Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
https://www.udemy.com/datascience/learn/v4/overview
Extremely Hands-On... 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 challenging 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.
Only a passion for success
All software used in this course is either available for Free or as a Demo version
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
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