   Interview FAQs from Project

DAY 6 : CONSTRAINTS, INDEXES - BASICS

  • 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
  • Database Diagrams and ER Models
  • Relationships Verification and Links
  • Indexes : Basic Types and Creation
  • Index Sort Options, Search Advantages
  • Clustered and NonClustered Indexes
  • Primary Key and Unique Key Indexes
  • Need for Indexes with working with Keys

DAY 12 : ER MODELS, NORMAL FORMS

  • Normal Forms for Entity Relationships
  • First, Second, Third Normal Forms Usage
  • Boycee-Codd Normal Form : BNCF : Usage
  • 4 NF, EKNF, ETNF. Functional Dependency
  • Multi-Valued, Transitive Dependencies
  • Composite Keys and Composite Indexes
  • 1:1, 1:M, M:1, M:M Relationship Types
  • Self Referencing Keys and Self Joins
  • Adding NOT NULL Property to Columns
  • Adding Primary Key to Existing Tables
  • Adding Foreign Key to Existing Tables
  • Synonyms : Creation and Real-time Use
  • Using Synonyms in Self Join Queries
  • Cascading Keys. UPDATE/DELETE Types
Real-time Case Study - 1 (Sales & Retail)
Objective : DB Design, Table Design, Relations
Involves Purchases, Products, Customers
and Time Data with Various Data Types.
Solution Explanation in Day 13
DAY 13: Real-time Case Study - 2 (Sales & Retail)
Objective : Query Writing, Excel Integration
Writing Queries, Generate Excel Pivot Tables
Excel Pivot Charts, Data Formatting,
ODC Connections, Charts, Data Labelling.

Module I: Power BI Basics, Visuals

Module II: ETL and Data Modeling

Module III: Power BI Service (Cloud)

DAY 1 : POWER BI INTRO, INSTALL

  • Need for Big Data and BI Technologies
  • Power BI : Self-Service BI, Usage Scope
  • Data Analyst, Data Scientists Job Roles
  • Power BI Developer, Admin - Job Roles
  • Power BI Vs MSBI & SSRS Vs Tableau
  • Career Options: SQL + Power BI + MSBI
  • MCSA 70-778 Power BI Examination Plan
  • Power BI Architecture and EcoSystem
  • Report Types and Power BI Licensing Plans
  • Developer Tools, Mobile Apps, Gateways
  • Power BI Cloud Account Creation, Usage
  • Power BI Desktop and Power BI DesktopRS
  • Report Builder and Power BI Mobile Tools
  • Power BI Training Course - Lab Plan

DAY 7 : POWER QUERY - LEVEL 1

  • Power Query [M Language] - Architecture
  • Basic Data Types, Literals and Values
  • M Language : Data Mash Up & Primitives
  • let, source, in statements in M Lang
  • Functions, Parameters, Frowns, Invoke
  • Advanced Editor, Grouping, Load / Unload
  • Query Reference, Query Duplicate, Steps
  • Power Query Transformations Categories
  • Query Combine & Union Transformations
  • Merge Transformation Join Kinds, Grouping
  • Truncate, Replace, Split, Reduce Rows
  • Manage Columns, Aggregation Column Formats
  • Transpose, Reverse Rows Transformations
  • Power Query - Row Count and Replace

DAY 13 : POWER BI CLOUD

  • Power BI Service (Cloud) Architecture
  • App Workspace Creation in Real-time
  • Publish Reports from Power BI Desktop
  • Reports, Datasets in Power BI Cloud
  • Dashboards : Pins, Tiles and Shares
  • Pin Visuals and Pin LIVE Report Pages
  • Dashboard Actions : Metrics, Themes
  • REST APIs and Streamining Datasets
  • Pubnub Datasets, API Tiles @ LIVE Data
  • Report Actions: Metrics, Shares
  • Insights, Q & A, Visual Options
  • Persistent Filters, Cross Filtering
  • Report Edits, Filters, Embed Codes
  • Performance Inspector and QR Codes

