Data Analyst (Excel, SQL, Python, Power BI & Tableau) by Pavithradevi

The Data Analyst Professional Course is a comprehensive, skill-focused online program designed for students, graduates, and working professionals who want to build a strong career foundation in data analytics. This course covers the complete analytics lifecycle from raw data handling to advanced visualization and insights using industry-relevant tools such as Advanced Excel, SQL, Python, Power BI, and Tableau.

Structured into clearly defined modules, the course is suitable for beginners as well as learners with basic technical knowledge who want to transition into data analyst roles. The curriculum emphasizes practical understanding, tool proficiency, and analytical thinking required to work with real-world datasets across domains.

What Students Will Learn

Module 1: Data Analytics Foundations

• Introduction to data analytics and its business applications

• Types and benefits of data analytics

• Data analysis process and data types

• Sampling techniques and sampling variation

• Graphical techniques and data visualization fundamentals

• Data modeling and confidence intervals

• Data analytics using Python libraries

• Data visualization and analytics using Tableau

Module 2: Excel & Advanced Excel

• Data entry, editing, and worksheet management

• Essential and advanced Excel functions and formulas

• Pivot Tables, Power Pivot, slicers, and lookup functions

• Data validation, conditional formatting, and charts

• Text functions for data cleaning and transformation

• Date and time calculations and workday analysis

• Advanced table handling, grouping, and formatting

• Excel-based data visualization and analysis projects

Module 3: SQL

• Database fundamentals and RDBMS concepts

• SQL commands, tables, clauses, and functions

• Joins, subqueries, keys, and set operators

• Database normalization and ER modeling

• Indexes, views, constraints, and stored procedures

• SQL performance optimization and exception handling

• Introduction to NoSQL and comparison with SQL databases

Module 4: Power BI

• Power BI installation and interface overview

• Data transformation using Power Query Editor

• Data modeling and data cardinality

• DAX expressions and custom visuals

• Interactive dashboards and AI visuals

• Power BI Service, security, and organizational sharing

• Python and SQL integration with Power BI

• Animated and sample dashboards for practical exposure

Module 5: Python Programming

• Python basics, data types, and operators

• Control structures, loops, and functions

• Lists, tuples, sets, dictionaries, and strings

• File handling, modules, and exception handling

• Regular expressions and date-time handling

• Python OOP concepts and MySQL integration

• Arrays, threading basics, and practical coding exercises

Module 6: Tableau

• Tableau fundamentals and report creation

• Charts, filters, formatting, and calculations

• Dashboards, actions, and hierarchies

• Groups, bins, sets, and parameters

• Data blending and combining techniques

• Tableau Public and server concepts

• Advanced dashboards and analytics use cases

Teaching Method

This course is delivered through online live classes, combining conceptual explanations with hands-on practice.

Teaching approach includes:

• Tool-based demonstrations and guided practice

• Real-time problem-solving and data analysis exercises

• Step-by-step coverage of each module

• Practical assignments and mini-projects

• Doubt-clearing support during sessions

• Focus on real-world analytical workflows

The structured module-based delivery ensures progressive skill development from fundamentals to advanced analytics.

Why This Tutor

Pavithradevi follows a structured, curriculum-driven teaching approach focused on clarity, practical tool usage, and analytical thinking. The emphasis is on helping learners understand how different data tools work together in real data analyst roles, rather than treating each tool in isolation.

The teaching style supports learners from diverse educational backgrounds and helps them build confidence in handling data independently.

Location Context

As this is a fully online data analyst course, learners from any city or location can enroll and attend live sessions conveniently, making it suitable for students and working professionals alike.

Benefits / Outcomes

After completing this course, students can expect:

• Strong foundation in data analysis concepts and workflows

• Proficiency in Excel, SQL, Python, Power BI, and Tableau

• Ability to clean, analyze, visualize, and interpret data

• Hands-on experience with dashboards and analytical reports

• Job-ready skills for entry-level data analyst and analytics roles

• Confidence to work on academic, professional, or portfolio projects

This course prepares learners for opportunities in data analytics, business analytics, and reporting roles.