About This Course
3 months program
The Data Scientist Nanodegree program is an advanced program designed to prepare you for data scientist jobs. As such, you should have a high comfort level with a variety of topics before starting the program. In order to successfully complete this program, we strongly recommend that the following prerequisites are fulfilled. If you do not have the necessary rerequisites, Bootcamp AI has courses and programs that prepare you for this Nanodegree program.
Certificate
To share in LinkedIn
You can share your Certificates in the Certifications section of your LinkedIn profile, on your printed resume, or in other documents.
Learning Objectives
By the end of the program, you’ll have an impressive portfolio of real-world projects, and valuable hands-on experience. You’ll also receive career support via profile and portfolios reviews to help make sure you’re ready to establish a successful data science career, and land a job you love.
Requirements
- Python programming, including common data analysis libraries (NumPy, pandas, Matplotlib).
- SQL programming
- Statistics (Descriptive and Inferential)
- Calculus
- Linear Algebra
- Experience wrangling and visualizing data
Target Audience
- This program offers an ideal path for experienced programmers and data analysts to advance their data science careers. If you’re interested in deepening your expertise in the fields of analytics, machine learning, data engineering, and/or data science, this is a great way to get hands on practice with a variety of techniques and learn to build end to end data science solutions.
Curriculum
565 Lessons
Welcome to the Nanodegree program
01. Welcome00:00:00
02. Meet the Instructors00:00:00
03. Term 2 Projects00:00:00
03.2 Term 2 Projects00:00:00
03.3 Term 2 Projects00:00:00
03.4 Term 2 Projects00:00:00
04. Program Structure & Syllabus
05. Learning Plan – First Two Weeks
06. How to Succeed00:00:00
Words of Encouragement00:00:00
The Skills That Set You Apart
The Data Science Process
Learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders
Communicating to Stakeholders
Project Write A Data Science Blog Post
In this project, learners will choose a dataset, identify three questions, and analyze the data to find
answers to these questions. They will create a GitHub repository with their project, and write a blog post to
communicate their findings to the appropriate audience. This project will help learners reinforce and extend
their knowledge of machine learning, data visualization, and communication.
Introduction to Software Engineering
In this lesson, you’ll write production-level code and practice object-oriented programming, which you can
integrate into machine learning projects.
Software Engineering Practices Pt I
Software Engineering Practices Pt II
OOP
Portfolio Exercise: Upload a Package to PyPi
Web Development
Portfolio Exercise: Deploy a Data Dashboard
Introduction to Data Engineering
ETL Pipelines
Introduction to NLP
Learn Natural Language Processing one of the fields with the most real applications of Deep Learning
Machine Learning Pipelines
Disaster Response Pipeline
Project1: Disaster Response Pipeline
Concepts in Experiment Design
Statistical Considerations in Testing
Statistical Considerations in Testing
AB Testing Case Study
A/B Testing Case Study
Portfolio Exercise Starbucks
Introduction to Recommendation Engines
Matrix Factorization for Recommendations
Recommendation Engines
Upcoming Lesson
Sentiment Prediction RNN
Convolutional Neural Networks
Transfer Learning
Weight Initialization
Autoencoders
Job Search
Find your dream job with continuous learning and constant effort
