Python & DataCamp

DataCamp courses and Curriculum

The following list shows the required DataCamp courses for the Data Science with Python Track at TechAcademy. As a beginner, please stick to the courses of the "beginner" program. Ambitious beginners can, of course, take the advanced courses afterward. However, it would be best if you worked through the courses in the order we listed them.

The same applies to the advanced courses. Here, too, you should finish the specified courses in the given order. Since it can, of course, happen that you have already mastered the topics of an advanced course, you can replace some courses. If you are convinced that the course does not add value to you, feel free to replace it with one of the courses in the "Exchange Pool" (see list below). However, you should not pursue an exchange course until you finish all chapters from the advanced course: "Intermediate Python." We provide you with a recommended timeline in the Agenda.

To receive the certificate, both beginners and advanced learners must complete at least two-thirds of the curriculum (6/9 courses).

For the beginners, this means until -- and including -- the course "Joining Data with pandas (4h)" and for the advanced until --and including -- "Introduction to Linear Modeling in Python (4h)." In addition, you should complete at least two-thirds of the project tasks. After completing the curriculum and the project's (minimal) requirements, you will receive your TechAcademy certificate!

Python Fundamentals (Beginner Track)

1. Introduction to Python (4h)

2. Intermediate Python (4h)

3. Introduction to Functions in Python (3h)

4. Introduction to Data Visualization with Matplotlib (4h)

5. Data Manipulation with pandas (4h)

6. Joining Data with pandas (4h)

7. Exploratory Data Analysis in Python (4h)

8. Working with Dates and Times in Python (4h)

9. Introduction to Importing Data in Python (3h)

Data Science with Python (Advanced Track)

1. Intermediate Python (4h)

2. Introduction to Functions in Python (3h)

3. Python Toolbox (4h)

4. Cleaning Data in Python (4h)

5. Exploratory Data Analysis in Python (4h)

6. Introduction to Linear Modeling in Python (4h)

7. Statistical Thinking in Python (Part 1) (3h)

8. Supervised Learning with scikit-learn (4h)

9. Machine Learning for Time Series Data in Python (4h)

Data Science with Python (Advanced Track) - Exchange Pool

• Interactive Data Visualization with Bokeh (4h)

• Introduction to Data Visualization with Seaborn (4h)

• Time Series Analysis in Python (4h)

• Linear Classifiers in Python (4h)

• Unsupervised Learning in Python (4h)

• Introduction to Deep Learning in Python (4h)

• ARIMA Models in Python (4h)

• Web Scraping in Python (4h)

• Writing Efficient Python Code (4h)

• Writing Efficient Code with pandas (4h)

• Statistical Thinking in Python (Part 2)

• Machine Learning with Tree-based Models in Python

Helpful Links

While learning from and completing DataCamp courses is half the battle in climbing the mountain of learning Python, the following resources will help you to solving the semester project and help you in your future own projects.

Official Tutorials/Documentation:

• https://docs.python.org/3/tutorial/index.html

• https://jupyter.org/documentation

Further Explanations:

• https://pythonprogramming.net/

• https://automatetheboringstuff.com/

• https://www.reddit.com/r/learnpython

• https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook