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      • Supervised Learning with scikit-learn
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      • Linear Classifiers in Python
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      • Cluster Analysis in Python
      • Dimensionality Reduction in Python
      • Preprocessing for Machine Learning in Python
      • Machine Learning for Time Series Data in Python
      • Feature Engineering for Machine Learning in Python
      • Model Validation in Python
        • Basic Modeling in scikit-learn
        • Validation Basics
        • Cross Validation
        • Selecting the best model with Hyperparameter tuning.
      • Skill Assessment
      • Machine Learning Fundamentals in Python
      • Introduction to Natural Language Processing in Python
      • Feature Engineering for NLP in Python
      • Introduction to TensorFlow in Python
      • Introduction to Deep Learning in Python
      • Introduction to Deep Learning with Keras
        • 1. Introducing Keras
        • Chapter2
      • Introduction to Deep Learning with Keras
      • Advanced Deep Learning with Keras
      • Image Processing in Python
      • Image Processing with Keras in Python
      • Hyperparameter Tuning in Python
      • Introduction to PySpark
      • Machine Learning with PySpark
      • Winning a Kaggle Competition in Python
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  • Data Scientist with Python
  • 1. Introduction to Python (https://www.datacamp.com/courses/intro-to-python-for-data-science)
  • 2. Intermediate Python (https://www.datacamp.com/courses/intermediate-python)
  • 3. Investigating Netflix Movies and Guest Stars in The Office (https://www.datacamp.com/projects/1237)
  • 4. Data Manipulation with pandas (https://www.datacamp.com/courses/data-manipulation-with-pandas)
  • 5. The Android App Market on Google Play (https://www.datacamp.com/projects/619)
  • 6. Joining Data with pandas (https://www.datacamp.com/courses/joining-data-with-pandas)
  • 7. The GitHub History of the Scala Language (https://www.datacamp.com/projects/163)
  • 8. Introduction to Data Visualization with Matplotlib (https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib)
  • 9. Introduction to Data Visualization with Seaborn (https://www.datacamp.com/courses/introduction-to-data-visualization-with-seaborn)
  • 10. Python Data Science Toolbox (Part 1) (https://www.datacamp.com/courses/python-data-science-toolbox-part-1)
  • 11. Python Data Science Toolbox (Part 2) (https://www.datacamp.com/courses/python-data-science-toolbox-part-2)
  • 12. Intermediate Data Visualization with Seaborn (https://www.datacamp.com/courses/intermediate-data-visualization-with-seaborn)
  • 13. A Visual History of Nobel Prize Winners (https://www.datacamp.com/projects/441)
  • 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)
  • 16. Intermediate Importing Data in Python (https://www.datacamp.com/courses/intermediate-importing-data-in-python)
  • 17. Cleaning Data in Python (https://www.datacamp.com/courses/cleaning-data-in-python)
  • 18. Working with Dates and Times in Python (https://www.datacamp.com/courses/working-with-dates-and-times-in-python)
  • 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)
  • 21. Python Programming](https://www.datacamp.com/signal)
  • 22. Exploratory Data Analysis in Python (https://www.datacamp.com/courses/exploratory-data-analysis-in-python)
  • 23. Analyzing Police Activity with pandas (https://www.datacamp.com/courses/analyzing-police-activity-with-pandas)
  • 24. Statistical Thinking in Python (Part 1) (https://www.datacamp.com/courses/statistical-thinking-in-python-part-1)
  • 25. Statistical Thinking in Python (Part 2) (https://www.datacamp.com/courses/statistical-thinking-in-python-part-2)
  • 26. Dr. Semmelweis and the Discovery of Handwashing (https://www.datacamp.com/projects/20)
  • 27. Supervised Learning with scikit-learn (https://www.datacamp.com/courses/supervised-learning-with-scikit-learn)
  • 28. Predicting Credit Card Approvals (https://www.datacamp.com/projects/558)
  • 29. Unsupervised Learning in Python (https://www.datacamp.com/courses/unsupervised-learning-in-python)
  • 30. Machine Learning with Tree-Based Models in Python (https://www.datacamp.com/courses/machine-learning-with-tree-based-models-in-python)
  • 31. Case Study: School Budgeting with Machine Learning in Python (https://www.datacamp.com/courses/case-study-school-budgeting-with-machine-learning-in-python)
  • 32. Cluster Analysis in Python (https://www.datacamp.com/courses/cluster-analysis-in-python)
  • Instructors

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  1. Career Tracks

Data Scientist with Python

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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 ()

