Introduction to Data Science {AM}

seeders: 3
leechers: 14
Added on July 12, 2016 by AceMerlinin Other > Tutorials
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Introduction to Data Science {AM} (Size: 463.41 MB)
 AM.Torrents.nfo4.81 KB
 420305_00_04 - Using knowledge checks.mp4878.16 KB
 420305_00_03 - What you need to know.mp41.07 MB
 420305_00_02 - Exercise files.mp41.11 MB
 420305_00_01 - Welcome.mp45.45 MB
 420305_01_06 - Knowledge check_ What is data science_.mp4642.63 KB
 420305_01_05 - Team.mp45.39 MB
 420305_01_04 - Roles.mp48.65 MB
 420305_01_01 - Demand.mp48.73 MB
 420305_01_03 - Pipeline.mp410.5 MB
 420305_01_02 - Venn diagram.mp410.56 MB
 420305_02_04 - Knowledge check_ Fields of study.mp4702.89 KB
 420305_02_03 - Statistics.mp44.72 MB
 420305_02_02 - Programming.mp45.96 MB
 420305_02_01 - Big data.mp47.46 MB
 420305_03_02 - Knowledge check_ Ethics.mp4642.46 KB
 420305_03_01 - Ethical issues.mp45.94 MB
 420305_04_06 - Knowledge check_ Data sources.mp4672.14 KB
 420305_04_04 - Scrapping.mp44.53 MB
 420305_04_05 - Creating data.mp47.11 MB
 420305_04_01 - Metrics.mp48.37 MB
 420305_04_02 - Existing data.mp49.62 MB
 420305_04_03 - APIs.mp414.88 MB
 420305_05_03 - Knowledge check_ Data exploration.mp4657.45 KB
 420305_05_02 - Exploratory statistics.mp410.36 MB
 420305_05_01 - Exploratory graphs.mp410.63 MB
 420305_06_06 - Knowledge check_ Programming.mp4704.64 KB
 420305_06_04 - SQL.mp48.2 MB
 420305_06_01 - Spreadsheets.mp49.03 MB
 420305_06_05 - Web formats.mp49.59 MB
 420305_06_03 - Python.mp411.29 MB
 420305_06_02 - R.mp414.02 MB
 420305_07_06 - Knowledge check_ Mathematics.mp4689.47 KB
 420305_07_04 - Big O.mp413.12 MB
 420305_07_02 - Systems of equations.mp413.38 MB
 420305_07_01 - Algebra.mp415.03 MB
 420305_07_05 - Bayes probability.mp420.35 MB
 420305_07_03 - Calculus.mp424.06 MB
 420305_08_05 - Knowledge check_ Applied statistics.mp4697.05 KB
 420305_08_04 - Validating.mp48.14 MB
 420305_08_03 - Problems.mp412.35 MB
 420305_08_02 - Confidence.mp412.41 MB
 420305_08_01 - Hypothesis.mp414.16 MB
 420305_09_06 - Knowledge check_ Machine learning.mp4757.2 KB
 420305_09_01 - Decision trees.mp412.74 MB
 420305_09_02 - Ensembles.mp413.81 MB
 420305_09_04 - Naive Bayes classifiers.mp413.82 MB
 420305_09_03 - k-nearest neighbors (kNN).mp414.42 MB
 420305_09_05 - Artificial neural networks.mp416.14 MB
 420305_10_05 - Knowledge check_ Communicating.mp4716.56 KB
 420305_10_04 - Reproducible research.mp48.65 MB
 420305_10_02 - Actionable insights.mp412.42 MB
 420305_10_03 - Visualization for presentation.mp414.04 MB
 420305_10_01 - Interpretability.mp416.35 MB
 420305_11_01 - Next steps.mp46.35 MB
 Ex_Files_Intro_Data_Science.zip947.96 KB

Description

Introduction to Data Science with Barton Poulson

Introduction to Data Science provides a comprehensive overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data. While most only think of the "big subject," big data, there are many more fields and concepts to explore. Here Barton Poulson explores disciplines such as programming, statistics, mathematics, machine learning, data analysis, visualization, and (yes) big data. He explains why data scientists are now in such demand, and the skills required to succeed in different jobs. He shows how to obtain data from legitimate open-source repositories via web APIs and page scraping, and introduces specific technologies (R, Python, and SQL) and techniques (support vector machines and random forests) for analysis. By the end of the course, you should better understand data science's role in making meaningful insights from the complex and large sets of data all around us.

Level: Beginner
Length: 3h 6m
Released: July 5, 2016

Topics include:
The demand for data science
Roles and careers
Ethical issues in data science
Sourcing data
Exploring data through graphs and statistics
Programming with R, Python, and SQL
Data science in math and statistics
Data science and machine learning
Communicating with data


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463.41 MB
seeders:3
leechers:14
Introduction to Data Science {AM}