Master Business Analytics: Statistics & Probability for Data-Driven Decisions

What you will learn:

Understand the fundamentals of business analytics and its workflow.
Master descriptive and inferential statistics.
Become proficient in probability theory and its applications.
Utilize Python libraries (Pandas, NumPy, Matplotlib, SciPy, Seaborn, Scikit-learn) for data analysis.
Calculate key statistical measures (mean, median, mode, standard deviation, variance, etc.).
Perform hypothesis testing and t-tests.
Calculate confidence intervals.
Apply linear and logistic regression for predictive modeling.
Analyze data using ANOVA.
Calculate joint, conditional, and Bayesian probabilities.
Work with various probability distributions (Binomial, Poisson, Normal, Uniform, Exponential).
Perform correlation analysis.
Build data-driven business models and make informed decisions.

Description

Join our comprehensive course on mastering statistics and probability for business analytics! This course isn't just about numbers; it's about transforming data into actionable insights. Designed for aspiring and practicing data analysts and business professionals, this program blends statistical theory with practical Python application. We'll cover essential descriptive and inferential statistics, including hypothesis testing, regression analysis, and ANOVA. You'll also gain a robust understanding of probability concepts like Bayes' Theorem and various probability distributions (Binomial, Poisson, Normal, etc.). Through hands-on exercises and real-world case studies, you'll learn to analyze datasets using Python libraries like Pandas, NumPy, Matplotlib, SciPy, Seaborn, and Scikit-learn. Prepare to make data-driven decisions, predict customer churn, analyze market trends, and optimize business processes with confidence.

This course starts with the fundamentals of business analytics and its workflow, then dives into core statistical concepts, ensuring a strong foundation. You'll learn to calculate descriptive statistics (mean, median, mode, standard deviation, etc.), perform inferential statistics (hypothesis testing, confidence intervals, regression analysis), and master probability theory. The course culminates in applying all your skills to real business challenges using Python for data analysis and modeling, leading to predictive analytics expertise.

We'll cover a wide range of topics, from the basics of data visualization to advanced techniques in regression and predictive modeling. This course provides a practical, application-focused learning experience, designed to help you immediately improve your analytical skills and contribute meaningfully to data-driven decision-making within your organization. Are you ready to unlock the power of data?

Curriculum

Course Introduction & Setup

This introductory section lays the groundwork for the course. The "Introduction" lecture provides an overview of the course content and learning objectives. "Table of Contents" gives a detailed roadmap of the topics to be covered. The "Intended Audience" lecture clarifies who will benefit most from taking this course and what prior knowledge is helpful.

Essential Tools and Datasets

This section focuses on the tools and datasets used throughout the course. The "Tools, IDE, and Datasets" lecture covers the software and resources needed, including Python libraries, the recommended IDE, and where to find suitable datasets. It guides learners through setting up their environment for optimal learning.

Statistics & Probability in Business Analytics

This section explains the role of statistics and probability within the framework of business analytics, detailing how these disciplines aid decision-making in business scenarios. It highlights practical applications and demonstrates the importance of sound statistical reasoning in data-driven decisions.

Data Acquisition from Kaggle

This section provides a practical guide on finding and downloading relevant datasets from Kaggle. The lecture covers techniques for searching, selecting, and downloading data, empowering learners to source their own data for future projects.

Descriptive Statistics: Fundamentals

This section introduces fundamental descriptive statistics. The lecture "Calculating Mean, Median, Mode, Sum, Max, and Min" explains basic statistical measures. Subsequent lectures build upon this foundation, covering standard deviation, variance, range, and quartile analysis, and concluding with data visualization techniques using histograms.

Inferential Statistics: Hypothesis Testing & More

This section focuses on inferential statistics. The "Conducting Hypothesis Testing & T-Test" lecture introduces hypothesis testing and t-tests. Further lectures then demonstrate the calculation of confidence intervals, the application of linear regression for predictive modeling (using house price prediction as an example), and the analysis of variance (ANOVA) to compare means across groups.

Probability: Core Concepts & Applications

This section delves into probability theory. It covers calculating joint and conditional probabilities, applying Bayes' Theorem, and calculating expected values. This section is critical for understanding and quantifying uncertainty in real-world business settings.

Probability Distributions

This section explores various probability distributions. Lectures cover discrete distributions (Binomial and Poisson) and continuous distributions (Normal, Uniform, and Exponential), providing learners with the tools to model different types of data and situations. The application of these distributions to real-world problems is emphasized.

Correlation Analysis & Predictive Modeling

This section focuses on correlation analysis and advanced predictive modeling techniques. Learners will master correlation analysis, including calculating correlation coefficients, and apply logistic regression for a practical customer churn prediction model using real-world data.

Course Conclusion & Next Steps

The final section summarizes the key concepts and techniques learned throughout the course. It provides guidance for further learning and resources for continued skill development, helping learners to solidify their newfound knowledge and plan their future learning journey.