- Successfully perform all steps in a complex Data Science project
- Perform Data Mining in Tableau
- Apply Ordinary Least Squares method to Create Linear Regressions
- Assess the Adjusted R-Squared for all types of models
- Create a Multiple Linear Regression (MLR)
- Interpret coefficients of an MLR
- Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
- Intuitively understand a Logistic Regression
- Read a Confusion Matrix
- Transform independent variables for modelling purposes.
- Check for multicollinearity using VIF and the correlation matrix
- Apply the Cumulative Accuracy Profile (CAP) to assess models
- Use Training and Test data to build robust models.
- Understand the Odds Ratio
- Understand what model deterioration actually looks like
- Install and navigate SQL Server
- Clean data and look for anomalies.
- Create Conditional Splits in SSIS
- Create Scripts in SQL
- Create stored procedures in SQL
- Create Basic Tableau Visualisations
- Understand how to apply the Chi-Squared statistical test
- Assess R-Squared for all types of models
- Create a Simple Linear Regression (SLR)
- Create Dummy Variables
- Read statistical software output for created models
- Create a Logistic Regression
- Operate with False Positives and False Negatives and know the difference
- Create a Robust Geodemographic Segmentation Model
- Derive new independent variables for modelling purposes
- Understand the intuition of multicollinearity
- Build the CAP curve in Excel
- Derive insights from the CAP curve
- Derive business insights from the coefficients of a logistic regression
- Apply three levels of model maintenance to prevent model deterioration
- Install and navigate Microsoft Visual Studio Shell
- Use SQL Server Integration Services (SSIS) to upload data into a database
- Deal with Text Qualifier errors in RAW data
- Apply SQL to Data Science projects
- Present Data Science projects to stakeholders
- Introduction of data science 00:04:38 Preview
- profession of the future 00:01:27
- Areas of Data Science 00:07:51
- Intro (what you will learn in this section) 00:16:11
- Installing Tableau Desktop and Tableau Public (FREE) 00:01:48
- Challenge description + view data in file 00:03:41
- Connecting Tableau to a Data file - CSV file 00:06:11
- Intro (what you will learn in this section) 00:16:11
- Get the Dataset + Project Overview 00:07:44
- Only a passion for success
- All software used in this course is either available for Free or as a Demo version.
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.
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
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