Data Science A-Z™: Real-Life Data Science Exercises Included Free Download

Data Science A-Z™: Real-Life Data Science Exercises Included Free Download

About this course:

Free download Data Science A-Z™: Real-Life Data Science Exercises Included, you can download this course Data Science A-Z™: Real-Life Data Science Exercises Included if you want to learn how to Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!. You will get 217 high quality recorded videos compressed in a zip file so it will be easy to download the whole course Data Science A-Z™: Real-Life Data Science Exercises Included with a single click, explained in English. Many users are already getting benefits from this course, so don't hesitate to download it and start learning now, it is completely free to download and use. You can start downloading Data Science A-Z™: Real-Life Data Science Exercises Included by clicking on the link below.

Before you can start learning from this course explained in English (US) you need to have Only a passion for success, All software used in this course is either available for Free or as a Demo version.

Data Science A-Z™: Real-Life Data Science Exercises Included is targeting for people that have interset in 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.

Finally you will learn how to 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 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 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.

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