Sadhvi Academy

• Data Integration:

  • Multiple Data Sources: Combine data from various sources like SQL Server, Access, Oracle, and more.
  • Data Model: Create a data model by importing tables and establishing relationships between them.

• Large Data Handling:

  • In-Memory Analytics Engine: Handle large volumes of data efficiently using the in-memory analytics engine.
  • Compression: Compresses data for faster processing and reduced memory usage.

• Advanced Calculations:

  • Data Analysis Expressions (DAX): Use DAX formulas to perform complex calculations and create custom measures.
  • Calculated Columns: Add columns with custom calculations within the data model.

• Relationships and Hierarchies:

  • Table Relationships: Define relationships between tables to create a cohesive data model.
  • Hierarchies: Create hierarchies to drill down into data easily (e.g., Year > Quarter > Month > Day).

• Data Refresh:

  • Scheduled Refresh: Set up scheduled data refreshes to keep your data up-to-date.
  • Manual Refresh: Manually refresh data connections as needed.

• PivotTables and PivotCharts:

  • Enhanced PivotTables: Create more powerful and flexible PivotTables using the data model.
  • PivotCharts: Visualize data using PivotCharts that update automatically with changes in the PivotTable.

• Key Performance Indicators (KPIs):

  • KPIs: Define and monitor KPIs to track performance against goals.

• Role-Based Security:

  • Row-Level Security: Implement row-level security to restrict data access based on user roles.

• Slicer and Timeline Filters:

  • Slicers: Use slicers to filter data interactively.
  • Timelines: Filter data by date periods with timeline controls.

• Integration with Power BI:

  • Power BI Integration: Export data models to Power BI for further analysis and visualization.

Hands-On Practice: Start by practicing basic SQL queries like SELECT, INSERT, UPDATE, and DELETE on sample databases.

Online Courses: Enroll in online courses on platforms like Coursera, Udemy, or Khan Academy to learn SQL fundamentals.

Tutorials and Documentation: Utilize tutorials and official documentation from database vendors like MySQL, PostgreSQL, or SQL Server.

Interactive Platforms: Use interactive platforms like Codecademy or Mode Analytics' SQL Tutorial for interactive learning experiences.

Join Data Science Projects: Apply SQL skills to real-world data science projects to gain practical experience and solve data-related problems.

Learn Advanced Topics: Progress to learning advanced SQL concepts such as joins, subqueries, aggregations, and window functions for more complex data analysis.

Practice Optimization: Learn techniques for optimizing SQL queries, indexing, and performance tuning to improve query efficiency.

Seek Feedback: Share your SQL queries with peers or mentors for feedback and suggestions on improving your coding style and efficiency.

Stay Updated: Stay informed about new SQL features, best practices, and industry trends by following blogs, forums, and online communities.

Continuous Learning: Make SQL learning an ongoing process by regularly practicing, exploring new concepts, and applying them to different datasets and scenarios.

The prerequisites for a machine learning course include a stronghold of linear algebra, calculus, programming skills, probability, and statistics.

Most of the topics can be quickly understood while working on solved projects by ProjectPro that resemble how solutions are implemented in the real world.