Data Science A-Z™: Real-Life Data Science Exercises Included | Udemy

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xDATA SCIENCE A-Z™: REAL-LIFE DATA SCIENCE EXERCISES INCLUDED

Data Science A-ZTM: In this course you will Learn Data Science Gradual through real Analytics examples like Data Mining & Modeling or  Tableau Visualization & more!

Created by: Kirill Eremenko

Last Updated: 5/2017

English

Dutch

French

Hindi

Korean

Polish

Traditional Chinese

What will you learn in this course ?

  • In this course you will perform all steps in a Complicated data Science Project.
  • Build the basic Tableau Visualizations.
  • Achieve data mining in  tableau.
  • Explain 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, & Bidirectional Elimination methods to create statistical models.
  • Create a Logistic Regression.
  • Intuitively understand a Logistic Regression.
  • Operate with False Positives and False Negatives & know the difference.
  • Read a Confusion Matrix.
  • Create a Robust Geodemographic Segmentation 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 & navigate SQL Server.
  • Install & navigate Microsoft Visual Studio Shell.
  • Clean data & 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=>

  • You will need only a passion for success.
  • All Software  which is used in this course either there are available in free as demo software.

Description:

There will be no one of those who is fluffy classes where everything works . It would be training for you smooth sailing. this course will throws you into the buried end. In this course you will learn how to clean & prepare your data for analysis & You will learn how to perform basic visualisation of your data or model your data. If you attend your course and do practical exercises than they work will be like piece of cake.

Who is the target audience ?

  • Anyone who is interested in data science.
  • Anyone who wish to improve their data mining skills.
  • Anyone who wish to improve their statistical modelling skills.
  • Anyone who wish to improve their data preparation skills.
  • Anyone who wish to improve their data science presentation skills.

Size: 8.54G

Content retrieved from: https://www.udemy.com/datascience/.

 

 

 

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