Introduction to Data Science {AM}

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Added on July 12, 2016 by AceMerlinin Other > Tutorials
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Introduction to Data Science {AM} (Size: 463.41 MB)

		AM.Torrents.nfo	4.81 KB
		420305_00_04 - Using knowledge checks.mp4	878.16 KB
		420305_00_03 - What you need to know.mp4	1.07 MB
		420305_00_02 - Exercise files.mp4	1.11 MB
		420305_00_01 - Welcome.mp4	5.45 MB
		420305_01_06 - Knowledge check_ What is data science_.mp4	642.63 KB
		420305_01_05 - Team.mp4	5.39 MB
		420305_01_04 - Roles.mp4	8.65 MB
		420305_01_01 - Demand.mp4	8.73 MB
		420305_01_03 - Pipeline.mp4	10.5 MB
		420305_01_02 - Venn diagram.mp4	10.56 MB
		420305_02_04 - Knowledge check_ Fields of study.mp4	702.89 KB
		420305_02_03 - Statistics.mp4	4.72 MB
		420305_02_02 - Programming.mp4	5.96 MB
		420305_02_01 - Big data.mp4	7.46 MB
		420305_03_02 - Knowledge check_ Ethics.mp4	642.46 KB
		420305_03_01 - Ethical issues.mp4	5.94 MB
		420305_04_06 - Knowledge check_ Data sources.mp4	672.14 KB
		420305_04_04 - Scrapping.mp4	4.53 MB
		420305_04_05 - Creating data.mp4	7.11 MB
		420305_04_01 - Metrics.mp4	8.37 MB
		420305_04_02 - Existing data.mp4	9.62 MB
		420305_04_03 - APIs.mp4	14.88 MB
		420305_05_03 - Knowledge check_ Data exploration.mp4	657.45 KB
		420305_05_02 - Exploratory statistics.mp4	10.36 MB
		420305_05_01 - Exploratory graphs.mp4	10.63 MB
		420305_06_06 - Knowledge check_ Programming.mp4	704.64 KB
		420305_06_04 - SQL.mp4	8.2 MB
		420305_06_01 - Spreadsheets.mp4	9.03 MB
		420305_06_05 - Web formats.mp4	9.59 MB
		420305_06_03 - Python.mp4	11.29 MB
		420305_06_02 - R.mp4	14.02 MB
		420305_07_06 - Knowledge check_ Mathematics.mp4	689.47 KB
		420305_07_04 - Big O.mp4	13.12 MB
		420305_07_02 - Systems of equations.mp4	13.38 MB
		420305_07_01 - Algebra.mp4	15.03 MB
		420305_07_05 - Bayes probability.mp4	20.35 MB
		420305_07_03 - Calculus.mp4	24.06 MB
		420305_08_05 - Knowledge check_ Applied statistics.mp4	697.05 KB
		420305_08_04 - Validating.mp4	8.14 MB
		420305_08_03 - Problems.mp4	12.35 MB
		420305_08_02 - Confidence.mp4	12.41 MB
		420305_08_01 - Hypothesis.mp4	14.16 MB
		420305_09_06 - Knowledge check_ Machine learning.mp4	757.2 KB
		420305_09_01 - Decision trees.mp4	12.74 MB
		420305_09_02 - Ensembles.mp4	13.81 MB
		420305_09_04 - Naive Bayes classifiers.mp4	13.82 MB
		420305_09_03 - k-nearest neighbors (kNN).mp4	14.42 MB
		420305_09_05 - Artificial neural networks.mp4	16.14 MB
		420305_10_05 - Knowledge check_ Communicating.mp4	716.56 KB
		420305_10_04 - Reproducible research.mp4	8.65 MB
		420305_10_02 - Actionable insights.mp4	12.42 MB
		420305_10_03 - Visualization for presentation.mp4	14.04 MB
		420305_10_01 - Interpretability.mp4	16.35 MB
		420305_11_01 - Next steps.mp4	6.35 MB
		Ex_Files_Intro_Data_Science.zip	947.96 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}