Basic Statistics and Regression in Python Practice Exam
Basic Statistics and Regression in Python Practice Exam
About Basic Statistics and Regression in Python Exam
The Basic Statistics and Regression in Python Exam evaluates foundational knowledge and practical skills in statistical analysis and regression modeling using Python. This exam is designed to test a candidate's ability to apply statistical methods and regression techniques to analyze datasets, draw insights, and build predictive models.
Key Highlights
It covers concepts such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis, emphasizing the application of these techniques in Python using libraries like NumPy, pandas, Matplotlib, Seaborn, and scikit-learn. The exam assesses both theoretical understanding and hands-on coding proficiency, preparing candidates for data-driven problem-solving in real-world scenarios.
Skills Required
- Understanding measures of central tendency (mean, median, mode).
- Calculating measures of variability (variance, standard deviation, range, IQR).
- Summarizing datasets with statistical functions and visualizations.
- Knowledge of basic probability concepts and rules.
- Working with probability distributions (normal, binomial, Poisson).
- Generating random samples and performing simulations in Python.
- Hypothesis testing (e.g., t-tests, chi-square tests, ANOVA).
- Confidence intervals and significance levels.
- Understanding Type I and Type II errors.
- Calculating and interpreting correlation coefficients.
- Simple linear regression and multiple regression.
- Evaluating model fit using metrics like R², RMSE, and MAE.
- Proficiency in Python basics, including data types, loops, and functions.
- Data manipulation with pandas and NumPy.
- Visualization using Matplotlib and Seaborn.
- Regression modeling with scikit-learn.
- Formulating hypotheses and testing them with statistical methods.
- Choosing appropriate regression techniques for different data scenarios.
- Interpreting and communicating statistical findings effectively.
Who should take the Exam?
This exam is ideal for individuals seeking to establish or enhance their statistical and regression modeling skills using Python. It caters to:
- Aspiring Data Analysts and Scientists
- Professionals from non-technical backgrounds transitioning into data analysis or machine learning roles.
- Those working in business intelligence, market research, or finance.
- Python Developers
- Researchers requiring statistical tools for data analysis and hypothesis testing.
- Anyone passionate about learning statistics and regression to solve real-world problems using Python.
Course Outline
The Basic Statistics and Regression in Python Exam covers the following topics -
Domain 1 - Environment Setup – Preparing Your Computer
- Environment Setup: Part 1
- Environment Setup: Part 2
Domain 2 - Key Components of Anaconda
- Overview of Essential Components Included in Anaconda
Domain 3 - Python Basics - Assignments
- Python Basics: Assignment
Domain 4 - Python Basics - Flow Control
- Flow Control: Part 1
- Flow Control: Part 2
Domain 5 - Python Basics - Lists and Tuples
- Understanding Lists and Tuples in Python
Domain 6 - Python Basics - Dictionaries and Functions
- Dictionaries and Functions: Part 1
- Dictionaries and Functions: Part 2
Domain 7 - Introduction to NumPy
- NumPy Basics: Part 1
- NumPy Basics: Part 2
Domain 8 - Introduction to Matplotlib
- ○ Matplotlib Basics: Part 1
- ○ Matplotlib Basics: Part 2
Domain 9 - Fundamentals of Data for Machine Learning
- Basics of Data for Machine Learning
Domain 10 - Central Tendency - Mean
- Understanding Mean Calculation
Domain 11 - Central Tendency - Median and Mode
- Median and Mode: Part 1
- Median and Mode: Part 2
Domain 12 - Variance and Standard Deviation - Manual Calculation
- Manual Calculation: Part 1
- Manual Calculation: Part 2
Domain 13 - Variance and Standard Deviation in Python
- Using Python to Calculate Variance and Standard Deviation
Domain 14 - Percentiles - Manual Calculation
- Step-by-Step Percentile Calculation
Domain 15 - Percentiles in Python
- Calculating Percentiles Using Python
Domain 16 - Uniform Distribution
- Overview of Uniform Distribution
Domain 17 - Normal Distribution
- Normal Distribution: Part 1
- Normal Distribution: Part 2
Domain 18 - Z-Score Calculation - Manual Method
- Calculating Z-Scores Manually
Domain 19 - Z-Score Calculation in Python
- Computing Z-Scores Using Python
Domain 20 - Scatter Plot for Multivariable Datasets
- Visualizing Multi-Variable Datasets Using Scatter Plots
Domain 21 - Linear Regression Overview
- Introduction to Linear Regression
- Manually Calculating Linear Regression Correlation Coefficient: Part 1
- Manually Calculating Linear Regression Correlation Coefficient: Part 2
- Manually Deriving the Linear Regression Slope Equation: Part 1
- Manually Deriving the Linear Regression Slope Equation: Part 2
- Predicting Future Values Using Linear Regression Equation
Domain 22 - Linear Regression in Python
- Introduction to Linear Regression with Python
- Implementing Linear Regression in Python: Part 1
- Implementing Linear Regression in Python: Part 2
- Understanding Strong and Weak Linear Regression Models
- Future Value Prediction with Linear Regression in Python
Domain 23 - Introduction to Polynomial Regression
- Visualizing Polynomial Regression
- Prediction and R² Value in Polynomial Regression
- Finding SD Components in Polynomial Regression
- Polynomial Regression Using Manual Methods
- Calculating SD Components for Polynomial Regression (abc Method)
- Deriving Polynomial Regression Equations and Predictions
- Understanding Polynomial Regression Coefficients
Domain 24 - Introduction to Multiple Regression
- Multiple Regression Overview
- Importing Data as CSV for Multiple Regression in Python
- Visualizing Data for Multiple Regression in Python
- Creating Regression Objects and Making Predictions Using Python
Domain 25 - Manual Approach to Multiple Regression
- Introduction and Finding Means for Multiple Regression
- Identifying Components of Multiple Regression: Part 1
- Identifying Components of Multiple Regression: Part 2
- Finding Coefficients (abc) for Multiple Regression
- Deriving Predictions and Coefficients for Multiple Regression Equations
Domain 26 - Introduction to Feature Scaling
- Overview of Feature Scaling
- Standardization Scaling in Python: Part 1
- Standardization Scaling in Python: Part 2
- Manual Calculation for Standardization Scaling
