Data Analyst

Business Analytics with Python

Unlock the power of data analysis in business using Python. This comprehensive course will equip you with the essential skills to analyze and visualize data, making informed business decisions.

Hands-on Python data manipulation and cleaning (pandas & NumPy) to prepare and transform real-world business datasets. Applied statistical analysis and basic predictive modeling to generate data-driven insights for business decisions. Data visualization and storytelling using matplotlib, Seaborn, and Plotly to communicate actionable insights and build dashboards.

4.9

Program rating

1,761+

Learners enrolled

48 hrs

Total duration

English,Hindi

Language

This program includes

Live, mentor-led classes Recognized certificate Placement support Hands-on projects Lifetime access

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Live, mentor-led classes
Recognized certificate
Placement support

Program overview

About this program

This Data Analyst program blends live, mentor-led sessions with hands-on projects and real-world case studies, so you build genuinely job-ready skills — not just theory. You'll learn from working industry experts, apply what you learn on practical assignments, and finish with a portfolio, a recognized certificate, and dedicated placement support to help you land the right role.

Unlock the power of data analysis in business using Python. This comprehensive course will equip you with the essential skills to analyze and visualize data, making informed business decisions.

This course covers business analytics concepts and techniques using Python. Participants will learn how to work with data, apply statistical methods, create visualizations, and develop insights for effective decision-making in business. By the end of the course, you will be able to analyze real-world data sets and make data-driven decisions.

Why learners love this program

Hands-on Python data manipulation and cleaning (pandas & NumPy) to prepare and transform real-world business datasets.
Applied statistical analysis and basic predictive modeling to generate data-driven insights for business decisions.
Data visualization and storytelling using matplotlib, Seaborn, and Plotly to communicate actionable insights and build dashboards.

Lectures

Hours

All level

Level

Globally trusted accreditations

Recognitions that power your profile

Trusted by leading accreditation bodies — so your certificate is respected by employers worldwide.

Learning outcomes

What you'll be able to do

Import, clean, and transform business data using Python libraries (pandas, NumPy)

Perform exploratory data analysis and compute descriptive statistics to uncover patterns

Handle missing values, outliers, and data quality issues for reliable analysis

Create clear, effective visualizations with matplotlib, seaborn, and interactive tools (Plotly)

Apply statistical methods including hypothesis testing, confidence intervals, and ANOVA

Build and interpret linear and logistic regression models for prediction and inference

Develop classification and evaluation workflows using scikit-learn (cross-validation, metrics)

Perform time series analysis and basic forecasting (trend, seasonality, ARIMA/exponential smoothing)

Engineer and select features, apply scaling and encoding, and construct reproducible pipelines

Tune models with hyperparameter search and assess performance using robust validation techniques

Translate analytical results into actionable business insights and data-driven recommendations

Design concise dashboards and reports to communicate findings to stakeholders

Understand data ethics, privacy considerations, and best practices for reproducible analysis

Curriculum

Structured modules that build real skills

15 modules · designed for progressive, hands-on learning.

What is Business Analytics?

Define business analytics and explain its role in data-driven decision-making in organizations.

1.00 hrs

Analytics Lifecycle and Use Cases

Describe the analytics lifecycle and identify common business use cases where analytics adds value.

1.00 hrs

Python Installation and Environment Setup

Set up a Python environment and explain the tools commonly used for data analysis, including Jupyter and virtual environments.

1.00 hrs

Basic Syntax and Data Types

Understand Python syntax, core data types, and basic operations for working with data.

1.00 hrs

Control Flow and Functions

Write control flow statements and functions to structure code and automate repetitive tasks.

1.00 hrs

Data Structures in Pandas (Series & DataFrame)

Explain Pandas primary data structures and perform basic indexing and selection operations.

1.00 hrs

Data Cleaning and Preparation

Apply techniques to clean, handle missing values, and standardize datasets for analysis.

1.00 hrs

Transformations and Aggregations

Perform data transformations, group-wise aggregations, and pivot operations to prepare analysis-ready tables.

