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Business Intelligence and Data Analytics Developer Course Syllabus

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Our Business Intelligence and Data Analytics Developer Course Syllabus covers key topics to prepare you for a career in BI and data analytics. You’ll learn data visualization, warehousing, ETL processes, and tools like Tableau, Power BI, and SQL. This course equips you with skills in both front-end and back-end BI and data analytics, readying you for real-world challenges in the field.

Download our Business Intelligence and Data Analytics Developer course syllabus PDF from the best Business Intelligence & Data Analytics Training Institute in Chennai.

DURATION
Real-Time Location Services
3 Months
JOB READY
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Breakdown of Business Intelligence and Data Analytics Course Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
May 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
May 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

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Syllabus for The Business Intelligence and Data Analytics Course

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CORE PYTHON

1

  • Python Introduction & history
  • Color coding schemes
  • Salient features & flavors
  • String handling management
  • Native data types
  • Decision making statements
  • Looping statements
  • Function types
  • OOPs
  • Exception handling
POWER BI INTRODUCTION

2

  • Data Visualization
  • Reporting Business Intelligence (BI)
  • Traditional BI
  • Self-Serviced BI Cloud Based BI
  • On Premise BI
  • Power BI Products
  • Power BI Desktop (Power Query, Power Pivot Power View)
  • Flow of Work in Power BI Desktop
  • Power BI Report Server
  • Power BI Service, Power BI Mobile
  • Power BI Architecture
  • A Brief History of Power BI
POWER QUERY

3

  • Data Transformation
  • Benefits of Data Transformation
  • Shape or Transform Data using Power Query
  • Overview of Power Query / Query Editor
  • Query Editor User Interface
  • The Queries Pane
  • The Data View / Results Pane
  • The Query Settings Pane, Formula
  • Bar Saving the Work
  • Data types
  • Keep Errors Remove Top Rows
  • Remove Bottom Rows
  • Remove Alternative Rows
  • Remove Duplicates, Remove Blank Rows
  • Remove Errors Group Rows / Group By
M LANGUAGE

4

  • IF..ELSE Conditions
  • TransformColumn()
  • RemoveColumns()
  • SplitColumns()
  • ReplaceValue()
  • Table.Distinct() Options and GROUP BY Options
  • Table.Group()
  • Table.Sort() with Type Conversions
  • PIVOT Operation and Table.Pivot ().
  • List Functions Using Parameters with M Language
DATA MODELING

5

  • Data Modeling Introduction Relationship
  • Need of Relationship Relationship Types
  • Cardinality
  • AutoDetect the relationship
  • Create a new relationship
  • Edit existing relationships
  • Make Relationship Active or Inactive
  • Delete a relationship
DAX

6

  • What is DAX
  • Calculated Column, Measures
  • DAX Table and Column Name Syntax
  • Creating Calculated Columns
  • Creating Measures
  • Calculated Columns Vs Measures
  • DAX Syntax & Operators
DAX FUNCTIONS TYPES

7

  • Date and Time Functions
  • Text Functions
  • Logical Functions
  • Math & Statistical Functions
VISUALIZATIONS

8

  • Visualizing Data
  • Why Visualizations
  • Visualization types
  • Create and Format Bar and Column Charts
  • Create and Format Stacked Bar Chart Stacked Column Chart
  • Create and Format Clustered Bar Chart
  • Clustered Column Chart
  • Create and Format 100% Stacked Bar Chart 100%
  • Stacked Column Chart
  • Create and Format Pie and Donut Charts
  • Create and Format Scatter Charts
  • Create and Format Table Visual
  • Matrix Visualization
  • Line and Area Charts
  • Create and Format Line Chart, Area Chart
  • Stacked Area Chart Combo Charts
ADVANCED PANDAS FUNCTIONS

9

  • Group by()
  • Pivot tables()
  • Multi-indexing()
  • merge()
  • concatenate()
  • join()
  • data transformation using apply()
  • map()
  • query()
  • Resampling time series functionality
  • excel writer()
  • pipe()
  • creating dataframes
  • reading CSV files with intrinsic index
  • converting CSV files to dataframes
  • converting dataframes to CSV files
  • converting dataframes to excel file

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