Data Science I (Python)

Gain hands-on experience with the latest neural network, artificial intelligence and data science techniques that employers are seeking!

  • TBD
  • 6 sessions
  • 2.5 hours/session
  • Data Science

About this course

The final step in the data science journey at UpCode Academy, our Data Science I (Python) course will equip students with practical skills using the Python language.

Students will learn to use and operate libraries such as Pandas, NumPy and Surprise to prepare data and build effective recommendation systems. Course materials provided will serve as useful reference that support post-course revision and practice.


Students are required to have basic knowledge of programming before signing up for this course.

If you want to pick up basic programming skills, please register for our Python Development course instead.

What You Get

Students will learn how to perform the various parts of the "data science process":
- Preparing data: Cleaning, transforming, etc.
- Selecting a machine learning model (algorithm & settings)
- Training a model
- Evaluating a model
- Making predictions


Our Data Science I (Python) course is the final course on our Data Science track and will equip you with all the skills you need to start a career in the Data Science field.

Course Plan

- Exploration of Data
- DataFrames and Series
- Visualization
- Data Aggregation for Reporting
- Data Preparation: Cleaning, Transforming, Data Fusion
- Introduction to Unsupervised Learning (clustering and data compression)
- Introduction to Supervised Learning (linear regression, and briefly, logistic regression, decision trees, & support vector classifiers)
- numpy Arrays
- Linear Regression: Talking about the Learning in Machine Learning
- Feature Generation
- Underfitting and Overfitting: Qualitatively and Quantitatively
- Model Selection in Linear Regression
- Another Regressor: The Decision Tree Regressor
- Mixing Methods: Clustering and Predictors
- Classification Performance: Confusion Matrix
- Classification Performance: The ROC Curve
- Classification Performance: Linking Classifier Performance to Business Outcomes
- Making Sense of Logistic Regression
- Visualizing Decision Trees
- Extracting Information from Decision Trees
- Visualizing Classification Boundaries
- Naive Bayes for Classification
- Item-Item Similarity-based Collaborative Filtering
- Exploring a Ratings Data Set and Similarity as a Basis for Recommendations
- Beginning to work on our Item-Item Similarity-based Collaborative Filtering Method
- Broadcasting in numpy
- Iterating on the Item-Item Similarity-based Collaborative Filtering Method
- Writing a performant Item/User-Based Collaborative Filtering method
- The Surprise package and other methods for collaborative filtering


This course requires a basic understanding of Python and Data Science. You should have sat and completed our Python Development Course and Data Science Intro Course or already have intermediate-level understanding of Python.

If you have not sat our Python Development or Data Science Intro course, our friendly Student Affairs Officers will reach out to you upon registration to confirm your mastery of Python and Data Science.

There are no open runs for this course at the moment. If you're interested in taking this course, you may join the waitlist and you will be notified when there are vacancies.

UpCode Academy

Attend coding classes taught by true experts working in the industry. Get practical instructions and interact with these practitioners during the classes.

  • 29 Courses
  • 1,315 Students
  • 29 Instructors

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