DAY 2: REPORT DESIGN CONCEPTS

  • Designing Reports using Power BI Desktop
  • Report Visuals, Fields, Pages and Filters
  • Report, Data and Relationship Options
  • Enter Data and Get Data in PBI Canvas
  • Power BI Visualizations & Types, Reports
  • SPOTLIGHT, Web / Mobile, View Options
  • PBIX and PBIT File Formats. Differences
  • Table Visual : Grid Properties and Pages
  • Visual Options : Export and Focus Mode
  • Basic Modelling - Summary,Format,Currency
  • Visual Interactions & Values in Power BI
  • Edit Interactions - Filter, Slice Options
  • Slicer : Number, Text and Date Conditions
  • Visual Sync Property and Limitations

DAY 8 : POWER QUERY - LEVEL 2

  • Data Type Detections and Conversions
  • Fill Up/Fill Down, Rename, Move, Split
  • PIVOT and UNPIVOT Transformations
  • FORMAT, FILTER and ConvertToList()
  • EXTRACT, PARSE, NUMBER, COMBINE
  • DELETE and DELETE UNTIL END
  • Step Edits, New Steps, Step Inserts
  • Working with Date, Time Transformations
  • Deriving Year, Quarter, Month, Day
  • Add Column / Custom Column Transformation
  • Columns From Examples, Index Columns
  • JSON, XML and Query Combine - M Language

DAY 14 : POWER BI CLOUD, EXCEL

  • Defining Datasets in Cloud : Imports
  • Defining Workbooks in Cloud : Uploads
  • Reports from Excel Imports and Datasets
  • Dashboards from Workbooks, Excel Online
  • Creating and Using Content Packs
  • Organizational, Service Content Packs
  • Publishing App Workspace, Updates
  • Using Excel with Power BI Reports
  • Using Excel Analyzer in Power BI
  • Using Excel Publisher in PBI Cloud
  • Excel with Power BI Profiles, Pivot
  • Excel ODC Connections - Power Pivot
  • Connect Options : Excel to PBI Cloud
  • DAX Measures with Excel Analyser

DAY 3 : HIERARCHIES, FILTERS

  • Grouping and Binning with Fields
  • Bin Size and Biz Limits (Max, Min)
  • Creating Hierarchies. DrillDown Reports
  • Drill Thru Reports and Conditional Filters
  • Expand, Expand All Down, Goto Next Level
  • Drill Up, Drill Down. Exclude and Include
  • See Data, Export Data, See Records Options
  • Filters : Types and Usage in Real-time
  • Visual Filters, Page Filters, Report Filters
  • Drill-thru Filters with Hierarchy Levels
  • Basic, Advanced, TOP N Filters - Usage
  • Filtering at Category Level, Summary Level
  • Slicer Versus Filters - Comparisons

DAY 9 : POWER QUERY - LEVEL 3

  • Parameter Creation, Edits and Real-time Use
  • Parameter Data Types, Default / Current Values
  • Static Lists, Dynamic Lists For Parameters
  • Creating Parameters in Data Set Queries
  • Converting Columns to Lists in Power Query
  • Dynamic Dropdowns, Multi Valued Parameters
  • Data Conversions and toText() Functions
  • Data Modeling Options - Custom Columns
  • Columns From Examples, Indexed Column
  • Conditional Column, Custom Functions
  • Variables, Parameters - Power Query
  • IF..ELSE Conditions in Power Query
  • Loops and Cases in Power Query Expressions
  • Using Power Query Result in Power BI

DAY 15 : POWER BI CLOUD (ADMIN)

  • Power BI Data Gateway - Architecture
  • Gateway Installation, Configuration
  • Single Sign On Security, Kerberos
  • Incremental Loads and Privacy Levels
  • PBIEngw Service and ODG Logs, Audits
  • DataFlows Creation - Gateway Connections
  • Using Data Flows and Power BI Datasets
  • Security Levels in Power BI Cloud
  • Dashboard Security, Report Security
  • Dataset Security - Read and Reshare
  • App Workspace Security Management
  • Content Pack Security, Featured Items
  • Creating, Managing Alerts in Cloud
  • Row Level Security (RLS) with DAX

