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August 19, 2024
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This course is part of Applied Data Analytics Specialization
Instructor: Edureka
Included with
Recommended experience
Intermediate level
Prior experience with Python programming concepts and mathematics is necessary
Recommended experience
Intermediate level
Prior experience with Python programming concepts and mathematics is necessary
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.
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December 2024
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This course provides a practical introduction to statistical analysis and machine learning with Python. Learn essential machine learning concepts, methods, and algorithms with a focus on applying them to solve real-world 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. 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. 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.
12 videos3 readings4 assignments2 discussion prompts
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.
17 videos3 readings5 assignments
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.
30 videos3 readings5 assignments1 discussion prompt
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.
29 videos3 readings4 assignments1 discussion prompt
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.
42 videos2 readings4 assignments1 discussion prompt
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.
1 video1 reading1 assignment1 discussion prompt
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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.
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.
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.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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.
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. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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.