Business Intelligence and Data Analytics Course Syllabus
Have Queries? Ask our Experts
+91 89256 88858
Quick Enquiry
Discover business intelligence and data analytics with SLA Institute, the top institute for Business Intelligence and Data Analytics Training in Chennai. Our course covers essential topics to help you analyze data effectively and derive valuable insights. Learn data visualization, statistical analysis, and tools like Power BI and Tableau for creating informative reports and dashboards. Gain practical experience with large datasets, predictive analytics, and implementing business intelligence solutions. SLA Institute provides outstanding training and support to prepare you for roles where data drives decisions. Join our Business Intelligence and Data Analytics Course with 100% Placement Support to acquire crucial skills, build confidence, and access excellent job opportunities in this critical field. Begin your journey towards a successful career in business intelligence and data analytics with SLA Institute.
Course Syllabus
Download SyllabusCORE PYTHON
- 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
- 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
- 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
- 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
- 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
- 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
- Date and Time Functions
- Text Functions
- Logical Functions
- Math & Statistical Functions
VISUALIZATIONS
- 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
- 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