Data Science with Python
Data Science with Python is a 12+ hours FREE course – a journey from zero to mastery.
This is a 12 hours full Expert Data Science course. We focus on getting you started with Data Science with the most efficient tools and the right understanding of what adds value in a Data Science Project.
Most use too much time to cover too many technologies without adding value and end up creating poor quality Data Science Project. You don’t want to end up like that! Follow the Secret Data Science Blueprint, which will give a focused template covering all you need to create successful Data Science Projects.
This course consist of the following content.
- 15 video lessons – covers the Data Science Workflow and concepts, demonstrates everything on real data, introduce projects and shows a solution (YouTube video).
- 30 JuPyter Notebooks – with the full code and explanation from the lectures and projects (GitHub).
- 15 projects – structured with the Data Science Workflow and a solution explained in the end of video lessons (GitHub).
How to get started:
- Download all Jupyter Notebooks from repo (zip-file-download).
- Unzip download (main.zip) appropriate place.
- Launch Ananconda and start JuPyter Notebook (Install it from here if needed)
- Open the first Notebook from download.
- Start watching the first video lesson (YouTube video).
What Makes This Data Science Course Different
- Expert Data Scientists focus on creating valuable actionable insights for clients.
- Beginner Data Scientists focus on covering the biggest tech stack.
- Experts know they need to understand the problem from start, to get the right data, and create client value.
- Beginners do not understand how each step in the Data Science Workflow is crucial to add value to the next step.
Mastering the Data Science Workflow is crucial, along with the right tools, to become an Expert Data Scientist. This course will cover all you need to start your journey towards Data Science Mastery.
At the end of the course you will get a template covering all aspects to ensure your Data Science Project follows this flow and is done effectively with Python code using the right libraries.
What will you learn in the Data Science with Python course?
- Data Science Workflow
- Data Visualization
- pandas for Data Science
- Data Sources
- Web Scraping
- CSV, Excel & parquet files
- Where to find data
- Join (combine) data
- Statistics you need to know
- Machine Learning Models
- Linear Regression
- …also check out the Machine Learning Course
- Cleaning Data
- Feature Scaling
- Feature Selection
- Model Selection
At the end of the course you are provided with a template covering all aspects of the Data Science Workflow.
Lesson 00 – Introduction to the Data Science Workflow
In the first lesson you learn about the following.
- What makes a successful Data Scientist?
- Why Data Science?
- How did Data Science start – this will surprise you!
- How the Data Science Workflow works.
- What skills does a Data Scientist need?
- Beginner vs Expert Data Scientist.
- Data Science Workflow used on real data with Python code.
- A project to try it out yourself – make your first Data Science project.
- A solution to the project with code.
All the code examples are available from the GitHub.
This will give you an understanding of what Data Science is and how to become successful. How to focus your effort to get the fastest results and not waste time on learning all possible technologies for Data Science.
See the Video below.
Lesson 01 – Data Visualization for Data Science
Data Visualization for Data Science is not just how to present your data, it is about exploring data quality, and exploring data to get an understanding of the nature of the data.
Data visualization helps you to understand data fast. Our human brain is not good at understanding rows of numeric data. But when we are presented with data absorb information quickly. It improves our insights in data and enables us to make faster decisions.
In this lesson we will learn how to use pandas DataFrames integration with Matplotlib to visualize data in charts to understand the power of visualization for the three purposes: Data Quality, Data Exploration, and Data Presentation.