Jupyter Notebook for Everyone
Jupyter Notebook for Everyone
Beginner
4 Hrs
Learn to use the most powerful tool for working on data science projects and acquire practical fundamentals of data manipulation, visualization and building machine learning pipelines in python.
Learn to use the most powerful tool for working on data science projects and acquire practical fundamentals of data manipulation, visualization and building machine learning pipelines in python.
Overview
If you are looking for the right way how to organize your data science projects, and the right tool and workflow according to the latest trends, you are on the right place.
In this course you will learn everything you need to be comfortable to work on your data science projects. Primary, you will get detailed knowledge of the concepts and possibilities of the most used tool currently, jupyter, its extension jupyter lab, how to set jupyter hub to work with multiple users. You will get the basics on how to work with docker containers. Main programming language will be python, but we cover the foundations on how to work with other programing languages in jupyter as well, like R and Julia. We will show you how to work with it on cyber security use cases, how to manipulate data which fits into RAM with pandas, but we show you the options on how to work with big data too using dask. You will learn how to effectively visualize data with static or interactive charts in jupyter, and ways on how to present your work with various export options.
Machine learning is one of the ultimate topics today, and it will not be left out. You will learn basic concepts of selected supervised and unsupervised ML algorithms, how to build optimal ML pipelines using sklearn, starting from data preparation, variables transformations and selections and the final model. You will be able to build by yourself “good enough” models with minimum effort and theory, but with maximum practical usability using state-of-art algorithms like xgboost and lightgbm, and you will get the foundations on how to automatically tweak the hyperparameters of the pipeline to find the best ones. All of it in jupyter, jupyter lab and python.
What You Will Learn
- Acquire working knowledge of the Jupyter ecosystem
- Understanding Docker basics
- Learn Data analytics using pandas
- Familiarize working with big data using dask
- Understand data visualization using pandas, matplotlib, and plotly express
- Getting equipped with Machine learning practical fundamentals
- Understand about designing machine learning pipelines in the scikit-learn ecosystem
Prerequisites
- Basic level of Python, Linux or Windows, and Git.
Content
Chapter 1: Introduction to Jupyter Ecosystem
3 Videos
The Jupyter Ecosystem Why to Use Jupyter Notebooks? $7 Million Cybersecurity Scholarship by EC-Council Chapter 1 Quiz
Preview
Chapter 2: Setting Up Our Environment
3 Videos
What Do We Need to Install? Docker Basics Jupyter Notebooks on Docker Chapter 2 Quiz
Preview
Chapter 3: Jupyter Notebook – Interface, Features, and Components
6 Videos
Jupyter Notebook App UI File Browser File Editor Terminal Extensions Configuration Chapter 3 Quiz
Preview
Chapter 4: Working with the Notebook
17 Videos
UI - Main Panel and Toolbar Dataset Introduction Working With Cells Markdown Latex Simple Regression Model Working With Kernels Use Kernel from Other Environment R & Python in Same Notebook Downloading and Exporting the Notebook Running the Notebook and Exporting It via Terminal Parameterising the Notebook, Running, and Exporting It from Python Embedding HTML Interactivity with Ipywidgets Turning the Notebook into a Dashboard with Voila Debugging Python Code Sharing Notebooks Chapter 4 Quiz
Preview
Chapter 5: Juypter Lab
6 Videos
Jupyter Lab – Workspace Jupyter Lab UI Debugger Binary Classification Extensions Collaborative Editing Chapter 5 Quiz
Preview
Chapter 6: Data Analysis in Pandas
11 Videos
Pandas Dataframe & Series Loading Data into Dataframes Initial Data Audit Data Types Index and Multiindex Dataframe Operations Apply Method Merging Dataframes Summarizing Data with Crosstab and Pivot Table Summarizing Data Using groupby and agg Styling Pandas Dataframes in Juyter Notebooks Chapter 6 Quiz
Preview
Chapter 7: Data Visualization in Jupyter
7 Videos
Dataset Univariate Analysis – Numeric Variables Univariate Analysis – Categorical Variables Bivariate Analysis Multivariate Analysis Other Useful Plots Jupyter Lab Extensions for EDA Chapter 7 Quiz
Preview
Chapter 8: Working with Big Data Using Dask
8 Videos
Strategies for Dealing with Big Data Dask Introduction Our Dataset Dask Graph Dask Dashboard Analytics on a Dask Dataframe Part 1 Analytics on a Dask Dataframe Part 2 Visualisation on Dask Dataframe via Datashader Chapter 8 Quiz
Preview
Chater 9: Machine Learning in Jupyter
11 Videos
Kmeans Clustering - Theory Kmeans Clustering - Practice XGBoost – Theory XGBoost – Practice Cross Validation Making Pipelines Hyperparameter Tuning – Gridsearch Hyperparameter Tuning – Randomized Search Hyperparameter Tuning – tunesearchCV LightGBM – Theory LightGBM – Practice Chapter 9 Quiz
Preview
Chapter 10: Jupyter Hub
5 Videos
Jupyter Hub - Introduction Launching Jupyter Hub Configuration Creating Users Administration Panel Chapter 10 Quiz
Preview
This course is part of a learning path
skill path
Transforming Business Decisions with Data Analytics - Beginner
Get started with data analytics techniques to generate actionable business insights from data.
Instructor
Jaroslav Klen
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