DAY 4 : BOOKMARKS, BIG DATA

  • Using Buttons, Images in Power BI Canvas
  • Bookmarks in Power BI Desktop - Usage
  • Buttons, Actions and URLs with Text
  • Using Bookmarks for Visual Filters
  • Using Bookmarks for Page Navigations
  • Using Selection Pane with Bookmarks
  • Buttons, Images in Power BI Desktop
  • Power BI Reports with OLTP Databases
  • Power BI Reports with OLAP Databases
  • Power BI Reports with DWH Databases
  • Power BI Reports with Big Data Sources
  • On-premise, Azure Cloud Database Access
  • Import and Direct Query with Power BI
  • Connect LIVE options in OLAP Databases

DAY 10 : DAX FUNCTIONS - LEVEL 1

  • DAX : Importance and Real-time Usage
  • DAX as library of Functions, Operators
  • Real-world usage of DAX with Power BI
  • Formulation Rules and DAX Data Types
  • In-Memory xVelocity Vertipaq Engine
  • Syntax, Functions, Context Options
  • ROW Context and Filter Context in DAX
  • Creating and Using Measures with DAX
  • Creating and Using Columns with DAX
  • Data Modeling Options in DAX
  • Detecting & Adding Relations for DAX
  • Power BI DAX Functions - Types, Usage
  • Quick Measures in DAX - Auto validations
  • Deciding / Identifying Measures & Columns

DAY 16: Report Server, Mobile Reports

  • Power BI Report Server - Installation
  • Report Server Databases and Web URLs
  • Hybird Cloud [Tenant] Configuration
  • Deploying Reports to Power BI Server
  • Report Builder Paginated Reports, Upload
  • SQL Server Agent, Credentials, Pins
  • Paginated Reports Vs Interactive Reports
  • Power BI Mobile Reports - Configurations
  • Mobile Report Design Tools, Data Sources
  • Power BI Mobile Elements and Grid Counts
  • Excel Data for Power BI Mobile Reports
  • Report Server Sources and Report Hosting
  • Navigation Options with Power BI Mobile
  • Mobile Reports Vs Interactive Reports

DAY 5 : POWER BI VISUALS - LEVEL 1

  • Table Visual and Matrix Visuals: Styles
  • Column Formatting, Conditional Formatting
  • Divergent Colors, Grids, Groups, Totals
  • Chart Reports : Axis, Legend, Value, Labels
  • Stacked Bar Chart, Stacked Column Chart
  • Clustered Bar and Clustered Column Chart
  • 100% Stacked Bar and Stacked Column Charts
  • Line Chart, Area Chart, Stacked Area Chart
  • Ribbon Chart and Scater Chart : Play Axis
  • Match Series, Plot Area, Color Saturation
  • Waterfall Chart - Sentiment Colors
  • Breakdown Count - Decrease / Increase
  • Join Types, Lines: Round, Bevel, Miter
  • Shapes and Markers in Power BI Visuals

DAY 11 : DAX FUNCTIONS - LEVEL 2

  • Date and Time & Text Functions
  • Time Intelligence Functions in DAX
  • Logical & Mathematical Functions
  • Data Modeling with DAX. Creating Roles
  • SUM and DATEDIFF Examples in DAX
  • TODAY, DATE, DAY Arguments with DAX
  • DIVIDE, CALCULATE, Variables
  • IF..ELSE..THEN, RELATED & COUNTROWS
  • SELECTEDVALUE, FORMAT Functions
  • CALCULATE, SUM, ALL Functions
  • FILTER with ROWCOUNT, USERELATIONSHIP
  • EARLIER for Time Based Comparisons
  • Using Variables in DAX Computations
  • Considerations for Quick Measures

