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BUSINESS ANALYTICS MASTERY : : From Data to Decision

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Our Business Analytics Course is designed to equip learners with the essential skills to analyze data, uncover insights, and make data-driven business decisions. Covering the full analytics life cycle from data collection and cleaning to visualization, predictive modeling, and prescriptive analytics—this program blends theory with practical, real-world applications.

You’ll gain hands-on experience with industry-leading tools such as Statistics ,Power BI , Djanko, Tableau, SQL, Python, and Excel, while working on live datasets and capstone projects. The curriculum also includes domain-specific analytics for marketing, finance, HR, and supply chain, ensuring you can apply your skills across multiple business functions.

By the end of the course, you’ll be able to:

  • Interpret and present complex data in a business-friendly manner.

  • Apply predictive models to solve real-world challenges.

  • Use BI tools to create interactive dashboards and reports.

  • Make strategic, data-backed recommendations for business growth.

Whether you are a student, working professional, or business owner, this course will prepare you to leverage analytics for better decisions and career advancement.

Learning Outcomes

By the end of this course, learners will be able to:

  • Apply analytics tools to address real business challenges

  • Communicate insights effectively to stakeholders

  • Develop predictive and prescriptive models to guide decision-making

  • Analyze real-world datasets to create actionable, data-driven strategies

Overview of Business Analytics and the role of data-driven decision-making
Types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
Functions and responsibilities of business analysts in organizations
Introduction to the analytics life cycle

Understanding data types, sources, and collection methods
Data cleaning, preprocessing, and transformation
Basics of Database Management Systems (DBMS) and SQL
Data warehousing concepts and ETL (Extract, Transform, Load) processes

Descriptive statistics: Mean, Median, Mode, Variance, Standard Deviation
Probability concepts and data distributions
Anova, Hypothesis testing, correlation, and regression analysis
Introduction to time series analysis

Fundamentals of BI tools and dashboards
Principles of effective data visualization
Practical applications using Power BI, Tableau, and Google Data Studio
Data storytelling techniques for business impact

Using Excel for business analytics
Python for data analysis
SQL for data querying and management