Data Analytics with Python

 Professor Ernesto Lee from Broward College and Ernesto.Net introduces timeless Data Analytics techniques to introduce you to the world of Data Analysis using Python. You will be introduced to the "essence" of each topic followed by real Python implementations and valid use cases. At the end of each chapter, you will be able to apply modern Analytical techniques which make this book a great option for Corporate IT Training or Academic classes. At the end of each chapter, you will find assessments and additional programming assignments. You will learn the math behind scientific computing using Pandas, NumPy, matplotlib, seaboarn, and machine learning with scikit-learn. You will learn:- Understand the core concepts of statistics and how they apply to data analysis using Python- Dive deep into Probability Distributions and how they are related to your analysis- Obtain objective truths through Inferential Statistics and Analysis of Variance- Go in-depth with pandas for reading, writing, and processing data- Use tools and techniques for Exploratory Data Analysis (EDA)- Examine how to model to make predictions- Why gradient descent is the gold standard for optimization algorithms used in Data Analysis and Data Science- Time Series AnalysisHands on Labs- Setup the Python ecosystem for Data Analytics- Compute Central Tendencies and Dispersions (Use blood pressure data to learn about the "big picture" of the dataset).- Calculate binomial distributions (What is the probability that 6 randomly selected patients will recover from a disease?)- Apply Poisson distributions (What is the probability that a bird in a nature park will sing during a set time frame?)- Code and understand distributions with z values (What is the probability of an exam score falling between a defined range - 70 and 80%?)- Computer confidence intervals for a population mean using t-distributions (Use survey data to make inferences).- Understand ANOVA (Analyze the effects of independent variables with respect to automotive fuel efficiency).- Univariate, Bivariate, and Multivariate Analysis (Analyze energy distribution in real estate sites).- Use Regression Analysis (Use world bank data to predict trends).- Handle categorical predictors, regularization, and polynomial regression (Use advertising data to predict expenditures and sales).- Time Series Analysis (Forecasting sales based on seasonality).This book is for you if:You have a basic Python background and you are interested in becoming a Data Analyst.You are not intimidated by looking at formulas. Data Analysis is heavily based on statistics so the equations are provided and explained. You don't need to be a PhD in mathematics to gain value out of the book but you will learn the math behind the techniques. (Even if you don't care about the math - skip it - you can still learn from the programming applications).