From your first line of Python to models you can trust: SQL, charts, statistics, then machine learning with scikit-learn. Real code in your browser, a chart in every ML lesson.
2,160,483+ codders enrolled across these courses
487 lessons539 challenges2,161 quiz questions
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Hands-on interactive lessons
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Quizzes to test your knowledge
A path certificate at the end, plus 8 along the wayReal scikit-learn code with its real output. Drag the slider to change one line and see what the model learns.
Small k memorizes every training point and draws a jagged border. Larger k smooths it out.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X, y = make_moons(n_samples=200, noise=0.3, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
k = 5
model = KNeighborsClassifier(n_neighbors=k)
model.fit(X_train, y_train)
print(f"train accuracy: {model.score(X_train, y_train):.2f}")
print(f"test accuracy: {model.score(X_test, y_test):.2f}")
xx, yy = np.meshgrid(np.linspace(-2, 3, 56), np.linspace(-1.5, 2, 40))
proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
proba = proba.reshape(xx.shape)
plt.figure(figsize=(5, 4))
plt.pcolormesh(xx, yy, proba, cmap="coolwarm", alpha=0.35)
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train,
cmap="coolwarm", edgecolors="k")
plt.title(f"k-nearest neighbors, k = {k}")
plt.show()
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Promotion zoneRead, listen, test yourself, ask the AI, or look up anything you've already covered. Every lesson meets you where you are.
A variable is a named container that stores a value you can reference later in your program.
In Python, you create one by writing the name, an equals sign, then the value you want to store.
The value can change over time. Reassigning the name simply points it to a new value.
Earn certificates for every course you complete. Add them to your LinkedIn profile and resume to showcase your coding expertise to employers.
Certificate of Completion
Python Fundamentals
Add to LinkedInLearn Python, then SQL, then how to explore and chart data with pandas and matplotlib, then enough statistics to tell a pattern from noise, then machine learning with scikit-learn. That is this roadmap, in that order. Then build two or three projects on public datasets that answer a question someone would care about, and write up what you found.
Python. It is the language of almost every data science job posting, and the libraries the rest of the path uses (pandas, matplotlib, scikit-learn) are Python libraries. The first step covers only the parts of Python the later steps need: variables, conditions, loops, functions and lists.
Not to start, and not for many jobs. Employers look for people who can get data with SQL, clean and chart it with pandas, reason about it with statistics and build a model they can defend. This path teaches the statistics with simulations and charts, with no calculus. A degree helps for research roles; a portfolio of honest projects helps everywhere.
About five to seven months at an hour a day. Python basics take around six weeks, SQL three to four, and the four machine learning sections about 40 hours together. The time after that, spent on your own projects, is what turns the skills into a portfolio.
No, and the page says so below. It covers classic machine learning, the models most business problems are solved with: regression, trees, forests, boosting, clustering and PCA. Neural networks, deep learning frameworks and large language models come after these, and they are easier to learn once you can split, train and evaluate a model properly.
Yes. Finish every step and you get one free certificate for the whole Data Scientist path. It lists each course you finished on the way, and links each course's own certificate, so anyone can check every skill it names. You can add it to your LinkedIn profile. It is a certificate of completion from Coddy, not an accredited qualification.
