Basecamp

Everything you need to know about Data Visualisation with Python. Data visualization is a discipline of trying to understand data by placing it in a visual context so that patterns, trends, and correlations that might not otherwise be detected can be exposed. A picture is worth a thousand words. This course is here for you if you want to learn how to do data visualization in Python and are looking for a good selection of resources to help you with that. The course consists of the following topics: - Introduction to Matplotlib. - Introduction to Seaborn. - Introduction to Plotly. - 3D charts with Matplotlib. - Maps with Geopandas. - Interactive charts with IPython widgets. Prerequisites: The prerequisite of this course is the basic knowledge of Python, including the syntax and its data types. The exercises are shared through Github, so basic knowledge of GIT is also required. By the end of this course you will be able to: - Create many different types of charts with Matplotlib and Seaborn. - Create interactive charts with Plotly. - Visualize geospatial data with GeoPandas. - Understand IPython widgets. Mentor support: You can select one of three available pricing plans. Each will give you access to all course materials including the quizzes and challenges. Additionally, you can opt for mentor support through Slack and mentor calls. Course duration: ca. 15 hours

Everything you need to know about Data Wrangling with Python. Data wrangling is an important part of the data science process and it is essential to master it before moving on to machine learning. This course is here for you if you want to learn how to do data wrangling in Python and are looking for a good selection of resources to help you with that. This course is NOT for you if you don't know the basics of Python yet or if you want to jump directly to machine learning modeling (check out our Intro to Python course or stay tuned for Introduction to Machine Learning). The course consists of the following topics: - Introduction to NumPy. - Introduction to Pandas and its data types. - Processing of different data formats like XML, JSON, CSV, or text. - Introduction to APIs. Prerequisites: The prerequisite of this course is the basic knowledge of Python, including the syntax and its data types. The exercises are shared through GitHub, so basic knowledge of GIT is also required. By the end of the course, you will be able to: - Process and convert raw data into formats needed for analysis. - Define the basic Pandas and NumPy functions and data types. - Apply different data wrangling techniques to prepare JSON and XML files. - Understand the concept of APIs and access data through different ones - Solve various data wrangling and reporting challenges using Pandas functions and data types. Mentor support: You can select one of three available pricing plans. Each will give you access to all course materials including the quizzes and challenges. Additionally, you can opt for mentor support through Slack and weekly mentor calls. Course duration: ca. 50 hours

Learn how to use and leverage Docker for your data science projects. Docker gained a lot of popularity in the last couple of years and nowadays, it can be very useful also for Data Scientists. By the end of this course, you will be able to: - Install Docker on your machine. - Understand how Docker can be useful for data scientists. - Download a Docker image of your choice and set up a dev environment. - Create your own simple Docker files and images. Prerequisites: For this micro-course, you will need basic knowledge of the Command line. If you need to refresh it, you can take our course Environment Setup. Mentor support: You can select one of three available pricing plans. Each will give you access to all course materials. Additionally, you can opt for mentor support through Slack and a mentor call. Course duration: ca. 6 hours

This course is an introductory course for everybody who wants to learn data science. It will guide you through everything you need to download and set up before you start learning. Moreover, you will learn some basic tools and commands that you will find helpful in the future stages of your learning or career. By the end of this course, you will be able to: - Set up your own data science environment on your machine. - Understand the value of virtual environments in data science. - Use the command line for data science purposes. - Apply Bash commands to do basic operations on datasets. Prerequisites: None. If you are looking for ways how to start your data science education, this is the right course to start. Mentor support: You can select one of three available pricing plans. Each will give you access to all course materials. Additionally, you can opt for mentor support through Slack and a mentor call. Course duration: ca 10hours

Start your Data Science career by learning all the coding you will need. In the last years, Python has become the most popular programming language among data scientists and it will most probably bring you the best opportunities in your data science career. This course will guide you through everything important you need to know about coding to become a great data scientist. We will start with basic Python syntax and variables and then move to more advanced stuff essential for any data scientist. By the end of this course you will be able to: - Understand Python syntax and basic data types and write Python code. - Tell the difference between complex data types like lists, dictionaries, or tuples and use them for your benefit. - Apply data types from module collections to improve the efficiency of your code. - Apply various datetime methods to handle time and date. - Understand common errors that can occur during the execution. Prerequisites: For this course, you will need to have Python and JupyterLab installed on your computer. No prior Python knowledge is required. If you are not sure how to set up those, you can learn it from our micro-course: Environment Setup. Mentor support: You can select one of three available pricing plans. Each will give you access to all course materials including the quizzes and challenges. Additionally, you can opt for mentor support through Slack and mentor calls. Course duration: ca 20 hours

Everything you need to know about Probability & Statistics for Data Science.