Udacity – Data Scientist Nanodegree nd025 v1.0.0 2018-12
Udacity – Data Scientist Nanodegree nd025 v1.0.0 2018-12 Downloadly IRSpace
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Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science career. You’ll master the skills necessary to become a successful Data Scientist. You’ll work on projects designed by industry experts, and learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud. Build effective machine learning models, run data pipelines, build recommendation systems, and deploy solutions to the cloud with industry-aligned projects. 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. Figure Eight (formerly Crowdflower) crowdsourced the tagging and translation of messages to apply artificial intelligence to disaster response relief. In this project, learners will build a data pipeline to prepare the message data from major natural disasters around the world. They’ll build a machine learning pipeline to categorize emergency text messages based on the need communicated by the sender
What you’ll learn
- Learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders.
- Develop software engineering skills that are essential for data scientists, such as creating unit tests and building classes.
- Learn to work with data through the entire data science process, from running pipelines, transforming data, building models, and deploying solutions to the cloud.
- Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.
- Leverage what you’ve learned throughout the program to build your own open-ended Data Science project. This project will serve as a demonstration of your valuable abilities as a Data Scientist.
Who this course is for
- 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.
Specificatoin of Become a Data Scientist Nanodegree
- Publisher : Udacity
- Teacher : Josh Bernhard , Judit Lantos , David Drummond , Mike Yi , Andrew Paster , Luis Serrano , Juno Lee
- Language : English
- Level : Expert
- Number of Course : 105
- Duration : 39 hours and 22 minutes
Content of Become a Data Scientist Nanodegree
Part 01 : Welcome to the Nanodegree
Part 02 : Supervised Learning
Part 03 : Deep Learning
Part 04 : Unsupervised Learning
Part 05 : Congratulations
Part 06 (Elective): Prerequisite: Python for Data Analysis
Part 07 (Elective): Prerequisite: SQL
Part 08 (Elective): Prerequisite: Data Visualization
Part 09 (Elective): Prerequisite: Command Line Essentials
Part 10 (Elective): Prerequisite: Git & Github
Part 11 (Elective): Prerequisite: Linear Algebra
Part 12 (Elective): Prerequisite: Practical Statistics
Part 13 : Welcome to Term 2
Part 14 : Introduction to Data Science
Part 15 : Software Engineering
Part 16 : Data Engineering
Part 17 : Experimental Design & Recommendations
Requirements
Machine Learning
- Supervised and Unsupervised methods equivalent to those taught in the Intro to Machine Learning Nanodegree Program.
Python
- Python Programming including writing functions, building basic applications, and common libraries like NumPy and Pandas
- SQL programming including querying databases, using joins, aggregations, and subqueries.
- Comfortable using the Terminal and Github
Probability and Statistics
- Descriptive Statistics including calculating measures of center and spread
- Inferential Statistics including sampling distributions, hypothesis testing
Mathematics
- Calculus including maximizing and minimizing algebraic equations
- Linear Algebra including matrix manipulation and multiplication
Data Wrangling and Visualization
- Accessing database, CSV, and JSON data
- Data cleaning and transformations using pandas and Sklearn
- Data Visualization with matplotlib
- Exploratory and explanatory data analysis and visualization
Pictures
Sample Clip
Installation Guide
Click on the Index.html file and follow the links on every section to watch the course
Subtitle : English
Quality: 720p
Download Links
File size
7.16 GB
