INFINITESKILLS_DATA_EXPLORATION_IN_PYTHON_TRAINING_VIDEO_TUTORIAL-kEISO

Section

Appz

Group

kEISO

Size

884,14 MB

Files

21

Date

2015-12-14

NFO

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   [PUBLISHER] INFINITESKILLS                                     ██
   [RUNTIME  ] 3H 3M                 [LINK]                       ██
   [LEVEL    ] BEGINNER              [TYPE] RETAIL                ██
   [ISO SIZE ] 954.617.856 B         [CHKS] 21A80DAA              ██
   [ON STORE ] 2015.12.08            [$$$$] 200000000000 DOLLAR   ██
   [DISKNAME ] keiso_isdeinp                                      ██
   [HELD BY  ] ALLEN B. DOWNEY                                    ██
                                                                  ██
                                                                  ██
   If you're a fledgling data scientist with only cursory         ██
   statistical training and little experience with real world     ██
   data sets, you may feel like you're stumbling around in the    ██
   dark when you're asked to interpret and present data to        ██
   decision makers. How do you validate the data? What            ██
   analytic model should you use? How do you differentiate        ██
   between correlation and causation? How do you ensure that      ██
   your data is solid and your conclusions are on target?         ██
   Allen Downey, Professor of Computer Science at Olin College    ██
   of Engineering, author of Think Stats, Think Python, and       ██
   Think Complexity, provides safe passage around the common      ██
   pitfalls of exploratory data analysis, so you can manage,      ██
   analyze, and present data with confidence.                     ██
   - Learn the fundamental tools and methodologies used in        ██
   data science                                                   ██
   - Discover best practices regarding the ETL (Extract,          ██
   Transform, and Load) process and data validation               ██
   - Use the open science framework: practice version control,    ██
   replication, and data pipelining                               ██
   - Grasp the effectiveness of CDFs (Common Data Formats) in     ██
   visualizing distributions                                      ██
   - Choose the correct analytic model for your data              ██
   - Comprehend statistical inference, effect size, confidence    ██
   intervals, and hypothesis testing                              ██
   - Discern the relationship between variables: understand       ██
   scatter plots and scatter plot alternatives                    ██
   - Understand correlation, linear least squares, linear         ██
   regression, and logistic regression                            ██
   - Master the Zen of testing your data and your conclusions     ██
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  ▄░▒▓▀░░░      ░░░▄     ░░░  [LAXiTY]-[LMS]-[NOY]-[OUTLAWS]   ░░▐█▓█ ░█▌
 ▒▒▒░░   ░░░▀      ▄▄▄      ▀░░     [PANTHEON]-[PARADOX]      ░░ █▓▒██ ░▓░
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░▒░░ ░░▄▓░▀▀▓▓▄ ░░▒▌       ▄   [PRESSECiTRON]-[SO]-[SOUNDz]  ▄███     ▀████░
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