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Business Analytics: Strategies for Data-Driven Decision Making

Students will explore various analytics tools and techniques, including statistical analysis, predictive modeling, and data visualization. The curriculum emphasizes practical applications, enabling learners to apply their knowledge to real-world business challenges. This course is perfect for professionals aiming to enhance their decision-making capabilities and drive business strategy through robust data analysis.

4.0

4033 ratings
60,458 students enrolled
₹13,000
₹18,000
27% off

Course Highlights

Live Classes

Engage in interactive sessions led by experienced mentors.

Placement Assistance

Gain exclusive access to Skolar's job portal for career opportunities.

Beginner to Advance

No prior experience required; suitable for learners at all levels.

Project Based Learning

Learn through hands-on experience through projects

Industry Expertise

Engage in interactive sessions led by experienced mentors.

Quick Course

Join a comprehensive program spanning 12 weeks

Course Highlights

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Course content

  • Introduction to Business Analytics and its impact on Business.
  • Types and Overview of Analytics: Descriptive, Predictive, and Prescriptive.
  • Data Analytics: Introduction to data analytics, data, information.
  • Introduction to IBM SPSS: Inserting Data and Defining Variables.
  • Data Cleaning: Handling missing values and outliers.
  • Fundamentals of Descriptive Analytics: Mean, Median, SD, Variance.
  • Data Normality: Understanding normality, methods of checking normality.
  • Hypothesis Testing: Meaning of hypothesis, types, and process.
  • RESUME BUILDING WORKSHOP
  • Drafting hypotheses for real-life problems.
  • Parametric Tests: t-test, ANOVA, z-test.
  • Non-Parametric Tests: Chi-square test.
  • Practical implementation of statistical tests on SPSS.
  • Correlation: Types and degrees of correlation.
  • Practical implication of correlation analysis using SPSS.
  • SOFT SKILL DEVELOPMENT WORKSHOP
  • Regression Analysis: Assumptions, regression equation, linear, and logistic regression.
  • Practical implication of regression analysis using IBM SPSS.
  • Practical Implementation of Factor Analysis Using SPSS: Data Import and Manipulation.
  • Descriptive Statistics and Inferential Statistics
  • Customization, Automation, and Data Mining.
  • Chi-square Test of Independence and Contingency Tables.
  • Goodness-of-Fit Chi-square Test and Chi-square Test for Homogeneity
  • ANOVA: One-Way, Two-Way, Factorial, Assumptions, ANCOVA, Repeated Measures.
  • Direct Marketing Techniques, RFM Analysis, and Cluster Analysis.

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4.7

4033 ratings

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Frequently Asked Questions

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