Business Analysis Course

Job Assistance

Program

Online Lectures

Delivery Mode

English

Language

Course Syllabus

Module 1: Introduction to Business Analytics

Business Analytics OVERVIEW

Benefits of Business Analytics:
Real-World Applications of Business Analytics
Case Studies in Business Analytics
Overview of the analytics process

Introduction to Data

Types of Data On The Basis of Sources
Types of Data On The Basis of Nature
Types of Data On The Basis of Format or Organization
The 5 Vs of Data: the Essence of Data
Data Collection Methods

Module 2: Advance Excel

Introduction to Excel

Overview of Excel Interface and Features.
Understanding Data and Data Types.
Data Cleaning and Preprocessing:
- Removing duplicates.
- Handling missing values.
- Formatting datasets.

Data Manipulation and Management in Excel

Using Excel Functions:
- Lookup functions (VLOOKUP, HLOOKUP).
- Logical functions (IF, AND, OR).
- Aggregation functions (SUMIF, COUNTIF).
Data Validation and Conditional Formatting.
Sorting, Filtering and Pivot Tables.
Working with Excel Tables.

Data Visualization

Creating Charts:
- Bar, Line, Pie, Scatter, and more.
Advanced Visualizations:
- Combo charts, Sparklines.
Conditional Formatting for Visual Insights.
Dashboards:
- Design and automation principles.

Statistical Analysis in Excel

Descriptive Statistics:
- Mean, Median, Mode, Standard Deviation.
Correlation and Regression Analysis.
Hypothesis Testing.
Time Series Analysis.

Advanced Excel Techniques

Using Macros and VBA for Automation.
Solver for Optimization Problems.
Data Analysis ToolPak: Advanced Analytical Functions.
Power Query for Data Transformation.

Business Applications

Financial Analysis: ROI, Break-even Analysis.
Sales and Marketing Analytics: Forecasting, Trend Analysis.
HR Analytics: Attrition Rates, Productivity Analysis.
Supply Chain Analytics: Inventory Management.

Capstone Project

Comprehensive business case analysis.
Data cleaning, visualization, and insights generation.
Designing a dashboard for stakeholders.

Module 3: SQL

Introduction to SQL

Definition and Purpose
Importance in Data Management

Basic SQL Commands
- SELECT Statement
- RANGE Keyword
- Comparison and Logical Operators
- IN Operator
- Wildcard Characters
- LIKE Clause for Pattern Matching
- Constraints in SQL

Advanced SQL Concepts

Aggregation Functions (COUNT, SUM, AVG, etc.)
Functions in SQL (String, Numeric, Date, etc.)
GROUP BY Clause for Grouping Data
ORDER BY Clause for Sorting Results
HAVING Clause for Filtering Grouped Data

SQL Joins

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL JOIN

Conditional Logic in SQL
- CASE Statement for Conditional Processing
- Using CASE in SELECT, WHERE, and ORDER BY clauses

Complex Queries

Writing Complex SQL Queries
Subqueries and Nested Queries
Combining Multiple SQL Commands

Doubt Clearing
- Addressing Common SQL Query Issues
- Troubleshooting SQL Errors

Tips for Efficient SQL Query Writing

Module 4: Python for Business Analytics

Basics of Python
- Introduction to Python
  - Overview of Python and its applications in data analytics.
  - Installing Python and setting up the environment (Anaconda, Jupyter Notebooks).
- Python Syntax and Data Types
  - Basic syntax: variables, data types (integers, floats, strings, booleans).
  - Control structures: if statements, loops (for, while).
  - Functions: defining and calling functions, understanding scope.
- Data Structures
  - Lists, tuples, and dictionaries: creation, manipulation, and common methods.
  - Sets and their operations.
- File Handling
  - Reading from and writing to files (text and CSV).
  - Understanding file paths and context managers.

Data Manipulation with Numpy and Pandas

Introduction to Pandas DataFrames and Series.
Data cleaning: handling missing values, duplicates, and data types.
Data transformation: filtering, sorting, and aggregating data.

Data Visualization

Introduction to Matplotlib and Seaborn.
Creating basic plots: line, bar, scatter, and histogram.
Customizing visualizations: titles, labels, and legends.

Statistical Analysis

Descriptive statistics: mean, median, mode, standard deviation.
Correlation and regression analysis.
Hypothesis testing and confidence intervals.

Module 5: R for Business Analytics

Basics of R
- Introduction to R
  - Overview of R and RStudio.
  - Installing R packages and libraries.
- R Syntax and Data Types
  - Basic syntax: variables, data types (numeric, character, logical).
  - Control structures: if statements, loops (for, while).
  - Functions: defining and calling functions, understanding scope.
- Data Structures
  - Vectors, lists, matrices, and data frames: creation, manipulation, and common functions.
  - Factors and their importance in categorical data analysis.
- File Handling
  - Reading from and writing to files (CSV, Excel).
  - Understanding file paths and using read/write functions.

