Applied Statistics for Real-World Problem Solving in 2025

2025 Applied Statistics Real World Problem Solving

Applied Statistics Real World Problem Solving

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Last Updated: 2025-01-28 10:01:10 English Course Type: Self Learning

Understand and differentiate data types in statistics: Gain a comprehensive understanding of various data types and their applications in business statistics.
Apply measures of central tendency and dispersion: Learn how to calculate and interpret mean, median, mode, standard deviation, and more.
Perform hypothesis testing and confidence intervals: Master the skills needed to conduct hypothesis tests and calculate confidence intervals using real-world da
Analyze relationships between variables: Develop the ability to use correlation coefficients, scatter plots, and advanced statistical techniques to identify and

Applied Statistics: Real World Problem Solving is a comprehensive course designed to equip you with the statistical tools and techniques needed to analyze real-world data and make informed decisions. Whether you're a business analyst, data scientist, or simply looking to enhance your data analysis skills, this course will provide you with a solid foundation in applied statistics.

Key Topics Covered:

Introduction to Business Statistics: Understand the basics of data types and their relevance in business, along with the differences between quantitative and qualitative data.

Measures of Central Tendency: Learn about mean, median, and mode, and their importance in summarizing data.

Measures of Dispersion: Explore standard deviation, mean deviation, and quantile deviation to understand data variability.

Distributions and the Central Limit Theorem: Dive into different types of distributions and grasp the central limit theorem's significance.

Sampling and Z-Scores: Understand the concepts of sampling from a uniform distribution and calculating Z-scores.

Hypothesis Testing: Learn about p-values, hypothesis testing, t-tests, confidence intervals, and ANOVA.

Correlation: Study the Pearson correlation coefficient and its advantages and challenges.

Advanced Statistical Concepts: Differentiate between correlation and causation, and perform in-depth hypothesis testing.

Data Cleaning and Preprocessing: Master techniques for cleaning and preprocessing data, along with plotting histograms and detecting outliers.

Statistical Analysis and Visualization: Summarize data with summary statistics, visualize relationships between variables using pair plots, and handle high correlations using heat maps.

What You'll Gain:

Practical Skills: Apply statistical techniques to real-world problems, making data-driven decisions in your professional field.

Advanced Understanding: Develop a deep understanding of statistical concepts, from basic measures of central tendency to advanced hypothesis testing.

Hands-On Experience: Engage in practical exercises and projects to solidify your knowledge and gain hands-on experience.

Who This Course Is For:

Business Analysts: Looking to enhance their data analysis skills.

Data Scientists: Seeking to apply statistical techniques to solve complex problems.

Students and Professionals: Interested in mastering applied statistics for career advancement.

Prerequisites:

Basic Understanding of Mathematics: No prior programming experience needed.

Interest in Data Analysis: A keen interest in learning how to analyze and interpret data effectively.

By the end of this course, you will be equipped with the skills and knowledge to tackle real-world data problems using applied statistics. Enroll now and take the first step towards becoming proficient in statistical analysis!

Basic understanding of mathematics: A fundamental knowledge of mathematics is helpful but not mandatory.
Interest in data analysis: A keen interest in learning how to analyze and interpret data effectively.
No programming experience needed: You will learn everything you need to know about applied statistics without any prior programming experience.

Business analysts: Professionals looking to enhance their data analysis skills for better decision-making.
Students and professionals: Those interested in mastering applied statistics for career advancement.
Researchers: Academics and researchers needing to apply statistical methods to their work for accurate results.
Data scientists: Individuals seeking to apply statistical techniques to solve complex problems.

Section 1 : Introduction to Business Statistics 3 Lectures 00:12:45
Lecture 1 :
Introduction to Data Types and Business Statistics Preview
Lecture 2 :
Quantitative vs Qualitative Data: A Comparative Analysis
Lecture 3 :
Measures of Central Tendency: Mean, Median, and Mode
Section 2 : Measures of Dispersion and Distributions 3 Lectures 00:41:40
Lecture 1 :
Understanding Measures of Dispersion
Lecture 2 :
Introduction to Distributions and the Central Limit Theorem
Lecture 3 :
Sampling and Z-Scores
Section 3 : Hypothesis Testing and Correlation 3 Lectures 00:34:34
Lecture 1 :
Hypothesis Testing and P-Value Interpretation
Lecture 2 :
T-tests, Confidence Intervals, and ANOVA
Lecture 3 :
Pearson Correlation Coefficient Explained
Section 4 : Advanced Statistical Concepts 3 Lectures 00:28:55
Lecture 1 :
Advanced Hypothesis Testing and Correlation Analysis
Lecture 2 :
Data Cleaning and Preprocessing Techniques
Lecture 3 :
Visualising Data with Histograms and Box Plots
Section 5 : Statistical Analysis and Visualization 4 Lectures 01:04:15
Lecture 1 :
Summary Statistics and Variable Relationships
Lecture 2 :
Correlation and Pair Plots
Lecture 3 :
Handling High Correlation and Using Heat Maps
Lecture 4 :
Practical Exercises: Pearson Correlation and Hypothesis Testing

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AKHIL VYDYULA,

1577 Course Views

16 Courses

Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.

My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.

I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.

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