1.00 hrs

Creating Plots with Matplotlib

Create common plot types (line, bar, scatter) using Matplotlib to visualize key patterns in data.

1.00 hrs

Customizing Figures and Styles

Customize plot aesthetics, labels, legends, and layouts to make visualizations clear and professional.

1.00 hrs

Statistical Plots with Seaborn

Use Seaborn to create statistical visualizations such as distribution plots and categorical comparisons.

1.00 hrs

Advanced Seaborn Techniques

Apply advanced Seaborn features like faceting and theme settings to enhance exploratory visuals.

1.00 hrs

Summary Statistics and Distributions

Compute and interpret summary statistics and distributional measures to characterize datasets.

1.00 hrs

Data Exploration and EDA Techniques

Conduct exploratory data analysis to uncover trends, outliers, and relationships in business data.

1.00 hrs

Hypothesis Testing Basics

Formulate hypotheses and perform basic statistical tests to support decision-making under uncertainty.

1.00 hrs

Confidence Intervals and Correlation

Construct confidence intervals and assess correlation to quantify relationships and estimate parameters.

1.00 hrs

Regression Models

Build and evaluate regression models to predict continuous business outcomes.

1.00 hrs

Classification Models

Train and assess classification models for predicting categorical outcomes and measuring performance.

1.00 hrs

Clustering Methods

Apply clustering techniques to segment data and discover natural groupings.

1.00 hrs

Dimensionality Reduction

Use dimensionality reduction methods to simplify data and improve model performance or visualization.

1.00 hrs

Interactive Charts with Plotly

Create interactive charts that allow users to explore data through hover, zoom, and filter features.

1.00 hrs

Plotly Express and Figure Customization

Leverage Plotly Express for rapid charting and customize figures for presentation and dashboards.

1.00 hrs

Dashboard Layouts and Callbacks

Design Dash app layouts and implement callbacks to create interactive, reactive dashboards.

1.00 hrs

Deploying and Sharing Dashboards

Prepare and deploy Dash applications for sharing results with stakeholders and collaborators.

1.00 hrs

Data Preparation and Exploration

Prepare the sales dataset and perform exploratory analysis to identify key metrics and trends.

1.00 hrs

Deriving Business Insights

Translate analysis results into actionable business insights and recommendations for stakeholders.

1.00 hrs

Project Scoping and Dataset Selection

Define project objectives, select appropriate datasets, and outline success criteria for the final project.

1.00 hrs

Designing Analysis Approach

Plan the analysis workflow, choose methods and visualizations, and assign milestones for project execution.

1.00 hrs

Implementing Analysis and Visuals

Execute the planned analysis, build models and visualizations, and iterate based on findings.

1.00 hrs

Reporting Results and Presentation

Compile findings into a clear report and present results with supporting visuals and recommendations.

1.00 hrs

Recap and Key Takeaways

Summarize core concepts learned and reflect on how to apply them in business contexts.

1.00 hrs

Further Learning Resources and Career Paths

Identify resources and learning pathways to continue skill development and explore career opportunities in analytics.

1.00 hrs

Your credential

The certificate you'll earn

Earn an industry-recognized certificate on successful completion of this program.

Business Analytics with Python certificate

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Reviews

What our learners say

This course helped me see how Python supports decision-making. Data analysis and visualisation exercises made learning interactive and practical.

ISIsha Saxena

Business Analytics with Python helped me think more analytically. Visualising data and drawing insights felt very rewarding.

GAGurleen Aulakh

Business Analytics with Python helped me build confidence in working with data. Cleaning, analysing, and visualising datasets made analytics feel approachable and useful.

KAKritika Arora

Business Analytics concepts became clearer once Python was applied to real datasets. Visualising trends and insights helped me understand patterns quickly.

NANoman Akhtar

I enjoyed how Python was taught alongside real business problems. Cleaning data, analysing trends, and building reports gave me confidence to work with real datasets.

KMKeshav Malhotra

Business Analytics with Python helped me understand how data actually turns into insights. Working with pandas, NumPy, and visualisation libraries made analysis feel practical instead of theoretical.

AAArnav Aggarwal

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