Statistics, Probability & EDA for Data Science using Python

About Statistics, Probability & EDA for Data Science using Python

Statistics and Probability:Statistics and Probability are essential pillars in Data Science and Machine Learning, providing the foundational tools needed for data analysis and interpretation. Our course is designed to give you a deep understanding of these crucial concepts through practical, hands-on learning.

Throughout the course, we teach each concept in Statistics and Probability by working through real-world examples and implementing them using Python code. You'll explore Descriptive and Inferential Statistics, including measures of central tendencies, measures of dispersion, and various statistical methods and variables, all illustrated with practical examples. In Probability, you'll learn about random variables, Probability distributions, probability density functions, and cumulative distribution functions, each concept reinforced with Python coding exercises to solidify your understanding.

By learning Statistics and Probability through practical examples and Python code, you’ll not only grasp these critical concepts but also gain the confidence to apply them in real-world scenarios. This approach ensures that you are well-prepared to tackle advanced Data Science and Machine Learning challenges, making you proficient in these key areas and setting you up for success in the data-driven world.

Please find the brief Syllabus to the Course.

Statistics and Probability: 1. Introduction to StatisticsIntroduction to Statistics, Types of statistics, Descriptive Statistics and its attributes, Limitations of descriptive statistics

Statistics and Probability: 2. Introduction to Inferential StatisticsInferential Statistics and its attributes, Two ways to use inferential statistics, types of variables and their statistical methods.

Statistics and Probability: 3. Descriptive StatisticsMeasures of Central Tendencies and its types, Statistical Measure of Positions and its types.

Statistics and Probability: 4. Measures of DispersionMeasures of Dispersion and its types with examples and python code.

Statistics and Probability: 5. Introduction to ProbabilityDefinition of Probability, Different terms in Probability with an example, Types of Random variable with examples.

Statistics and Probability: 6. Types of Probability functionsDistribution, Probability Distribution, Types of Probability functions with Python Code

Statistics and Probability: 7. Probability density function

Probability density function and its attributes, Normal and Standard Normal Distribution, Properties of Normally distributed Curve with a Python Code, Density of a value in the Distribution.

Statistics and Probability: 8. Cumulative Distribution FunctionExplanation to Cumulative Distribution Function with a Python Code

Statistics and Probability: 9. Types and attribute of DistributionSymmetric distribution, Skewness, Kurtosis with a Python Code.

Statistics and Probability: 10. Box-plot with Whiskers and Voilin PlotsBox-plot, Voilin Plot, Plotting a Boxplot and Voilin plot using a Python code, Calculation of Quantiles and whisker values, Dropping the outliers in our data.

Statistics and Probability: 11. Kernel Density EstimationWhat is a Kernel, Properties of a Kernel, Kernel Density Estimation Plot and its properties, KDE visualizations, Univariate Analysis using KDE plot, Bivariate Analysis using contour plot.

Statistics and Probability: 12. CovarianceCovariance its attributes and examples, Properties of Covariance Value, Comparison of Covariance between two variables, Creating a Covariance Matrix, Negative Covariance and Zero Covariance.

Statistics and Probability: 13. CorrelationCorrelation and its properties, Analysis of Correlation between two variables, Assumptions before we calculate the Correlation, Correlation and its visualizations (Heatmap), 2. Coefficient of Determination, Causation and its relationship with Correlation with examples.

Statistics and Probability: 14. RegressionRegression and its definition, Types of Variable, Use of Regression, Difference between Regression and Correlation, Simple Linear Regression, Calculating the Least Squares Regression Line, Standard error of Estimate and its Assumptions, Linear Regression using a Python code.

Exploratory Data AnalysisExploratory Data Analysis (EDA) is a crucial step for anyone pursuing a career in Data Science and Machine Learning. It allows you to uncover patterns, identify anomalies, and test hypotheses within your datasets. Our course is specifically designed to equip you with the practical EDA skills necessary for success as a Machine Learning engineer or data scientist.

In this course, you’ll gain hands-on experience with EDA by working through various datasets, which is essential for developing real-world expertise. You'll begin with Univariate, Bivariate, and Multivariate Analysis, where you'll learn to set Seaborn styles and create visualizations like scatter plots, kernel density estimation plots, and hex plots. We then guide you through the EDA of the IMD Rainfall Dataset, using heatmaps to summarize data and automate visualizations. You'll also perform EDA on a Real Estate Dataset, conducting correlation matrix analysis, regression analysis, and examining categorical variables through regression plots. Finally, you’ll analyze an IPL player performance dataset, using bar plots, count plots, strip plots, swarm plots, box plots, and violin plots to derive insights. This hands-on approach ensures that you not only understand EDA theoretically but also know how to apply it to real-world datasets, a vital skill for data professionals.

Mastering Exploratory Data Analysis is essential for becoming a proficient Machine Learning engineer or data scientist. EDA is the foundation of deeper statistical analysis and machine learning model development, helping you make sense of raw data and identify key trends and outliers. The skills you gain from this course will empower you to transform raw data into actionable insights, a core competency in Data Science and Machine Learning. By the end of our course, you'll be proficient in EDA techniques, enabling you to approach any dataset with confidence and drive data-driven decision-making in your projects.Exploratory Data Analysis

1. Exploratory Data Analysis using Classroom DatasetUnivariate, Bivariate and Multivariate Analysis of Classroom dataset, Setting Seaborn style, Univariate Analysis, Bivariate Analysis, Scatter Plot, Kernel Density Estimation Plot, Hex Plot, Regression Plot, Multivariate Analysis.

2. Exploratory Data Analysis using IMD Rainfall DatasetAnalysis of Rainfall Dataset using Heatmap, leveraging Automation to generate multiple visualizations, Summarizing inferences from a Heatmap.

3. Exploratory Data Analysis of Real Estate DatasetCorrelation Matrix of the Real Estate Dataset, Regression Analysis of Real Estate Dataset, Categorical Analysis of Regression plots.

4. Exploratory Data Analysis using IPL player performance DatasetDifferent Analysis of dataset using Bar plot, Count plot, Strip plot, Swarm plot, Boxplot, Violin plot.

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Happy Learning!!! :)