Statistics, Probability & EDA for Data Science using Python

Statistics, Probability & EDA for Data Science using Python, available at $54.99, has an average rating of 5, with 18 lectures, based on 1 reviews, and has 4 subscribers.

You will learn about Understand the fundamental concepts of probability theory, including probability distributions, random variables, and basic probability rules. Learn how to summarize and describe datasets using measures such as mean, median, mode, variance, and standard deviation. Dive into the fundamentals terminologies of Inferential statistics. Explore probability distributions such as the Gaussian distribution and learn how to apply them to real-world problems. Gain hands-on experience using Python libraries like NumPy, Pandas, and Matplotlib to perform statistical analysis, visualize data, and interpret results. Understand the concepts of correlation and linear regression, and learn how to use Python to analyze relationships between variables and make predictions. Build probability models for events and experiments, and simulate random processes using Python to understand stochastic phenomena. Apply statistical and probabilistic concepts to real-world datasets and problems, developing the skills needed to tackle data analysis and decision-making tasks Gain expertise in Univariate, Bivariate, and Multivariate Data Analysis, equipping you with comprehensive skills to interpret and manage complex datasets. Discover the power of Seaborn displot, jointplot, and pairplot functions through hands-on examples, mastering univariate, bivariate, and multivariate analysis. Visualize data representations, including Histograms, Normal distributions, Kernel density estimations, Rug plots, Scatter plots, Contour plots and Hex plots. Master the Art of efficiently Visualizing, Analyzing, and Drawing insightful inferences from Heatmaps of large datasets. Discover the Power of Automation to create a Multiple Dynamic Visualizations effortlessly. Discover the art of proficiently analyzing data through Regression analysis, blending theory with practical examples to elevate your understanding & application Explore on a range of other Seaborn functions such as Bar, Count, Strip, Swarm, Box, and Violin plots, vital for Visualizing data distributions & Trends. This course is ideal for individuals who are Individuals looking to enter the field of data science who want to build a strong foundation in statistics and probability using Python. or Those interested in working with data to extract insights and make data-driven decisions would benefit from learning statistical analysis techniques in Python. or Students keen on understanding the statistical principles underlying machine learning algorithms and how they can be implemented in Python. or Undergraduate and graduate students studying computer science, mathematics, statistics, or related disciplines who wish to supplement their coursework with practical skills in statistical analysis and programming. or Working professionals in fields such as finance, marketing, healthcare, or engineering who want to enhance their analytical skills and improve their job prospects by learning Python-based statistical analysis techniques. or Individuals involved in research or academia who need to analyze data and interpret results using statistical methods, and who prefer using Python for its flexibility and extensive libraries. or Hobbyists or individuals from various backgrounds intrigued by data analysis and eager to explore statistical concepts using Python programming. It is particularly useful for Individuals looking to enter the field of data science who want to build a strong foundation in statistics and probability using Python. or Those interested in working with data to extract insights and make data-driven decisions would benefit from learning statistical analysis techniques in Python. or Students keen on understanding the statistical principles underlying machine learning algorithms and how they can be implemented in Python. or Undergraduate and graduate students studying computer science, mathematics, statistics, or related disciplines who wish to supplement their coursework with practical skills in statistical analysis and programming. or Working professionals in fields such as finance, marketing, healthcare, or engineering who want to enhance their analytical skills and improve their job prospects by learning Python-based statistical analysis techniques. or Individuals involved in research or academia who need to analyze data and interpret results using statistical methods, and who prefer using Python for its flexibility and extensive libraries. or Hobbyists or individuals from various backgrounds intrigued by data analysis and eager to explore statistical concepts using Python programming.

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Summary

Title: Statistics, Probability & EDA for Data Science using Python

Price: $54.99

Average Rating: 5

Number of Lectures: 18

Number of Published Lectures: 18

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

• Understand the fundamental concepts of probability theory, including probability distributions, random variables, and basic probability rules.
• Learn how to summarize and describe datasets using measures such as mean, median, mode, variance, and standard deviation.
• Dive into the fundamentals terminologies of Inferential statistics.
• Explore probability distributions such as the Gaussian distribution and learn how to apply them to real-world problems.
• Gain hands-on experience using Python libraries like NumPy, Pandas, and Matplotlib to perform statistical analysis, visualize data, and interpret results.
• Understand the concepts of correlation and linear regression, and learn how to use Python to analyze relationships between variables and make predictions.
• Build probability models for events and experiments, and simulate random processes using Python to understand stochastic phenomena.
• Apply statistical and probabilistic concepts to real-world datasets and problems, developing the skills needed to tackle data analysis and decision-making tasks
• Gain expertise in Univariate, Bivariate, and Multivariate Data Analysis, equipping you with comprehensive skills to interpret and manage complex datasets.
• Discover the power of Seaborn displot, jointplot, and pairplot functions through hands-on examples, mastering univariate, bivariate, and multivariate analysis.
• Visualize data representations, including Histograms, Normal distributions, Kernel density estimations, Rug plots, Scatter plots, Contour plots and Hex plots.
• Master the Art of efficiently Visualizing, Analyzing, and Drawing insightful inferences from Heatmaps of large datasets.
• Discover the Power of Automation to create a Multiple Dynamic Visualizations effortlessly.
• Discover the art of proficiently analyzing data through Regression analysis, blending theory with practical examples to elevate your understanding & application
• Explore on a range of other Seaborn functions such as Bar, Count, Strip, Swarm, Box, and Violin plots, vital for Visualizing data distributions & Trends.

Who Should Attend

• Individuals looking to enter the field of data science who want to build a strong foundation in statistics and probability using Python.
• Those interested in working with data to extract insights and make data-driven decisions would benefit from learning statistical analysis techniques in Python.
• Students keen on understanding the statistical principles underlying machine learning algorithms and how they can be implemented in Python.
• Undergraduate and graduate students studying computer science, mathematics, statistics, or related disciplines who wish to supplement their coursework with practical skills in statistical analysis and programming.
• Working professionals in fields such as finance, marketing, healthcare, or engineering who want to enhance their analytical skills and improve their job prospects by learning Python-based statistical analysis techniques.
• Individuals involved in research or academia who need to analyze data and interpret results using statistical methods, and who prefer using Python for its flexibility and extensive libraries.
• Hobbyists or individuals from various backgrounds intrigued by data analysis and eager to explore statistical concepts using Python programming.

Target Audiences

• Individuals looking to enter the field of data science who want to build a strong foundation in statistics and probability using Python.
• Those interested in working with data to extract insights and make data-driven decisions would benefit from learning statistical analysis techniques in Python.
• Students keen on understanding the statistical principles underlying machine learning algorithms and how they can be implemented in Python.
• Undergraduate and graduate students studying computer science, mathematics, statistics, or related disciplines who wish to supplement their coursework with practical skills in statistical analysis and programming.
• Working professionals in fields such as finance, marketing, healthcare, or engineering who want to enhance their analytical skills and improve their job prospects by learning Python-based statistical analysis techniques.
• Individuals involved in research or academia who need to analyze data and interpret results using statistical methods, and who prefer using Python for its flexibility and extensive libraries.
• Hobbyists or individuals from various backgrounds intrigued by data analysis and eager to explore statistical concepts using Python programming.