Statistics, Probability & EDA for Data Science using Python
What you'll 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.
Requirements
- Fundamentals of Mathematics and Python Programming
Description
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 Statistics
Introduction to Statistics, Types of statistics, Descriptive Statistics and its attributes, Limitations of descriptive statistics
Statistics and Probability: 2. Introduction to Inferential Statistics
Inferential Statistics and its attributes, Two ways to use inferential statistics, types of variables and their statistical methods.
Statistics and Probability: 3. Descriptive Statistics
Measures of Central Tendencies and its types, Statistical Measure of Positions and its types.
Statistics and Probability: 4. Measures of Dispersion
Measures of Dispersion and its types with examples and python code.
Statistics and Probability: 5. Introduction to Probability
Definition of Probability, Different terms in Probability with an example, Types of Random variable with examples.
Statistics and Probability: 6. Types of Probability functions
Distribution, 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 Function
Explanation to Cumulative Distribution Function with a Python Code
Statistics and Probability: 9. Types and attribute of Distribution
Symmetric distribution, Skewness, Kurtosis with a Python Code.
Statistics and Probability: 10. Box-plot with Whiskers and Voilin Plots
Box-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 Estimation
What 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. Covariance
Covariance 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. Correlation
Correlation 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. Regression
Regression 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 Analysis
Exploratory 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 Dataset
Univariate, 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 Dataset
Analysis of Rainfall Dataset using Heatmap, leveraging Automation to generate multiple visualizations, Summarizing inferences from a Heatmap.
3. Exploratory Data Analysis of Real Estate Dataset
Correlation 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 Dataset
Different Analysis of dataset using Bar plot, Count plot, Strip plot, Swarm plot, Boxplot, Violin plot.
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Who this course is for:
- 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.
Instructor
- 4.3 Instructor Rating
- 55 Reviews
- 1,695 Students
- 4 Courses
Welcome to the world of data mastery at Udemy! I'm thrilled to guide you through your journey in unlocking the power of data analytics and machine learning. With a passion for problem-solving and a commitment to continuous learning, I bring a unique blend of expertise in Excel, SAS programming, Python, and machine learning algorithms to the table.
Throughout my journey in the realm of Data Science and Machine Learning, I've come to understand the immense breadth of knowledge awaiting students. Recognizing the value of efficient learning, I've meticulously designed my courses to optimize learning time. By curating content thoughtfully, I ensure that students can absorb more in less time, empowering them to accelerate their learning journey effectively.
I have adorned the roles of being a Data Analyst, SAS Analyst, SAS Programmer, Machine Learning Engineer and as a Researcher before creating these courses for the benefit of the Data Science community.
I'm a Certified Base and Advanced SAS Programmer in the field of Data Science that adds to my credibility.
Each video in our courses has been meticulously crafted with the aim of making a positive contribution to the data science community and benefiting students at large.
Through my research being a Data Scientist, I have demonstrated an acumen to make successful real-time predictions in numerous fields. Do check out my blog section on my website to gauge my predictive ability.