Welcome to the exciting world of data analysis and unlock its power in today's dynamic business landscape. This course offers a comprehensive guide to mastering data analysis, transforming it into more than just a valuable skill. Gain essential knowledge and tools to convert data into actionable insights, drive informed decisions, and fuel business growth.
This course is designed for a wide range of individuals, including business professional data analysts, students or simply a curious learner eager to dive into data. This course will give you data-driven decision-making tools to gain a competitive edge. It will enable leveraging advanced technologies and practical tools like MS Excel, and its application to real-world case studies.
Upon completing this course, you will:
--Develop proficiency in applying Quantitative Analysis methods in business contexts.
--Recognize the significance of accurate data for credible results.
--Identify the salient aspects of an undertaking managerial assessment context.
In addition, you can get a head start on PGDM Online, an AICTE-approved master’s level program. This MOOC stacks into the PGDM Online program, an AICTE-approved master’s level program offered by SP Jain Institute of Management Research (SPJIMR), India. It's a premium online program catering to the needs of working professionals in India and the rest of the world.
Join us to unlock the full potential of data analysis and drive your success in the business-oriented world.
Overview
Syllabus
- Course Orientation
- Managerial decision-making is complicated, especially given the fast-paced and ever-changing business landscape. This necessitates that management graduates possess the skills to effectively navigate real-world challenges and employ robust analytical thinking. Such proficiency entails processing data, recognizing assumptions, addressing biases, and confidently making managerial judgments. Employers increasingly seek professionals capable of thriving in both structured and unstructured environments. The debate surrounding whether exceptional managerial acumen can solely stem from excellent analytical skills is gaining traction globally. Clarity of thought, mastery of essential concepts, and the application of sharp reasoning and problem-solving abilities are highly valued. Increasingly, such skills, when combined with innovative and creative thinking, are recognized as integrals components that form a potent combination essential for managerial success. Businesses across sectors stand to gain from training that provides tools for issue resolution, data-driven decision-making, and gaining competitive advantage. With advanced technologies, data analysis has become more efficient, and access to high-end computers and common software has removed barriers to practical solutions. The course will delve into utilization of MS Excel, demonstrating its applications through real-world case studies. Covering fundamental topics such as descriptive statistics, probability, sampling techniques, hypothesis testing, ANOVA, and basics of regression, the course aims to empower individuals to make informed, data-driven decisions and effectively communicate their findings.
- Business Statistics: Descriptive Statistics and Probability
- We begin, the journey as we delve into the essentials of statistics, data types, and scales. You'll learn how to compute and interpret statistical measures in Descriptive Statistics and understand fundamental Probability laws, Bayes' theorem, and business applications.
- Business Statistics: Probability distributions and Sampling
- This week, we continue with the fundamentals of probability, we introduce the concepts of random variables and probability distribution - namely - discrete and continuous. The focus for this course will be on Normal distribution. You will also learn the different types of Samples - Sample selection process and estimation of population mean using sample statistics. Further, you will get an understanding of how Sampling distribution. provides valuable insights into the variability that can be expected by repeatedly drawing samples from the same population.
- Business Statistics : Sampling Distribution, Estimation and Hypothesis Testing
- The Business Statistics module concludes with a discussion on the margin of error and its relation to sample estimates. You'll learn to calculate interval estimates and apply them in business contexts. Additionally, this week we will cover hypothesis testing, which includes formulating null and alternate hypotheses and conducting significance tests to determine criteria for hypothesis rejection. We will further explore the implications of type I and type II errors in this decision-making.
- Analysis of Variance (ANOVA)
- This week we explore statistical inferences involving multiple populations, with a particular emphasis on ANOVA as a method for comparing means among multiple groups or populations. You'll understand ANOVA's significance in assessing group differences and gain insights into interpreting the produced F-statistic. ANOVA facilitates identifying significant differences between group means and comprehending sources of data variation.
- Regression Analysis
- This week we introduce Regression Analysis as we delve into the relationship between dependent and independent variables. You'll discover how Regression analysis estimates the dependent variable values based on known independent variables, aiding in identifying the best-fit line or curve representing data patterns and trends. This analysis facilitates uncovering insights crucial for informed decision-making across various fields.
- Chi-square distribution and Goodness of Fit
- The course wraps up with a unique twist: testing the association of two variables in a contingency table; testing if a variable is likely to come from a particular distribution. We will delve into concepts such as contingency tables, column and row percentages, observed and expected frequencies, and apply Chi-square statistics to test population proportion equality. Additionally, we will learn how to use Chi-square statistics to test the independence of two categorical variables and assess goodness of fit to determine if an observed dataset aligns with a specific distribution.
Taught by
Dr. Debmallya Chatterjee