Data Manipulation with dplyr and tidyr

Data wrangling: filtering, selecting, and mutating data.
Reshaping data: pivoting and unpivoting.
Handling missing data and outliers.

Data Visualization with ggplot2

Introduction to the grammar of graphics.
Creating various plots: bar, line, scatter, and box plots.
Customizing plots with themes and scales.

Statistical Analysis

Basic statistical tests: t-tests, chi-squared tests.
Time series analysis and forecasting.

Advanced R Techniques

Writing functions and using apply family functions.
Introduction to R Markdown for reporting.
Integrating R with other tools (e.g., Shiny for web applications).

Module 6: Tableau for Business Analytics

Introduction to Tableau
- Overview of Tableau and its ecosystem.
- Understanding Tableau Desktop, Tableau Server, and Tableau Public.

2.Connecting to Data Sources

Importing data from various sources (Excel, SQL, etc.).
Data preparation and cleaning within Tableau.

3.Creating Visualizations

Building basic charts: bar, line, pie, and maps.
Using calculated fields and parameters for dynamic analysis.
Creating dashboards and stories for data presentation.

4.Advanced Analytics in Tableau

Using table calculations and level of detail (LOD) expressions.
Implementing forecasting and trend analysis.
Integrating R and Python scripts for advanced analytics.

5.Best Practices for Dashboard Design

Principles of effective data visualization.
Designing user-friendly dashboards.
Performance optimization techniques.

Module 7: Power BI for Business Analytics

1.Introduction to Power BI

Overview of Power BI and its components (Power BI Desktop, Power BI Service).
Understanding the Power BI interface.

2.Data Import and Transformation

Connecting to various data sources (Excel, SQL, web data).
Using Power Query for data cleaning and transformation.
Creating relationships between tables.

3.Creating Reports and Dashboards

Building visualizations: bar charts, line charts, maps, and tables.
Using slicers and filters for interactive reports.
Designing dashboards for effective storytelling.

4.DAX (Data Analysis Expressions)

Introduction to DAX and its syntax.
Creating calculated columns and measures.
Time intelligence functions for date-based analysis.

5.Sharing and Collaboration

Publishing reports to Power BI Service.
Setting up data refresh schedules.
Collaborating with team members and sharing insights.

FAQs

Helping You Understand Find My Tuition

1. What is Business Analysis?

Business Analysts help companies make strategic decisions by analyzing business processes, data, and market trends.

2. Who should enroll in this course?

✅ Graduates looking for a high-paying career

✅ IT & Non-IT professionals switching to Business Analysis

✅ Engineers, MBA graduates, and finance professionals

3. What skills will I learn?

📌 Requirement Gathering & Documentation

📌 Agile & Scrum Methodologies

📌 Stakeholder Management & Communication

📌 Data Analytics & Business Process Modeling

4. What job roles will I be eligible for?

✅ Business Analyst

✅ Product Analyst

✅ Agile Business Consultant

✅ Financial Analyst

5. What is the average salary for Business Analysts?

Entry-level: $65,000 - $85,000 per year
Mid-level: $90,000 - $120,000 per year
Senior-level: $140,000+ per year

6. How does the Pay After Placement model work?

📌 No upfront fees – Learn first, pay later

📌 6-month course – Hands-on, job-focused training

📌 Guaranteed job assistance – Resume prep, interviews, job referrals

📌 Repayment starts only after getting a job above $65,000/year

Recruiters

FAQs

Helping You Understand Find My Tuition

1. What is Generative AI?

Generative AI refers to AI models that create content, such as text, images, code, and music. Our Generative AI Course covers the latest advancements in AI, machine learning, and prompt engineering.

2. Who should enroll in this course?

✅ Graduates & students looking to start a career in AI

✅ Working professionals & developers looking to upskill

✅ Entrepreneurs interested in AI-driven automation

3. What will I learn in this course?

📌 Deep Learning & Neural Networks

📌 NLP & Large Language Models (LLMs)

📌 ChatGPT, DALL-E, and other AI tools

📌 Prompt Engineering & AI Automation

📌 Real-world AI applications & projects

4. What are the career opportunities after this course?

✅ AI Engineer

✅ Machine Learning Engineer

✅ NLP Engineer

✅ AI Research Analyst

✅ Prompt Engineer

5. What is the salary range for AI professionals?

Entry-level: $70,000 - $90,000 per year
Mid-level: $100,000 - $130,000 per year
Senior-level: $150,000+ per year

6. What is the minimum salary threshold for repayment?

You start paying only after securing a job with a salary of $72,000+ per year.

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