Applied Statistics Real World Problem Solving

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Akhil Vydyula

3:02:09

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1. Introduction to Business Statistics

1. Introduction to Data Types and Business Statistics.mp4

02:29

2. Quantitative vs Qualitative Data A Comparative Analysis.mp4

04:02

3. Measures of Central Tendency Mean, Median, and Mode.mp4

06:14

2. Measures of Dispersion and Distributions

1. Understanding Measures of Dispersion.mp4

03:38

2. Introduction to Distributions and the Central Limit Theorem.mp4

30:18

3. Sampling and Z-Scores.mp4

07:44

3. Hypothesis Testing and Correlation

1. Hypothesis Testing and P-Value Interpretation.mp4

13:43

2. T-tests, Confidence Intervals, and ANOVA.mp4

12:23

3. Pearson Correlation Coefficient Explained.mp4

08:28

4. Advanced Statistical Concepts

1. Advanced Hypothesis Testing and Correlation Analysis.mp4

10:25

2. Data Cleaning and Preprocessing Techniques.mp4

09:01

3. Visualizing Data with Histograms and Box Plots.mp4

09:29

5. Statistical Analysis and Visualization

1. Summary Statistics and Variable Relationships.mp4

10:45

2. Correlation and Pair Plots.mp4

08:28

3. Handling High Correlation and Using Heat Maps.mp4

04:28

4. Practical Exercises Pearson Correlation and Hypothesis Testing.mp4

40:34

Description

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

Who is this 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.

What You Need to Know?

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.

More details

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.

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Akhil Vydyula

Instructor's Courses

Hi There!My Name is Akhil Vydyula, I am a Data Scientist I was previously worked on BFSI data analysis and modelling skills to oversee the full-life cycle of development and execution. He possess strong.ability to data wrangling, feature engineering, algorithm development, model training and implementation.SKILLS AND COMPETENCIESExpert knowledge and experience with C/C++/python Programming and SQL.Should be able to learn and Implement new technologies quickly and effectively.Excellent Mathematical Skills, Problem Solving & Logical Skills.Actively Participating in hackathons in various platforms and writing blogs in medium.TECHNICAL SKILLSMachine Learning, Natural Language Processing(NLP),Computer Vision,Regression, Multi LabelClassification.Transfer Learning, Transformers, Ensembles, Stacking Classifiers.AutoML, SQL, Python, Keras, Pandas, NumPy, Seaborn,Matplotlib,Clustering,Recommendation Systems,Time Series Analysis.

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