Which platform is used to perform coding operation?

The course primarily utilizes Google Colab for coding exercises. However, learners have the option to use integrated development environments (IDEs) such as Jupyter Notebook, PyCharm, Spyder, or VS Code for more advanced coding projects, if preferred.

What are the pre - requisites for this course?

This course requires a foundational understanding of mathematics, particularly algebra and basic statistics. Familiarity with data analysis concepts, including data interpretation and manipulation, is also recommended. Proficiency in programming languages, especially Python, is essential for successful completion of the course.

Can this course help me in a data science career?

Yes, predictive modeling is a core skill for data science, business analytics, and AI-driven decision-making roles.

What career opportunities can predictive modeling skills open up?

Predictive modeling skills prepare you for careers as Data Scientists, Machine Learning Engineers, Business Analysts, and Risk Modelers.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate that you can showcase to employers and add to your LinkedIn profile.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Predictive Modeling with Python

This course is part of Applied Data Analytics Specialization

Instructor: Edureka

Included with

Learn more

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

23 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Applied Data Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

166 Courses 150,377 learners

Offered by

Edureka

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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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

Predictive Modeling with Python

Predictive Modeling with Python

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

There are 6 modules in this course

Data and Information

What's included

Probability Distribution Function

What's included

Inferential Statistics

What's included

Introduction to (Exploratory Data Analysis) EDA

What's included

Predictive Modeling and Analysis

What's included

Course Wrap-Up and Assessment

What's included

Earn a career certificate

Instructor

Offered by

Explore more from Probability and Statistics

Predictive Modeling with Python: Apply & Evaluate

Intro to Predictive Analytics Using Python

Applied Machine Learning with Python

AI & Predictive Analytics with Python

Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

What is this course about?

Who Should Take This Course?

What is the duration of this course?

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