DAY 17: POWER BI INTEGRATIONS

Power BI with MSBI Integrations
  • Power BI with MSBI - SSAS OLAP Database
  • Multidimensional, Tabular Mode Databases
  • Import Data and Connect LIVE Options
  • Using MDX & DAX with Power BI Reports
  • Excel Integration with Power BI, SSAS
  • Converting SSRS Reports to Power BI
Power BI for Data Scientists
  • Enabling R Script in Power BI, RMAN
  • Installing R Studio, FORECAST Packages
  • Using R Script Visuals in Power BI
  • Python Visuals in Power BI
  • ETL, Data Modeling with Python

DAY 6 : POWER BI VISUALS - LEVEL 3

  • Tree Map Visual and Funnel Visual
  • Gauge, Donut, Single Row, MultiRow Card
  • Callout Values in KPI Reports and Use
  • Indicator, Trend and Target Goals in KPIs
  • Map Reports and Filled Map Reports
  • ArcGIS Maps - Latitudes and Longitudes
  • Data Points, Series and Data Analytics
  • Constant Lines and Threshold Values
  • Marketplace Visuals and PBIVIZ Files
  • HeatMaps - Visual, Real-time Usage
  • FlowMaps - Visual, Real-time Usage
  • Table HeatMaps with Real-time Usage
  • GanttChart - Visual, Real-time Usage
  • Power BI Infographics, Custom Visuals
  • PBI Infographics with Big Data Sources
  • Custom Visual Limitations & Migrations

DAY 12 : DAX FUNCTIONS - LEVEL 3

  • AVERAGEX and AVERAGE in DAX
  • KEEPFILTERS, COUNTROWS, RELATED
  • DIVIDE with FILTER, ALLSELECTED
  • ALL, MAX, PARALLELPERIOD, DATEDADD
  • CALCULATE with PREVIOUSMONTH
  • FILTER with ROWCOUNT, USERELATIONSHIP
  • TOTALYTD , TOTALQTD, TOTALMTD
  • SAMEPERIODLASTYEAR, IF, VARIANCE
  • DATESINPERIOD and AVERAGEX
  • DAX Variables, Time Type Dimensions
  • HOUR, NOW, SWITCH, TRUE - RETURNs
  • DAX Modeling Components - use
  • TABLES, COLUMNS, RELATIONS
  • MEASURES and HIERARCHIES
  • INTELLISENSE with DAX Editors
  • DISTINCTCOUNT and DIVIDE

DAY 18 - 20 : Real-time Project

Case 1: Real-time Project for Power BI
  • Includes Power BI Dev, Data Modeling
  • Includes Power BI Cloud, Report Server

Case 2: Real-time Project for SQL + Power BI
  • Includes Power BI Dev, Data Modeling
  • Includes Power BI Cloud, Report Server
  • Includes SQL Database Queries, Joins

Case 3: Real-time Project for MSBI + Power BI
  • Includes Power BI Dev, Data Modeling
  • Includes Power BI Cloud, Report Server
  • Includes SQL Database Queries, Joins
  • Includes OLAP, DWH and HTAP Data Sources

Chapter 1 : INTRODUCTION TO SCRIPT

  • 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?

Chapter 7 : Python TUPLE

  • 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

  • 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 13 : Machine Learning Basics

  • 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

Chapter 2 : INTRODUCTION TO 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 8 : Python Dictionary

  • 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 14 : Generalized Linear Models in Python

  • Linear Regression
  • Regularization of Generalized Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and
  • performance measures for classification score models

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

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 9 : Python Functions

  • 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 15 : Boosting Algorithms using Python

  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)

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

Chapter 4 : String Handling

  • 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 10 : Python Modules

  • 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 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

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

Chapter 5 : Python Conditional

  • 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 11 : Packages in Python

  • 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 17 : 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

Chapter 6: Python LOOPS

  • 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

  • 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 12 : Python Date and Time

  • 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?

Essential to R programming

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

Functions

  • 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

Packages

  • 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

Vectors

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

Matrices

  • 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

Factors

  • 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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