Predictive Modeling with Python
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Predictive Modeling with Python
This course is part of Applied Data Analytics Specialization
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What you'll learn
Manage and preprocess data for statistical analysis and modeling.
Conduct hypothesis testing using advanced statistical techniques.
Build exploratory data analysis (EDA) models to uncover insights.
Build and evaluate models to solve real-world data challenges.
Skills you'll gain
Details to know
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There are 6 modules in this course
This Predictive Modeling with Python course provides a practical introduction to statistical analysis and machine learning with Python. You will learn essential machine learning concepts, methods, and algorithms with a strong emphasis on applying them to solve real-world business and data problems.
By the end of the course, you will: - Understand different data types used in statistical analysis. - Learn techniques to manage inconsistent data effectively. - Perform hypothesis testing using parametric and non-parametric tests. - Develop exploratory data analysis (EDA) models using statistical and machine learning methods. - Enhance machine learning models through evaluation and optimization techniques. This course is designed for individuals with a foundational knowledge of Python programming and basic statistical concepts. This course is ideal for aspiring data analysts, data scientists, business executives, machine learning engineers, and anyone passionate about data-driven decision-making Throughout the program, you will gain hands-on experience in statistical and predictive modeling and apply your skills to real-world scenarios. Enroll in "Predictive Modeling with Python" today and take your expertise to the next level!
In the first module of this course, learners will explore various data types and utilize different measures of central tendency and measures of dispersion to address data inconsistencies.
What's included
12 videos3 readings4 assignments2 discussion prompts
12 videos•Total 40 minutes
- Course Introduction•3 minutes
- Data Types•3 minutes
- Categorical Data•2 minutes
- Nominal Data•2 minutes
- Demonstration of Data Types - Dataset Description•4 minutes
- Demonstration of Types•5 minutes
- What is Statistics?•4 minutes
- Measures of Central Tendency•3 minutes
- Demonstration of Central Tendency•4 minutes
- Measures of Dispersion•3 minutes
- Demonstration: Measures of Dispersion•4 minutes
- Summary of Data and Information•2 minutes
3 readings•Total 30 minutes
- Welcome to Predictive Modeling with Python•10 minutes
- Applications of Central Tendency in Statistics •10 minutes
- Libraries for Statistical Analysis•10 minutes
4 assignments•Total 29 minutes
- Practice Quiz : Data Types in Statistics•3 minutes
- Practice Quiz : Statistical Analysis•3 minutes
- Practice Quiz : Statistical Dispersion•3 minutes
- Knowledge Check : Data and Information•20 minutes
2 discussion prompts•Total 20 minutes
- Introduce Yourself•10 minutes
- Why are measures of dispersion important in understanding datasets?•10 minutes
In this module, learners will learn to manage data using probability distribution functions. Learners will start by applying the Bernoulli distribution to model categorical data, explore the Poisson distribution for forecasting, and utilize the Exponential and Normal distributions for regression modeling.
What's included
17 videos3 readings5 assignments
17 videos•Total 69 minutes
- Probability Density and Mass Function•4 minutes
- Cumulative Distribution Function•4 minutes
- Discrete Probability•2 minutes
- Negative Bernoulli Distribution•2 minutes
- Demonstration of Negative Bernoulli Distribution•7 minutes
- Geometric Distribution•2 minutes
- Demonstration of Geometric Distribution•7 minutes
- Poisson Distribution•5 minutes
- Example of Poisson Distribution•2 minutes
- Demonstration of Poisson Distribution•4 minutes
- Continuous Probability Distribution•5 minutes
- Uniform Distribution•4 minutes
- Exponential Distribution•3 minutes
- Demonstration of Exponential Distribution•3 minutes
- Normal Distribution•6 minutes
- Demonstration of Normal Distribution•6 minutes
- Summary of Probability Distribution Functions•2 minutes
3 readings•Total 30 minutes
- Example of PDF and PMF•10 minutes
- Importance of Negative Bernoulli and Geometric Distributions•10 minutes
- Continuous Probability Distribution and Uniform Distribution: Mathematical Example•10 minutes
5 assignments•Total 32 minutes
- Practice Quiz : General Probability Distribution•3 minutes
- Practice Quiz : Negative Bernoulli and Geometric Distribution•3 minutes
- Practice Quiz : Poisson and Uniform Distribution•3 minutes
- Practice Quiz : Exponential and Normal Distribution•3 minutes
- Knowledge Check : Probability Distribution Function•20 minutes
In the third module of this course, Learners will learn to apply the Central Limit Theorem in scenarios where data may be improperly distributed. Identify and analyze sample data, using both parametric and non-parametric methods to handle various test cases for hypothesis testing and decision-making.
