Data Analysis with Python

IBM via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

17 hours 2 minutes
Sessions

On-Demand
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, Turkish, English, Spanish, Farsi, Thai, Indonesian, Kazakh, Hindi, Swedish, Korean, Greek, Chinese, Ukrainian, Japanese, Polish, Dutch, Hungarian, Bengali, Pashto, Urdu, Azerbaijani

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Overview

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Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.

Syllabus

Importing Data Sets

In this module, you will develop foundational skills in Python-based data analysis by learning how to understand and prepare datasets, utilize essential Python packages, and import and export data for analysis. You’ll gain hands-on experience using tools like Pandas, Numpy, and SQLite to begin analyzing real-world datasets, including a laptop pricing dataset. In addition, you’ll be provided with a cheat sheet that serves as a handy reference throughout this learning journey.

Data Wrangling

In this module, you will enhance your data wrangling skills using Python by learning techniques to clean, transform, and prepare data for analysis. You’ll work with real-world datasets to handle missing values, format and normalize data, bin numerical values, and convert categorical variables. Through guided labs, you’ll apply these skills to both the Laptop and Used Car Pricing datasets. You will also receive a cheat sheet to support you as a quick reference throughout the learning process.

Exploratory Data Analysis

In this module, you will build essential skills in exploratory data analysis (EDA) using Python. You will learn to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn how to group data to better visualize patterns, use the Pearson correlation method to compare two continuous numerical variables, and apply the chi-square test to assess associations between categorical variables and interpret the results. Further, you will be provided with a cheat sheet that will serve as a quick reference for commonly used EDA functions and methods.

Model Development

In this module, you will explore the fundamentals of model development in data analysis using Python. You’ll learn how to build, visualize, and evaluate different types of regression models, including simple linear, multiple linear, and polynomial regression models, along with pipelines to streamline your workflows. You’ll also interpret model performance using key metrics and visual tools such as kernel density estimation (KDE) plots. Hands-on labs will reinforce your learning with practical datasets like used car and laptop pricing. Additionally, the cheat sheet will serve as a quick reference for building and evaluating predictive models.

Model Evaluation and Refinement

In this module, you will refine your predictive modeling skills by learning how to evaluate, tune, and select models for optimal performance. You’ll explore concepts such as overfitting, underfitting, and hyperparameter tuning using grid search. You will also learn about using ridge regression to regularize and reduce standard errors to prevent overfitting a regression model. Through hands-on labs, you'll apply these techniques to real datasets to build robust, generalizable models. A cheat sheet is included to guide you in choosing the right tools and metrics for model optimization.

Final Assignment

In this final module, you will apply the complete data analysis workflow, from importing and cleaning data to building and evaluating models on real-world datasets. You’ll complete a hands-on practice project and a final project based on datasets related to insurance costs and house pricing. For the final project, you will take on the role of a Data Analyst at a real estate investment trust looking to invest in residential properties. You’ll work with a dataset containing detailed information on house prices and various property features, and your task will be to analyze the data and predict housing market values. These projects are designed to consolidate your skills and prepare you for real-world data analysis challenges. Also, you will make a submission of the final project notebook for evaluation. Your submission will be AI graded. Finally, you will demonstrate comprehension and application of key data analysis concepts through a final exam.

Taught by

Joseph Santarcangelo

Reviews

4.0 rating, based on 2 Class Central reviews

4.7 rating at Coursera based on 19629 ratings

Start your review of Data Analysis with Python

Berbelek @berbelek
125

It takes a few hours to complete. The course provides some basic lessons on working with data in Python. I think there are some better introductions available.
Dumisani Muzungu

The instructors demonstrated a clear and thorough understanding of the subject matter, making complex concepts accessible to learners of varying backgrounds. The course content was well-structured, starting with fundamental concepts before gradually…

The instructors demonstrated a clear and thorough understanding of the subject matter, making complex concepts accessible to learners of varying backgrounds. The course content was well-structured, starting with fundamental concepts before gradually progressing to more advanced techniques. Each module built upon the previous one, providing a logical and coherent learning path.

One notable strength of the course was its hands-on approach. Practical exercises and real-world examples were integrated seamlessly into the curriculum, allowing me to apply theoretical knowledge to actual data analysis scenarios. The use of industry-standard tools and software further enhanced the practicality of the course, ensuring that I gained valuable skills applicable to real-world projects.