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Data Science I (Python)

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

5 226 Graduates

Created By : Marco A. Gutiérrez

4th Week of February, 2019

Part Time

6 weeks

2.5 h/session

Data Science I (Python)

5 226 Graduates 4th Week of February, 2019 Part Time 6 weeks 2.5 h/session

What You'll Learn

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

Lesson 1: Pandas and Managing Data

- Exploration of Data
- DataFrames and Series
- Visualization
- Data Aggregation for Reporting
- Data Preparation: Cleaning, Transforming, Data Fusion

Lesson 2: Hello Machine Learning

- 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

Lesson 3: Delving Deeper into Supervised Learning

- 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

Lesson 4: Introduction to Recommendation Systems

- 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

Lesson 5: Developing a Practical Recommendation System

- 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

Lesson 6: Outstanding Material, Clarifications and Assessment

- 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

Timetable

COHORT

February 21st, 2019
(6 weeks)
April 6th, 2019
(6 weeks)
Location one-north The Cathay
Lesson 1 21/02/19
07:30pm - 10:00pm
06/04/19
09:30am - 12:00pm
Lesson 2 28/02/19
07:30pm - 10:00pm
13/04/19
09:30am - 12:00pm
Lesson 3 07/03/19
07:30pm - 10:00pm
20/04/19
09:30am - 12:00pm
Lesson 4 14/03/19
07:30pm - 10:00pm
27/04/19
09:30am - 12:00pm
Lesson 5 21/03/19
07:30pm - 10:00pm
04/05/19
09:30am - 12:00pm
Lesson 6 28/03/19
07:30pm - 10:00pm
11/05/19
09:30am - 12:00pm

Prerequisites

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

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

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.

Pre-requisites:

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

Show more ...

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