Statistical knowledge is key to evaluating, interpreting, and reporting findings from your data. With this Course, you will learn to confidently evaluate statistical models, simulate data, and draw conclusions from a wide variety of data sets. You will build the Foundation you need to think statistically and to speak the language of your data. Then you would practise how to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing. You will also explore the concepts and applications of linear models with python and build models to describe, predict, and extract. At the end you will wrap up by solving increasingly complex problems using simulations to generate and analyze data.

Description

• Begineer Python developers curious about data science.

Course content

• Graphical exploratory data analysis
  • Introduction to Exploratory Data Analysis
  • Plotting a histogram
  • Plot all of your data: Bee swarm plots
  • Plot all of your data: ECDFs
  • Onward toward the whole story!
• Quantitative exploratory data analysis
  • Introduction to summary statistics: The sample mean and median
  • Percentiles, outliers, and box plots
  • Variance and standard deviation
  • Covariance and the Pearson correlation coefficient
• Thinking probabilistically-- Discrete variables
  • Probabilistic logic and statistical inference
  • Random number generators and hacker statistics
  • Probability distributions and stories: The Binomial distribution
  • Poisson processes and the Poisson distribution
• Thinking probabilistically-- Continuous variables
  • Probability density functions
  • Introduction to the Normal distribution
  • The Normal distribution: Properties and warnings
  • The Exponential distribution
• Parameter estimation by optimization
  • Optimal parameters
  • Linear regression by least squares
  • The importance of EDA: Anscombe's quartet
• Bootstrap confidence intervals
  • Generating bootstrap replicates
  • Bootstrap confidence intervals
  • Pairs bootstrap
• Introduction to hypothesis testing
  • Formulating and simulating a hypothesis
  • Test statistics and p-values
  • Bootstrap hypothesis tests
• Hypothesis test examples
  • A/B testing
  • Test of correlation
• Exploring Linear Trends
  • Introduction to Modeling Data
  • Visualizing Linear Relationships
  • Quantifying Linear Relationships
• Building Linear Models
  • What makes a model linear
  • Interpreting Slope and Intercept
  • Model Optimization
  • Least-Squares Optimization
• Making Model Predictions
  • Modeling Real Data
  • The Limits of Prediction
  • Goodness-of-Fit
  • Standard Error
• Estimating Model Parameters
  • Inferential Statistics Concepts
  • Model Estimation and Likelihood
  • Model Uncertainty and Sample Distributions
  • Model Errors and Randomness
• Basics of randomness & simulation
  • Introduction to random variables
  • Simulation basics
  • Using simulation for decision-making
• Probability & data generation process
  • Probability basics
  • More probability concepts
  • Data generating process
  • eCommerce Ad Simulation
• Resampling methods
  • Introduction to resampling methods
  • Bootstrapping
  • Jackknife resampling
  • Permutation testing
• Advanced Applications of Simulation
  • Simulation for Business Planning
  • Monte Carlo Integration
  • Simulation for Power Analysis
  • Applications in Finance
  • Wrap Up