This is a course which teaches you how to turn raw data into useful insights.
This course aims wide. In 6 weeks, we will cover all topics required to join a Data Science team. This includes data munging, data exploration, and machine learning, using python frameworks like numpy, pandas, matplotlib, and sklearn. (for a full list check the syllabus and the learning goals section).
This course is lean. You will learn just enough to analyse datasets from scratch. However Data Science is a vast subject, so additional resources are provided for deeper dives into any of its subfields.
This course is pragmatic. All lectures consist of slides explaining key theoretical concepts, followed by a hands-on python notebook with coding exercises.
- teach you all the theory and skills required to load, manipulate, and analyse structured and unstructured datasets
- give you hands-on experience training and evaluating Machine Learning models
- make you employable in the Data Science industry
- turn you into a jack of all trades
- guide you to become a master of few
- share some low quality AI memes
- give you 10 years of experience in ML
- turn you into a Deep Learning wizard
- make you publish a paper at NeurIPS
- build Skynet
There are excellent online degrees that focus on ML theory, and great practical tutorials that cover frameworks. The Practical Data Scientist blends both in a guided package to bootstrap your Data Science career. This course's mission is to enable all coders to get out there and analyse the world's problems one dataset at a time.
This course is perfect for the beginner coder looking to start a career in Data Science, the software engineer curious about Machine Learning technologies, or the AI enthusiast searching for more practical experience.
Beginner programming skills are required. A little python experience (can you define a function?), and some statistical basics (what's a standard deviation?) are recommended.
Camille Van Hoffelen has worked as an Machine Learning engineer for the last 7 years, with a focus on large scale Natural Language Processing systems. He was a lecturer in Machine Learning at Ilia State University, and is currently the CTO & Co-founder of Watergenics.
The Practical Data Scientist is taught as a live online course on Jungle Program. Join the next micro-class and find more details here.
The course content is also open-source and free to use. Here's a few ideas:
- For each lecture, read the slides, then go through the notebooks. Complete the 💪 and 🧠 exercises, then flip through the additional resources.
- Skip straight to a particular section/lecture if you have already taken 50 billion ML courses.
- Forget about the slides, find the notebook for that method you can't remember how to use, and copy paste to your heart's content.
- Test yourself with the assignments. Become the nerdiest Pokemon trainer there ever was.
Notebooks can be viewed in github, viewed in nbviewer, run locally with jupyter, run with mybinder, or run with google colab.
Data scientists can tame all types of data and reveal their secrets. This course takes us through all the python tools needed to turn raw data into useful insights.
- Data Munging
Students can manipulate and visualise tabular, time series, image, text, and geospatial datasets - Unsupervised Learning
Students can use clustering, dimensionality reduction, and anomaly detection methods - Supervised Learning
Students can train regression and classification models - Evaluation and Optimisation
Students can build accurate Machine Learning models - Exploratory Data Analysis
Students can analyse a public dataset from scratch
The nitty-gritty of data analysis: this chapter teaches you how to load, clean, and manipulate basic datasets in python.
- 1.0 Introduction
slides - 1.1 Numpy & Pandas
notebook slides live recording - 1.2 Tabular Data Pt.1
notebook - 1.3 Tabular Data Pt.2
notebook live recording
Beyond tables: learn how to extract and communicate key insights from time-series, text, and image datasets.
- 2.1 Time Series Data
notebook slides live recording - 2.2 Text & Image Data
notebook - 2.3 Data Visualization
notebook slides live recording
Analytical power-up: this chapter adds clustering, dimensionality reduction, and anomaly detection to your data analysis techniques.
- 3.1 Clustering
notebook slides live recording - 3.2 Dimensionality Reduction
notebook slides - 3.3 Anomaly Detection
notebook slides live recording
Moving past data summaries: this chapter introduces predictive models to solve fundamental machine learning tasks.
- 4.1 Supervised Learning Fundamentals
slides live recording - 4.2 Linear Regression
notebook slides - 4.3 Logistic Regression
notebook
### Week 5: Advanced Supervised Learning
Taming the beast: this chapter shows how build accurate and effective machine learning models.
- 5.1 Learning Better Pt.1
notebook - 5.2 Learning Better Pt.2
notebook - 5.3 Evaluation Fundamentals
notebook
This chapter tests your skills on a public dataset by completing an exploratory data analysis report, and training at least one Machine Learning model.
- Week 1
- Week 2
- Week 3
- Week 5
- Final Project
- Final Presentation
The coursework is split between five small assignments, a final project, and a final presentation.
The small assignments serve to synthesise the previous course content on your own, and put it to practice. They are all coding exercises: you will be given resources and/or code stubs, and will submit runnable code and some observations.
The final project tests everything that you have learnt from this course. This is a python notebook report like those data scientists make to share their experimental progress. It tests your ability to design, carry out, and communicate machine learning experiments. This is complemented by the final presentation, a 10mn talk to synthesize, and discuss the results.
This work is licensed under a Creative Commons Attribution 4.0 International License.
See the LICENSE.txt file for details.
