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Data Science Course Outline Overview

This document outlines the course structure for a Data Science course. The course is divided into 5 main modules: 1) Data Wrangling with Python, 2) Data Analysis & Statistics, 3) Machine Learning, 4) Deep Learning, and 5) Individual specialization modules. The first four modules cover fundamental skills like data processing, visualization, statistical analysis, supervised and unsupervised machine learning, and deep learning. The final module allows students to specialize and work on projects from hiring partners.

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dazu861204
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193 views4 pages

Data Science Course Outline Overview

This document outlines the course structure for a Data Science course. The course is divided into 5 main modules: 1) Data Wrangling with Python, 2) Data Analysis & Statistics, 3) Machine Learning, 4) Deep Learning, and 5) Individual specialization modules. The first four modules cover fundamental skills like data processing, visualization, statistical analysis, supervised and unsupervised machine learning, and deep learning. The final module allows students to specialize and work on projects from hiring partners.

Uploaded by

dazu861204
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd

Data Science

course
Data science 2

Outline

1. Data Wrangling with Python 3

2. Data Analysis & Statistics 3

3. Machine Learning 4

4. Deep Learning 4

5. Individual specialisation modules 4

[Link]
Data science 3

Course structure

1. Data Wrangling with Python

Learn how to process and visualize data with NumPy, Pandas and other data visualization libraries.

Strengthen your linear algebra, Python programming and scientific computing skills in the process.

Python Master

Building Foundational Python Skills For Data Analytic

Improving Code Reliabilit

Cluster Analysis With Pytho

Containers & REST APIs

Data Processing with NumPy and Panda

Numerical Data with NumP

Exploratory Data Analysis with Pandas

Data Visualization with Pytho

Basic Chartin

Data Cleaning & Intermediate Chartin

Exploring Data With Advanced Charting

2. Data Analysis & Statistics

Upgrade your knowledge of statistics and leverage your new skills to create and test experimental

hypotheses and statistical modeling. Learn SQL to work with data in relational databases.

Understanding and Visualizing Data with Pytho


SQL For Data Analysi

Practical Statistics For Data Science

Inferential Statistical Analysi


Inferential Procedure

Confidence Interval

Hypothesis Testing
Statistical Modelin
Modeling Fundamental
Linear & Logistic Regressio

Multilevel and Marginal Model


Introduction To Bayesian Statistics

[Link]
Data science 4

3. Machine Learning
Learn how to use various types of supervised and unsupervised machine learning models, such as
KNNs, decision trees, random forests, support vector machines, gradient boosted trees, XGBoost,
LightGBM, K-Means clustering and more.
Supervised Machine Learning Fundamental
Introduction to Machine Learnin
Machine Learning Project
KNNs, Decision Trees, and Random Forest
Support Vector Machines
Gradient Boosted Trees & Feature Engineerin
Inferential Procedure
Confidence Interval
Hypothesis Testing
Unsupervised Learning & Hyperparameter Tunin
Dimensionality Reductio
Clusterin
Working with Imbalanced Dat
Hyperparameter Turning & Model Selection

4. Deep Learning
Learn how to build and use various neural network architectures with PyTorch. Apply these neural
networks to solve tabular data, computer vision and natural language processing problems.
Computer Visio
Deep Learning Fundamental
Introduction to PyTorc
Convolutional Neural Network
Transfer Learning
Natural Language Processin
Transformer
Recurrent neural network
Generative models
Practical Deep Learnin
Advanced NL
Advanced Deep Learnin
Delivering ML Project
Practical AI Ethics

5. Individual specialisation modules


Work on our hiring partners projects and build an internship-level portfolio.

[Link]

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