Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
What Will I 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 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
Requirements
- Only a passion for success
- All software used in this course is either available for Free or as a Demo version
Description
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
Who is the target audience?
- Anybody with an interest in Data Science
- Anybody who wants to improve their data mining skills
- Anybody who wants to improve their statistical modeling skills
- Anybody who wants to improve their data preparation skills
- Anybody who wants to improve their Data Science presentation skills
Created by Kirill Eremenko, SuperDataScience Team
Last updated 11/2018
English
English
Size: 5.67 GB
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