What's included
30 videos3 readings5 assignments1 discussion prompt
30 videos•Total 138 minutes
- Central Limit Theorem•5 minutes
- Demonstration of Central Limit Theorem•6 minutes
- Demontration: Conclusion of Central Limit Theorem•3 minutes
- Population and Sample Space•3 minutes
- Parameter and Statistics•5 minutes
- Forms of Inferential Statistics•1 minute
- Point and Interval Estimation•4 minutes
- Maximum Likelihood•4 minutes
- Demonstration Exploring Data •5 minutes
- Demonstration: Drawing Sample Data •4 minutes
- Hypothesis Testing•5 minutes
- Hypothesis Testing Example•5 minutes
- Statistical Test Implementation•6 minutes
- One Tailed and Two Tailed Test•5 minutes
- Z - Test and T - Test•6 minutes
- Power Analysis•6 minutes
- Demonstration of Confidence Interval and Margin of Error •3 minutes
- Demonstration of Hypothesis Testing•4 minutes
- Demonstrating Power Analysis•8 minutes
- Chi - Square Test•4 minutes
- Pearson and Spearman Correlation•6 minutes
- Chi square Test Demonstration•6 minutes
- Pearson Correlation Demonstration•6 minutes
- Spearman Correlation Demonstration•7 minutes
- ANOVA•6 minutes
- Example for One Way ANOVA - Part 1•2 minutes
- Example for Two Way ANOVA - Part 2•2 minutes
- Demonstration for One way ANOVA•5 minutes
- Demonstration for Two way ANOVA•3 minutes
- Summary for Inferential Statistics•2 minutes
3 readings•Total 30 minutes
- Central Limit Theorem (CLT): Mathematical Example•10 minutes
- Statistical Inference Real World Applications•10 minutes
- Shapiro-Wilk Test •10 minutes
5 assignments•Total 32 minutes
- Practice Quiz : Introduction to Central Limit Theorem•3 minutes
- Practice Quiz : Statistical Inference Methods•3 minutes
- Practice Quiz : Statistical Hypothesis and Significance Testing•3 minutes
- Practice Quiz : Parametric and Non Parametric Tests•3 minutes
- Knowledge Check : Inferential Statistics•20 minutes
1 discussion prompt•Total 10 minutes
- Have any of the you used the above methods in their professional careers•10 minutes
In the fourth module, learners will explore implementing Exploratory Data Analysis (EDA) on large, complex datasets by conducting both univariate and multivariate analysis. They will also learn how to clean and process data, as well as perform feature engineering to prepare the data for analysis.
What's included
29 videos3 readings4 assignments1 discussion prompt
29 videos•Total 138 minutes
- What is EDA?•5 minutes
- Univariate Analysis: Data and Outliers•4 minutes
- Univariate Analysis: Kurtosis and Chart Types•4 minutes
- Multivariate Analysis•4 minutes
- Multivariate Analysis: Covariance, Correlation, and Association•4 minutes
- Multivariate Analysis: Correlation Matrix•4 minutes
- Multivariate Analysis: Scatter Plots and HeatMaps•6 minutes
- Identifying and Handling Missing Data•7 minutes
- Sampling Methods•4 minutes
- Mean Median Mode Imputation•5 minutes
- Data Normalization and Standardization •3 minutes
- Methods to Transform Data•6 minutes
- Univariate, Bivariate and Multivariate Imputation•4 minutes
- Demonstration I: Understanding the Data•4 minutes
- Demonstration II: Visualizing and Handling Missing Data•6 minutes
- Demonstration III: Scaling and Imputation of Data•4 minutes
- Demonstration IV: Train Test Split•7 minutes
- Demonstration V: Stratified K-Fold Cross-Validation•4 minutes
- Demonstration VI: Sampling and Evaluation•4 minutes
- Introduction to Feature Engineering •5 minutes
- Feature Transformation•6 minutes
- Encoding: One Hot Encoding•6 minutes
- Encoding: Label Encoding•2 minutes
- Autofeat Library•3 minutes
- Demonstration I: Setting up the Scenario•4 minutes
- Demonstration II: Data Transformation•7 minutes
- Demonstration III: Encoding•8 minutes
- Demonstration IV: Autofeat•7 minutes
- Summary for Introduction to EDA•2 minutes
3 readings•Total 30 minutes
- Understanding Exploratory Data Analysis (EDA)•10 minutes
- Best Practices in Data Pre-processing •10 minutes
- Overview of Autofeat library•10 minutes
4 assignments•Total 29 minutes
- Practice Quiz : Understanding EDA•3 minutes
- Practice Quiz : Data Cleaning and Pre-processing•3 minutes
- Practice Quiz : Feature Engineering and Data Transformation•3 minutes
- Knowledge Check : Introduction to (Exploratory Data Analysis) EDA•20 minutes
1 discussion prompt•Total 10 minutes
- Explain the importance of Data Pre-processing•10 minutes
In this module, learners will learn how to use machine learning models to extract insights from data. They will apply regression and classification algorithms and then optimize the results produced by these models.
