Data Analysis

Dr. Debmallya Chatterjee

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.

What's inside

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.

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Analysis with these activities:

Review probability concepts

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Revisiting the concepts of probability will refresh your knowledge and ensure a solid foundation for the course.

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Review notes or textbooks on probability concepts.
Solve practice problems related to probability.

Compile a repository of data analysis resources

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Gathering and organizing resources will enhance your ability to access and utilize valuable information for data analysis.

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Search for and identify relevant data analysis resources, such as articles, tutorials, and tools.
Create a central repository or database to store and organize these resources.
Categorize and tag resources for easy retrieval.

Practice descriptive statistics calculations

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Engaging in practice drills will enhance your proficiency in calculating descriptive statistics, which is crucial for data analysis.

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Find datasets online or in textbooks.
Calculate measures of central tendency (mean, median, mode).
Calculate measures of dispersion (range, variance, standard deviation).

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Discuss sampling techniques in real-world scenarios

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Engaging in peer discussions will facilitate a deeper understanding of how sampling techniques are applied in various real-world scenarios.

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Form study groups or connect with peers online.
Identify different sampling techniques and their applications.
Discuss the advantages and disadvantages of each technique.

Explore ANOVA applications in research

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Delving into tutorials on ANOVA applications will provide practical insights into its usage in research and enhance your understanding.

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Search for tutorials or articles on ANOVA applications.
Review examples of ANOVA usage in different research fields.
Attempt practice problems or case studies involving ANOVA.

Develop a regression model

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Creating a regression model will allow you to apply your knowledge and gain hands-on experience in predictive modeling.

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Choose a dataset and identify the dependent and independent variables.
Explore different regression techniques and select the most appropriate one.
Build and evaluate the regression model.
Interpret the results and draw conclusions.

Contribute to data analysis projects on GitHub

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Involving yourself in open-source projects will provide practical experience in data analysis and contribute to the community.

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Find data analysis projects on GitHub that align with your interests.
Review the project documentation and identify areas where you can contribute.
Fork the repository and make your contributions.

Develop a data analysis dashboard

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Creating a data analysis dashboard will allow you to apply your skills and gain experience in presenting insights effectively.

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Identify a business problem or opportunity that can be addressed with data analysis.
Gather and analyze relevant data.
Design and develop a data analysis dashboard using visualization tools.
Share and communicate your insights to stakeholders.