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Data Science

at General Assembly - Online

(2652)

Course Details

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$3,950

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Description

Class Level: Beginner

Age Requirements: 18 and older

Average Class Size: 20

Teacher: Amy Roberts, Gianluca Campanella, Ian Hansel

Flexible Reschedule Policy: This provider has flexible, free rescheduling for any-in person workshop. Please see the cancellation policy for more details

What you'll learn in this data science course:

Overview

Skills & Tools

Use Python to mine datasets and predict patterns.

Production Standard

Build statistical models — regression and classification — that generate usable information from raw data.

The Big Picture

Master the basics of machine learning and harness the power of data to forecast what’s next.

Curriculum

Unit 1: Programming Basics

What is Data Science

Describe course syllabus and establish the classroom environment.
Answer the questions: "What is Data Science? What roles exist in Data Science?"
Define the workflow, tools and approaches data scientists use to analyze data.

Your Development Environment

Navigate through directories using the command line
Use git and GitHub to share repositories

Python Foundations

Conduct arithmetic and string operations in Python
Assign variables
Implement loops and conditional statements
Use Python to clean and edit datasets

Unit 2: Research Design and Exploratory Data Analysis

Exploratory Data Analysis

Use Data
Frames and Series to read data
Rename, remove, combine, select, and join data
Identify and handle null and missing values

Experiments and Hypothesis Testing

Determine causality and sampling bias
Test a hypothesis using a sample case study
Validate your findings using statistical analysis (p-values, confidence intervals)

Data Visualization in Python

Define key principles of data visualization
Create line plots, bar plots, histograms and box plots using Seaborn and Matplotlib

Statistics in Python

Use NumPy and Pandas libraries to analyze datasets using basic summary statistics
Create data visualization – scatter plots, scatter matrix, line graph, box plots, and histograms – to discern characteristics and trends in a dataset
Identify a normal distribution within a dataset using summary statistics and visualization

Unit 3: Foundations of Data Modeling

Linear Regression

Define data modeling and linear regression
Differentiate between categorical and continuous variables
Build a linear regression model using a dataset that meets the linearity assumption using the scikit-learn library

Evaluating Model Fit

Define regularization, bias, and errors metrics
Evaluate model fit by using loss functions including mean absolute error, mean squared error, root mean squared error
Select regression methods based on fit and complexity

KNN and Classification

Define a classification model
Build a K–Nearest Neighbors using the scikit–learn library
Evaluate and tune model by using metrics such as classification accuracy⁄error

Logistic Regression

Build a Logistic regression classification model using the scikit learn library
Describe the sigmoid function, odds, and odds ratios and how they relate to logistic regression
Evaluate a model using metrics such as classification accuracy ⁄ error, confusion matrix, ROC ⁄ AOC curves, and loss functions

Unit 4: Machine Learning

Decision Trees and Random Forest

Describe the difference between classification and regression trees and how to interpret these models
Explain and communicate the tradeoffs of decision trees vs regression models
Build decision trees and random forests using the scikit-learn library

Working with API Data

Access public APIs and get information back
Read and write data in JSON
Use the requests library

Natural Language Processing

Demonstrate how to tokenize natural language text using NLTK
Categorize and tag unstructured text data
Explain how to build a text classification model using NLTK

Working with Time Series Data

Explain why time series data is different than other data and how to account for it
Create rolling means and plot time series data using the Pandas library
Perform autocorrelation on time series data

Final Presentations

Present final presentation to peers, instructor, and guest panelists who will identify strengths and areas for improvement

FAQs

Why is this course relevant today?

Given the prevalence of technologies and the amount of data available in the online world about users, products, and the content that we generate, businesses can be making so much more well-informed decisions if this vast amount of data was more deeply analyzed through the use of data science. The data science course provides the tools, methods, and practical experience to enable you to make accurate predictions about data, which ultimately leads to better decision-making in business, and the use of smarter technology (think recommendation systems or targeted ads).

What practical skill sets can I expect to have upon completion of the course?

This course will provide you with technical skills in machine learning, algorithms, and data modeling which will allow you to make accurate predictions about your data. You will be creating your models using Python so you will gain a good grasp of this programming language. Furthermore, you will learn how to parse and clean your data which can take up to 70% of your time as a data scientist.

Whom will I be sitting next to in this course?

Individuals who have a strong interest in manipulating large data sets, finding patterns in data, and making predictions.

Are there any prerequisites?

A basic understanding of statistics
A basic understanding of variables, functions, and lists in Python

School Notes:
For students enrolling in 12 week part time and immersive classes, it is not recommended that you book more than one class simultaneously.

Still have questions? Ask the community.

Refund Policy

If you can't make it to a class/workshop, please email us at [email protected] at least 7 days before the scheduled event date. No refunds will be given after this timeframe.

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General Assembly (GA) equips individuals with the in-demand skills needed to build a career in today’s high-growth tech sectors. Their award-winning technical training includes flexible delivery, industry-tested curriculum, and a career services program.

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Data Science

What you'll learn in this data science course:

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Reviews of Classes at General Assembly (2,637)

School: General Assembly

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