Nisha Warkhede Data Analyst
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I can teach data analysis from basic to advance with Excel advance, SQL with Mysql, Power BI and Python Required for analysis.
Course Title:
Comprehensive Data Analysis: Excel, SQL, Power BI, and Python

Learning Objectives:
Understand the fundamentals of data analysis and its applications.
Gain hands-on experience in Excel for data cleaning and visualization.
Learn to query databases using SQL for data extraction and manipulation.
Develop dashboards and reports with Power BI for data visualization.
Use Python for advanced analytics, automation, and predictive modeling.
Course Structure:
1. Introduction to Data Analysis (Week 1):

Importance of data analysis in business.
Overview of tools: Excel, SQL, Power BI, Python.
Introduction to datasets – types, structures, and sources.
2. Data Analysis with Excel (Weeks 2-4):

Data Cleaning and Transformation:
Removing duplicates, handling missing data, text functions.
Data Visualization:
Pivot tables, charts, slicers, and conditional formatting.
Statistical Functions:
Descriptive statistics, regression, correlation.
Case Study:
Perform sales analysis and build a summary dashboard.
3. SQL for Data Extraction and Manipulation (Weeks 5-7):

Database Basics:
Tables, relationships, and database design.
SQL Queries:
SELECT, WHERE, GROUP BY, JOINs, and subqueries.
Data Aggregation and Filtering:
Aggregate functions, filtering data using HAVING and ORDER BY.
Case Study:
Extract insights from customer transactions using SQL queries.
4. Data Visualization with Power BI (Weeks 8-10):

Getting Started with Power BI:
Connecting to data sources (Excel, SQL).
Data Modeling:
Relationships, measures, and calculated columns.
Building Dashboards:
Designing interactive dashboards with visual elements.
Case Study:
Create a company performance dashboard with KPIs and insights.
5. Advanced Analytics with Python (Weeks 11-14):

Introduction to Python for Data Analysis:
Libraries: Pandas, NumPy, Matplotlib, Seaborn.
Data Wrangling and Analysis:
Cleaning and transforming data with Pandas.
Exploratory Data Analysis (EDA):
Visualizing patterns and trends.

Final Project (Weeks 15-16):
Students choose a dataset.
Apply Excel for initial analysis.
Extract additional insights using SQL.
Visualize results in Power BI.
Perform predictive modeling with Python.
Present findings and submit a report.
Assessment and Evaluation:
Weekly quizzes and exercises.
Hands-on projects at the end of each module.
Final project presentation and report.

Subjects

  • Data analysis Beginner-Intermediate

  • Data analysis in Excel Beginner-Intermediate

Experience

No experience mentioned.

Education

  • BE (Jan, 2017–Jul, 2021) from Priyadarshini Institute of Engineering and Engineering and Technology Nagpur–scored 78%

Fee details

    ₹300–500/hour (US$3.50–5.83/hour)

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