Applied Statistics Real World Problem Solving
What you'll learn
- 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
Requirements
- 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.
Description
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!
Who this course is for:
- 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.
Instructor
- 4.0 Instructor Rating
- 1,132 Reviews
- 70,328 Students
- 46 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.