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Data Science A-Z™: Real-Life Data Science Exercises Included
Разное | Автор: LeeAndro | Добавлено: 13-10-2020, 17:48 | Просмотров (14) | Комментариев (0) | Жалоба |
Data Science A-Z™: Real-Life Data Science Exercises Included
Genre: eLearning | MP4 | Video: h264, 1920x1080 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 14.8 GB | Duration: 28 sections | 217 lectures | (21h 18m)

Extremely Hands-On.


What you'll learn

Successfully perform all steps in a complex Data Science project

Create Basic Tableau Visualisations

Perform Data Mining in Tableau

Understand how to apply the Chi-Squared statistical test

Apply Ordinary Least Squares method to Create Linear Regressions

Assess R-Squared for all types of models

Assess the Adjusted R-Squared for all types of models

Create a Simple Linear Regression (SLR)

Create a Multiple Linear Regression (MLR)

Create Dummy Variables

Interpret coefficients of an MLR

Read statistical software output for created models

Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models

Create a Logistic Regression

Intuitively understand a Logistic Regression

Operate with False Positives and False Negatives and know the difference

Read a Confusion Matrix

Create a Robust Geodemographic Sntation Model

Transform independent variables for modelling purposes

Derive new independent variables for modelling purposes

Check for multicollinearity using VIF and the correlation matrix

Understand the intuition of multicollinearity

Apply the Cumulative Accuracy Profile (CAP) to assess models

Build the CAP curve in Excel

Use Training and Test data to build robust models

Derive insights from the CAP curve

Understand the Odds Ratio

Derive business insights from the coefficients of a logistic regression

Understand what model deterioration actually looks like

Apply three levels of model maintenance to prevent model deterioration

Install and navigate SQL Server

Install and navigate Microsoft Visual Studio Shell

Clean data and look for anomalies

Use SQL Server Integration Services (SSIS) to upload data into a database

Create Conditional Splits in SSIS

Deal with Text Qualifier errors in RAW data

Create Scripts in SQL

Apply SQL to Data Science projects

Create stored procedures in SQL

Present Data Science projects to stakeholders

Requirements

Only a passion for success

All software used in this course is either available for Free or as a Demo version

Description

.. Incredibly Practical... Unbelievably Real!

This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it!

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:

How to clean and prepare your data for analysis

How to perform basic visualisation of your data

How to model your data

How to curve-fit your data

And finally, how to present your findings and wow the audience

This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challeg that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools:

SQL

SSIS

Tableau

Gretl

This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.

Or you can do the whole course and set yourself up for an incredible career in Data Science.

The choice is yours. Join the class and start learning today!

See you inside,

Sincerely,

Kirill Eremenko

Who this course is for:

Anybody with an interest in Data Science

Anybody who wants to improve their data mining skills

Anybody who wants to improve their statistical modelling skills

Anybody who wants to improve their data preparation skills

Anybody who wants to improve their Data Science presentation skills



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