What's included
42 videos2 readings4 assignments1 discussion prompt
42 videos•Total 197 minutes
- Introduction to Linear Regression•6 minutes
- Assumptions in Linear Regression•4 minutes
- Working of Linear Regression•4 minutes
- Cost function in Linear Regression•2 minutes
- Gradient Descent in Linear Regression•3 minutes
- Demonstration of Linear Regression: Building Model•3 minutes
- Demonstration of Linear Regression: Testing the Model•6 minutes
- Logistic Regression•7 minutes
- Cost function in Logistic Regression•4 minutes
- Gradient Descent in Logistic Regression•4 minutes
- Importance of Sigmoid Function•1 minute
- Demonstration: Logistic Regression - Data Processing•6 minutes
- Demonstration: Logistic Regression - Model Execution•7 minutes
- Classification in Machine Learning•5 minutes
- Decision Tree Part 1: What is Decision Tree?•5 minutes
- Decision Tree Part 2: What is Random Forest?•5 minutes
- Basic Terminologies of Decision Tree•4 minutes
- Working of Decision Tree•6 minutes
- Building a Decision Tree•6 minutes
- Advantages and Disadvantages of Decision Tree•6 minutes
- Demonstration Part 1: Explaining the Scenario•5 minutes
- Demonstration Part 2: Exploring the Data•2 minutes
- Demonstration Part 3: Profiling Report•7 minutes
- Demonstration Part 4: Attrition and Univariate Graph•7 minutes
- Demonstration Part 5: Data Pre - processing•4 minutes
- Demonstration Part 6: Building Decision Tree•4 minutes
- Demonstration Part 7:Tree Classifier•5 minutes
- Demonstration Part 8: Pros and Cons•5 minutes
- Random Forest Example Part 1: Ensemble Learning and Bagging •4 minutes
- Random Forest Example Part 2: Working of Random Forest•4 minutes
- Performance Metrics for Regression - MAE and MAPE •4 minutes
- Performance Metrics for Regression - MSE, RMSE, RMSLE and R-square•4 minutes
- Confusion Matrix•5 minutes
- ROC and AUC•5 minutes
- Hyperparameter Tuning and Optimization•5 minutes
- Model Selection•7 minutes
- Model Evaluation •6 minutes
- Bias Variance Trade-off •4 minutes
- Cross Validation•4 minutes
- Demonstration I: Grid Search - Analyze the Data•4 minutes
- Demonstration II: Grid Search - Building Model•4 minutes
- Summary of Predictive Models•2 minutes
2 readings•Total 20 minutes
- Regularization in Regression•10 minutes
- Optuna: A Powerful Tool for Hyperparameter Optimization•10 minutes
4 assignments•Total 29 minutes
- Practice Quiz : Regression•3 minutes
- Practice Quiz : Classification: Decision Tree and Random Forest•3 minutes
- Practice Quiz : Model Evaluation and Optimization•3 minutes
- Knowledge Check : Predictive Modeling and Analysis•20 minutes
1 discussion prompt•Total 10 minutes
- Which among the following evaluation metrics are easy to use and fairly accurate?•10 minutes
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Probability, Statistical Modeling, and Machine Learning.
What's included
1 video1 reading1 assignment1 discussion prompt
1 video•Total 2 minutes
- Course Summary of Predictive Modeling with Python•2 minutes
1 reading•Total 30 minutes
- Black Friday - Sales Analysis•30 minutes
1 assignment•Total 20 minutes
- Knowledge Check : Predictive Modeling with Python•20 minutes
1 discussion prompt•Total 10 minutes
- Describe Your Learning Journey•10 minutes
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Frequently asked questions
This course is designed with emphasizes on predictive modeling and statistical analysis, providing learners with the skills and methods to examine data, discern trends, and make well-informed forecasts about future results.
Predictive Modeling with Python is tailored for professionals and enthusiasts seeking to deepen their expertise in predictive modeling and statistical analysis, including data analysts, aspiring data scientists, business leaders, and individuals dedicated to data-driven decision-making.
The course spans approximately 6 weeks, allowing flexibility based on the learner's pace, with an estimated weekly commitment of 2-3 hours for lectures, practical projects, and assessments.
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