Thanks to theidioms.com

Analyzing the sinking of the Titanic – Data Analysis with Python (Course V)

Analyzing the sinking of the Titanic – Data Analysis with Python (Course V)

Welcome to Course V!

Welcome to this course on Data Analysis with Python.

In this course, you will be performing hands-on Exploratory Data Analysis (EDA) on the dataset of the infamous Kaggle competition, ‘Titanic: Machine Learning from Disaster’.

During this course, you will learn how to perform general as well as problem-specific analyses to find insights from the given dataset. This will be a knowledgeable course for you and will involve the use of Python libraries such as Pandas, Matplotlib and Seaborn.

Objectives of the course

The learning objectives of the course are set out as follows:

  • Learn how to download a dataset from Kaggle
  • Learn how to read in a CSV file format dataset in Python
  • Learn how to perform Exploratory Data Analysis (EDA)
  • Learn how to visualize data insights

You can expect to have all of these objectives met by the time you reach the end of this course

Pre-requisites for the course

If this is your first time working on Python, it may be hard for you to effectively grasp all the concepts. Therefore, the following pre-requisites are required for you to get the best out of the course:

  • Solid understanding of the Python programming language
  • Familiarity with Pandas, Matplotlib and Seaborn
  • Interest in performing data analysis with Python

If you do not satisfy the above pre-requisites, don’t worry! You can always come back later to this course once you are ready.

Best way to work through the course

The course is not long but requires a good amount of attention from your end.

Before moving to the next lecture, we suggest you to set up your coding environment and open up your Jupyter Notebook. If you are a more advanced user of Python and have your own preferences, please feel free to choose an IDE that you prefer. However, all of the coding examples will be written for execution on Jupyter Notebook cells.

Also, if you come across any problem, please check to see if your code matches exactly with the course or not. If you still are facing errors or have some doubt, please provide your question through the comment section of the specific chapter you are stuck on.

We also recommend you join our community and get connected to our vibrant network of data science aspirants. Once you are in the community, you can share your learnings, form a study group or even get help building a project around Data Analysis.

All good? Let’s get started.

Leave your thought here

Your email address will not be published. Required fields are marked *

Close Bitnami banner
Bitnami