Data Analysis with Python

Go to Course: https://www.coursera.org/learn/data-analysis-with-python

Introduction

## Course Review: Data Analysis with Python In today’s data-driven world, the ability to analyze and interpret data has become a crucial skill across industries. Coursera's **Data Analysis with Python** course is an excellent choice for both beginners eager to dive into data analysis and for professionals looking to enhance their analytical skills using one of the most popular programming languages, Python. ### Course Overview This comprehensive course aims to equip you with essential data analysis skills, starting from the fundamentals and progressing to more complex tasks like building and evaluating data models. It addresses a wide range of topics, including: - Collecting and importing data - Cleaning and preparing data - Data frame manipulation - Summarizing data - Building machine learning regression models - Model refinement - Creating data pipelines ### What You Will Learn #### Importing Data Sets The journey starts with teaching you how to understand various data types and how to use Python libraries such as Pandas and NumPy to import data from different sources. By gaining an understanding of importing data, you set a solid foundation for further exploration and analysis. #### Data Wrangling In the Data Wrangling module, fundamental techniques are introduced, including handling missing values, formatting and normalizing data, and converting categorical data to a numerical format. This phase is crucial, as clean data is the backbone of any reliable analysis. #### Exploratory Data Analysis (EDA) Exploratory Data Analysis is highlighted as a significant aspect of the data analysis process. This module teaches you how to perform descriptive statistical computations to better understand data distributions and visualize the data effectively. By using methods like the Pearson correlation and Chi-square tests, you gain insights into relationships between variables. #### Model Development The course advances into model development, introducing you to linear regression models, both simple and multiple. You will learn how to evaluate models visually and interpret key metrics like R-squared and Mean Square Error. This knowledge is invaluable for decision-making processes within data sets. #### Model Evaluation and Refinement Knowing how to evaluate a model’s performance is critical, and this module equips you with the right tools. You will explore techniques like Ridge Regression to combat overfitting and learn how to use Grid Search for hyperparameter tuning, refining your models to achieve better accuracy. #### Final Assignment The course culminates in a practical assignment that simulates a real-world scenario. You will act as a Data Analyst in a real estate investment trust, analyzing a dataset to predict house prices. This hands-on project not only reinforces what you've learned but also provides a tangible outcome that can be showcased in your portfolio. ### Recommendations I highly recommend **Data Analysis with Python** for anyone interested in entering the field of data science or seeking to sharpen their analytics skills. Here are a few reasons why: 1. **Structured Learning Path**: The course is structured in a way that sequentially builds your knowledge and skills, making it suitable for beginner and intermediate learners alike. 2. **Hands-On Projects**: The practical assignments reinforce learning and allow you to apply your skills in real-world scenarios, making the experience more engaging and beneficial. 3. **Expert Instruction**: Taught by industry professionals, you can expect high-quality content and insights that are relevant and up-to-date. 4. **Flexible Learning Schedule**: With Coursera, you can learn at your own pace, fitting your education into a busy schedule. 5. **Networking Opportunities**: Engaging with peers and contributing to peer-graded assignments can lead to valuable connections within the data science community. In conclusion, if you're ready to embrace the world of data analysis and want a well-designed course that provides valuable knowledge and skills, **Data Analysis with Python** on Coursera is an excellent choice. It’s not just about learning Python; it's about understanding how to leverage it for meaningful insights in data. Start your data analysis journey today!

Syllabus

Importing Data Sets

In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set.

In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.

In this module, you will learn what is meant by exploratory data analysis, and you will learn how 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 about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.

In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.

In this module, you will learn about the importance of model evaluation and discuss different data model refinement techniques. You will learn about model selection and how to identify overfitting and underfitting in a predictive model. You will also learn about using Ridge Regression to regularize and reduce standard errors to prevent overfitting a regression model and how to use the Grid Search method to tune the hyperparameters of an estimator.

Congratulations! You have now completed all the modules for this course. In this last module, you will complete the final assignment that will be graded by your peers. In this final assignment, you will assume the role of a Data Analyst working at a real estate investment trust organization who wants to start investing in residential real estate. You will be given a dataset containing detailed information about house prices in the region based on a number of property features, and it will be your job to analyze and predict the market price of houses given that information.