Data Scientist with Python
Career track
Data Scientist with Python
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
1. Introduction to Python (https://www.datacamp.com/courses/intro-to-python-for-data-science)
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
4 hours
2. Intermediate Python (https://www.datacamp.com/courses/intermediate-python)
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
4 hours
3. Investigating Netflix Movies and Guest Stars in The Office (https://www.datacamp.com/projects/1237)
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
2 hours
4. Data Manipulation with pandas (https://www.datacamp.com/courses/data-manipulation-with-pandas)
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
4 hours
5. The Android App Market on Google Play (https://www.datacamp.com/projects/619)
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
2 hours
6. Joining Data with pandas (https://www.datacamp.com/courses/joining-data-with-pandas)
Learn to combine data from multiple tables by joining data together using pandas.
4 hours
7. The GitHub History of the Scala Language (https://www.datacamp.com/projects/163)
Find the true Scala experts by exploring its development history in Git and GitHub.
2 hours
8. Introduction to Data Visualization with Matplotlib (https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib)
Learn how to create, customize, and share data visualizations using Matplotlib.
4 hours
9. Introduction to Data Visualization with Seaborn (https://www.datacamp.com/courses/introduction-to-data-visualization-with-seaborn)
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
4 hours
10. Python Data Science Toolbox (Part 1) (https://www.datacamp.com/courses/python-data-science-toolbox-part-1)
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
3 hours
11. Python Data Science Toolbox (Part 2) (https://www.datacamp.com/courses/python-data-science-toolbox-part-2)
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
4 hours
12. Intermediate Data Visualization with Seaborn (https://www.datacamp.com/courses/intermediate-data-visualization-with-seaborn)
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
4 hours
13. A Visual History of Nobel Prize Winners (https://www.datacamp.com/projects/441)
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
2 hours
14. Data Manipulation with Python](https://www.datacamp.com/signal)
15. Introduction to Importing Data in Python (https://www.datacamp.com/courses/introduction-to-importing-data-in-python)
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
3 hours
16. Intermediate Importing Data in Python (https://www.datacamp.com/courses/intermediate-importing-data-in-python)
Improve your Python data importing skills and learn to work with web and API data.
2 hours
17. Cleaning Data in Python (https://www.datacamp.com/courses/cleaning-data-in-python)
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
4 hours
18. Working with Dates and Times in Python (https://www.datacamp.com/courses/working-with-dates-and-times-in-python)
Learn how to work with dates and times in Python.
4 hours
19. Importing & Cleaning Data with Python](https://www.datacamp.com/signal)
20. Writing Functions in Python (https://www.datacamp.com/courses/writing-functions-in-python)
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
4 hours
21. Python Programming](https://www.datacamp.com/signal)
22. Exploratory Data Analysis in Python (https://www.datacamp.com/courses/exploratory-data-analysis-in-python)
Learn how to explore, visualize, and extract insights from data.
4 hours
23. Analyzing Police Activity with pandas (https://www.datacamp.com/courses/analyzing-police-activity-with-pandas)
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
4 hours
24. Statistical Thinking in Python (Part 1) (https://www.datacamp.com/courses/statistical-thinking-in-python-part-1)
Build the foundation you need to think statistically and to speak the language of your data.
3 hours
25. Statistical Thinking in Python (Part 2) (https://www.datacamp.com/courses/statistical-thinking-in-python-part-2)
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
4 hours
26. Dr. Semmelweis and the Discovery of Handwashing (https://www.datacamp.com/projects/20)
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
2 hours
27. Supervised Learning with scikit-learn (https://www.datacamp.com/courses/supervised-learning-with-scikit-learn)
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
4 hours
28. Predicting Credit Card Approvals (https://www.datacamp.com/projects/558)
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
31. Case Study: School Budgeting with Machine Learning in Python (https://www.datacamp.com/courses/case-study-school-budgeting-with-machine-learning-in-python)
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
Instructors
[Maggie MatsuiCurriculum Manager at DataCamp
(https://www.datacamp.com/instructors/maggiematsui) See all instructors
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