Data Scientist with Python
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Career track
Gain the career-building Python skills you need to succeed as a data scientist. No prior coding experience required. In this track, you'll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you'll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. You'll then work with real-world datasets to learn the statistical and machine learning techniques you need to train decision trees and use natural language processing (NLP). Start this track, grow your Python skills, and begin your journey to becoming a confident data scientist.
Python
Clock: 88 hours Learn: 23 Courses Apply: 6 Projects
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
4 hours
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
4 hours
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
2 hours
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
4 hours
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
2 hours
Learn to combine data from multiple tables by joining data together using pandas.
4 hours
Find the true Scala experts by exploring its development history in Git and GitHub.
2 hours
Learn how to create, customize, and share data visualizations using Matplotlib.
4 hours
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
4 hours
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
3 hours
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
4 hours
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
4 hours
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
2 hours
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
3 hours
Improve your Python data importing skills and learn to work with web and API data.
2 hours
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
4 hours
Learn how to work with dates and times in Python.
4 hours
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
4 hours
Learn how to explore, visualize, and extract insights from data.
4 hours
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
4 hours
Build the foundation you need to think statistically and to speak the language of your data.
3 hours
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
4 hours
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
2 hours
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
4 hours
Build a machine learning model to predict if a credit card application will get approved.
2 hours
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
4 hours
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
5 hours
Learn how to build a model to automatically classify items in a school budget.
4 hours
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
4 hours
Hugo Bowne-AndersonData Scientist at DataCamp
Justin Saddlemyer
Richie CottonCurriculum Architect at DataCamp
Maggie MatsuiCurriculum Manager at DataCamp
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