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Data Analytics professional program

A Data Analytics Professional Program is a training program that provides individuals with the skills and knowledge needed to excel in data analytics. It covers topics such as data manipulation, statistical analysis, machine learning, and data visualization to enable participants to extract insights and make data-driven decisions in various industries.

A Data Analytics Professional Program is a comprehensive training program designed to equip individuals with the necessary skills and knowledge to excel in the field of data analytics. It covers a wide range of topics, including data manipulation, statistical analysis, data visualization, machine learning, and data storytelling using SQL, Power BI and Python.

The program typically includes hands-on projects, case studies, and real-world examples to provide practical experience. Participants learn to extract insights from large datasets, make data-driven decisions, and effectively communicate findings to stakeholders.

By completing this program, individuals gain a strong foundation in data analytics and increase their job prospects in industries that rely on data-driven decision-making.

Mode of class

LIVE

Time to Complete

3 Months

Individual Classes

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1 Month

SQL( Structured Query Language)

Gain practical skills in database management, query optimization, and data manipulation, enhancing their employability in data-centric roles and providing a strong foundation for their future career growth.

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1 Month

Power BI

Gain proficiency in data visualization, report creation, and data analysis, enhancing their employability in roles that require effective data-driven decision-making and communication skills.

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1 Month

Python

Gain proficiency in data manipulation, analysis, and visualization using Python, which enhances their ability to perform advanced analytics and leverage the full power of Python for BI purposes.

Course curriculum

Learn SQL, Power BI and Python form basics to advance with professional projects

SQL

  1. Introduction to SQL and Relational Databases
  2. Data Definition Language (DDL): Creating Tables, Constraints, and Indexes
  3. Data Manipulation Language (DML): SELECT, INSERT, UPDATE, DELETE
  4. Filtering Data with WHERE and ORDER BY clauses
  5. Joins and Subqueries
  6. Aggregation Functions: GROUP BY, HAVING
  7. Views and Stored Procedures
  8. Introduction to Performance Tuning and Indexing
  1. Advanced Joins: INNER, LEFT, RIGHT, FULL OUTER
  2. Common Table Expressions (CTEs)
  3. Window Functions
  4. Query Optimization Techniques
  5. Transactions and Concurrency Control
  6. Triggers and Cursors
  7. Working with Dates and Times
  8. Introduction to Database Administration (Backup, Restore, Security)
  1. Normalization and Database Design
  2. Indexing Strategies and Query Optimization
  3. Stored Procedures, Functions, and Triggers
  4. Database Security and User Management
  5. Performance Monitoring and Tuning
  6. Replication and High Availability
  7. Working with Large Datasets and Big Data Technologies (optional)
  8. Best Practices and Advanced Tips and Tricks

Microsoft Power BI

  1. Introduction to Power BI and its components
  2. Importing and Transforming Data
  3. Creating Visualizations: Charts, Tables, and Matrices
  4. Formatting and Customizing Visuals
  5. Creating Calculated Columns and Measures
  6. Creating Relationships between Tables
  7. Building Basic Reports and Dashboards
  8. Publishing and Sharing Reports
  1. Advanced Data Modeling: Hierarchies, Aggregations, and Time Intelligence
  2. Advanced Visualizations: Maps, Gauges, and Cards
  3. Using Power Query for Data Transformation
  4. Advanced DAX Functions and Formulas
  5. Power BI Desktop vs. Power BI Service
  6. Power BI Security and Row-Level Security (RLS)
  7. Advanced Sharing and Collaboration
  8. Performance Optimization and Query Folding
  1. Power BI Premium and Power BI Embedded
  2. Power BI Dataflows and Data Gateway
  3. Power Automate Integration
  4. Advanced Data Analysis Expressions (DAX)
  5. Power BI API and Custom Visuals
  6. Advanced Data Sources: Azure SQL Database, Azure Analysis Services
  7. Power BI Mobile App and Report Distribution
  8. Power BI Best Practices and Tips
  1. Assignments for practice
  2. Mock exams for practice
  3. Registration for PL:300 Microsoft Power BI Data Analyst exam.

Python

  1. Introduction to Python and its syntax
  2. Variables, Data Types, and Operators
  3. Control Flow: Conditionals and Loops
  4. Lists, Tuples, and Dictionaries
  5. Functions and Modules
  6. File Handling
  7. Exception Handling
  8. Introduction to Object-Oriented Programming (OOP)
  9.  
  1. Advanced Data Types: Sets and Collections
  2. Regular Expressions
  3. Working with Files and Directories
  4. Python Libraries: NumPy and Pandas
  5. Data Manipulation and Analysis with Pandas
  6. Error Handling and Debugging Techniques
  7. Working with APIs and Web Scraping
  8. Introduction to Testing and Debugging
  9.  
  1. Advanced OOP: Inheritance, Polymorphism, and Encapsulation
  2. Python Libraries: Matplotlib and Seaborn for Data Visualization
  3. Database Access with Python (SQL and NoSQL)
  4. Concurrency and Multithreading
  5. Web Development with Python: Flask or Django
  6. Machine Learning with Python: Scikit-learn
  7. Deploying Python Applications
  8. Python Best Practices and Code Optimization
  9.  

What is my total cost?

Enquire now on +919785290681

INR 35000 ($425)
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