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

4 hours

Hugo Bowne-Anderson Headshot

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

Instructors

  • Hugo Bowne-AndersonData Scientist at DataCamp

  • Justin Saddlemyer

  • Richie CottonCurriculum Architect at DataCamp

  • Maggie MatsuiCurriculum Manager at DataCamp

2. Intermediate Python ()

Hugo Bowne-Anderson Headshot

3. Investigating Netflix Movies and Guest Stars in The Office ()

Justin Saddlemyer Headshot

4. Data Manipulation with pandas ()

Richie Cotton Headshot

5. The Android App Market on Google Play ()

Lavanya Gupta Headshot

6. Joining Data with pandas ()

Aaren Stubberfield Headshot

7. The GitHub History of the Scala Language ()

Anita Sarma Headshot

8. Introduction to Data Visualization with Matplotlib ()

Ariel Rokem Headshot

9. Introduction to Data Visualization with Seaborn ()

Erin Case Headshot

10. Python Data Science Toolbox (Part 1) ()

Hugo Bowne-Anderson Headshot

11. Python Data Science Toolbox (Part 2) ()

Hugo Bowne-Anderson Headshot

12. Intermediate Data Visualization with Seaborn ()

Chris Moffitt Headshot

13. A Visual History of Nobel Prize Winners ()

Rasmus Bååth Headshot

14. Data Manipulation with Python]()

15. Introduction to Importing Data in Python ()

Hugo Bowne-Anderson Headshot

16. Intermediate Importing Data in Python ()

Hugo Bowne-Anderson Headshot

17. Cleaning Data in Python ()

Adel Nehme Headshot

18. Working with Dates and Times in Python ()

Max Shron Headshot

19. Importing & Cleaning Data with Python]()

20. Writing Functions in Python ()

21. Python Programming]()

22. Exploratory Data Analysis in Python ()

Allen Downey Headshot

23. Analyzing Police Activity with pandas ()

Kevin Markham Headshot

24. Statistical Thinking in Python (Part 1) ()

Justin Bois Headshot

25. Statistical Thinking in Python (Part 2) ()

Justin Bois Headshot

26. Dr. Semmelweis and the Discovery of Handwashing ()

Rasmus Bååth Headshot

27. Supervised Learning with scikit-learn ()

Hugo Bowne-Anderson Headshot

28. Predicting Credit Card Approvals ()

Sayak Paul Headshot

29. ()

Benjamin Wilson Headshot

30. ()

Elie Kawerk Headshot

31. Case Study: School Budgeting with Machine Learning in Python ()

Peter Bull Headshot

32. ()

Shaumik Daityari Headshot
Track statement of accomplishment

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https://www.datacamp.com/courses/intermediate-python
https://www.datacamp.com/projects/1237
https://www.datacamp.com/courses/data-manipulation-with-pandas
https://www.datacamp.com/projects/619
https://www.datacamp.com/courses/joining-data-with-pandas
https://www.datacamp.com/projects/163
https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib
https://www.datacamp.com/courses/introduction-to-data-visualization-with-seaborn
https://www.datacamp.com/courses/python-data-science-toolbox-part-1
https://www.datacamp.com/courses/python-data-science-toolbox-part-2
https://www.datacamp.com/courses/intermediate-data-visualization-with-seaborn
https://www.datacamp.com/projects/441
https://www.datacamp.com/signal
https://www.datacamp.com/courses/introduction-to-importing-data-in-python
https://www.datacamp.com/courses/intermediate-importing-data-in-python
https://www.datacamp.com/courses/cleaning-data-in-python
https://www.datacamp.com/courses/working-with-dates-and-times-in-python
https://www.datacamp.com/signal
https://www.datacamp.com/courses/writing-functions-in-python
https://www.datacamp.com/signal
https://www.datacamp.com/courses/exploratory-data-analysis-in-python
https://www.datacamp.com/courses/analyzing-police-activity-with-pandas
https://www.datacamp.com/courses/statistical-thinking-in-python-part-1
https://www.datacamp.com/courses/statistical-thinking-in-python-part-2
https://www.datacamp.com/projects/20
https://www.datacamp.com/courses/supervised-learning-with-scikit-learn
https://www.datacamp.com/projects/558
Unsupervised Learning in Python
https://www.datacamp.com/courses/unsupervised-learning-in-python
Machine Learning with Tree-Based Models in Python
https://www.datacamp.com/courses/machine-learning-with-tree-based-models-in-python
https://www.datacamp.com/courses/case-study-school-budgeting-with-machine-learning-in-python
Cluster Analysis in Python
https://www.datacamp.com/courses/cluster-analysis-in-python
https://www.datacamp.com/instructors/hugobowne
https://www.datacamp.com/instructors/jsaddlemyer
https://www.datacamp.com/instructors/richie
https://www.datacamp.com/instructors/maggiematsui
https://www.datacamp.com/courses/intro-to-python-for-data-science
Hugo Bowne-Anderson Headshot
Justin Saddlemyer Headshot
Richie Cotton Headshot
Maggie Matsui Headshot