Data Science: Complete Course

What can you learn from this “Data Science A to Z: The Complete Course” program?

  • How to perform each step in a complex Data Science project in a superior way
  • Making basic tableau visualizations
  • Doing data mining in the Tableau
  • Know how to implement the Chi-Squared statistical test
  • Implementation of basic Least Squares method for creating the linear regressions
  • Charging R-Squared for all kinds of models
  • Making a Simple Linear Regression
  • Making a Multiple Linear Regression
  • Setting up Dummy Variables
  • Interpretation of the coefficients in an MLR
  • Reading out the output of statistical software for creating models
  • Utilizing the Forward Selection, Backward Elimination, and Bidirectional Elimination methods for creating statistical models
  • Developing a Logical Regression
  • Active understanding of a Logistic Regression
  • Working effectively with False Positives and False Negatives. You will be able to know the difference between them efficiently
  • Reading out a Confusion Matrix
  • Making a Robust Geodemographic Segmentation Model in an appropriate manner
  • Converting the independent variables for modeling work
  • Interpreting new independent variables for efficient modeling work
  • Checking out the multicollinearity by utilizing the correlation matrix
  • Complete understanding of the intuition of multicollinearity
  • Implementing the Cumulative Accuracy Profile for assessing the models
  • Developing the CAP curve that is used in the Excel
  • Utilizing the training and check out data for building robust models
  • Concluding insights from the curve of the CAP
  • Complete knowledge regarding the Odds Ratio
  • Get a chance to know how the model deterioration looks like in reality
  • Implementing the three levels of model maintenance required for prevention of model deterioration
  • Installing and navigating the SQL Server
  • Installing and navigating the Microsoft Visual Studio Shell
  • Cleaning out the data and checking out all the anomalies
  • Utilizing the SQL Server Integration Services for uploading the data to a database
  • Developing the Conditional Splits in the SQL Server Integration Services
  • Dealing with the errors related to the Text Qualifier in the Raw data
  • Making scripts used in the SQL
  • Implementation of the SQL of the data science projects
  • Presenting the projects of data science for the stakeholders

Program Requirements

  • We only need individuals who want to become successful
  • All the software that will be used in this program are either free or comes with a demo version. So, you don’t need to buy anything extra.

Data Science A to Z: The Complete Course Details

It is a top-class program for the people who want to achieve unbelievable success in their life. This program doesn’t have those boring classes that include the same kind of content. This course uses a different approach to teaching about the data science.

This program will allow you to experience all the problems that a data scientist has to go through on day to day basis. Some of those issues are corrupt data, irregularities, and anomalies.

You will get a full description of the journey of data science.

After completing this Data Science A to Z: The Complete Course, you will be able to know about the following things:

How to clean out and making data ready for the analysis.

Procedures for carrying out the basic visualization of the data

Ways for modeling and curve-fit your data

To conclude, you will also learn how to present your research to attract the audience.

In addition, this course has many practical exercises through which you can deal with the real world much easier. There are homework exercises that can allow you to upgrade your knowledge and increase your confidence. Moreover, you will be able to learn about the real life data science that will help you while working in a company.

With the help of this program, you can develop a great understanding of below-mentioned tools:

  • Tableau
  • SQL
  • SSIS
  • Gretl

The pre-planned pathways are included in this course through which you can blend the sections of the program according to your preferences and need.

If you want to full course and looking to set an incredible career in data science, then this program is suitable for you.

However, the final decision is all yours about career. But if you want to make it incredible, then join this course and begin learning now!

Who can utilize the power of this Data Science A to Z: The Complete Course?

  • Individuals who are looking to raise the level of their data mining skills
  • Individuals who have a greater passion for data science
  • A Person who is looking for ways to make their statistical modeling skills better
  • Anybody who is seeking options for making their data preparation skills better
  • All the individuals who want to increase the professional level of their data science presentation skills.
  • In simple words, we can say that it is a very useful course for your career.

Starting Course

1
Welcome to Data Science A-Z™
2
Intro (what you will learn in this section)
3
Profession of the future
4
Areas of Data Science
5
IMPORTANT: Course Pathways
6
BONUS: Success Story
7
Welcome to Part 1
8
Intro (what you will learn in this section)
9
Installing Tableau Desktop and Tableau Public (FREE)
10
Challenge description + view data in file
11
Connecting Tableau to a Data file – CSV file
12
Navigating Tableau – Measures and Dimensions
13
Creating a calculated field
14
Adding colours
15
Adding labels and formatting
16
Exporting your worksheet
17
Section Recap
18
Tableau Basics
19
Intro (what you will learn in this section)
20
Get the Dataset + Project Overview
21
Connecting Tableau to an Excel File
22
How to visualise an ad-hoc A-B test in Tableau
23
Working with Aliases
24
Adding a Reference Line
25
Looking for anomalies
26
Handy trick to validate your approach / data
27
Section Recap
28
Tableau Basics
29
Intro (what you will learn in this section)
30
Creating bins & Visualizing distributions
31
Creating a classification test for a numeric variable
32
Combining two charts and working with them in Tableau
33
Validating Tableau Data Mining with a Chi-Squared test
34
Chi-Squared test when there is more than 2 categories
35
Visualising Balance and Estimated Salary distribution
36
Bonus: Chi-Squared Test (Stats Tutorial)
37
Bonus: Chi-Squared Test Part 2 (Stats Tutorial)
38
Section Recap
39
Part Completed
40
Welcome to Part 2
41
Intro (what you will learn in this section)
42
Types of variables: Categorical vs Numeric
43
Types of regressions
44
Ordinary Least Squares
45
R-squared
46
Adjusted R-squared
47
Intro (what you will learn in this section)
48
Introduction to Gretl
49
Get the dataset
50
Import data and run descriptive statistics
51
Reading Linear Regression Output
52
Plotting and analysing the graph
53
Intro (what you will learn in this section)
54
Caveat: assumptions of a linear regression
55
Get the dataset
56
Dummy Variables
57
Dummy Variable Trap
58
Ways to build a model: BACKWARD, FORWARD, STEPWISE
59
Backward Elimination – Practice time
60
Using Adjusted R-squared to create Robust models
61
Interpreting coefficients of MLR
62
Section Recap
63
Intro (what you will learn in this section)
64
Get the dataset
65
Binary outcome: Yes/No-Type Business Problems
66
Logistic regression intuition
67
Your first logistic regression
68
False Positives and False Negatives
69
Confusion Matrix
70
Interpreting coefficients of a logistic regression
71
Intro (what you will learn in this section)
72
Get the dataset
73
What is geo-demographic segmenation?
74
Let’s build the model – first iteration
75
Let’s build the model – backward elimination: STEP-BY-STEP
76
Transforming independent variables
77
Creating derived variables
78
Checking for multicollinearity using VIF
79
Correlation Matrix and Multicollinearity Intuition
80
Model is Ready and Section Recap
81
Intro (what you will learn in this section)
82
Accuracy paradox
83
Cumulative Accuracy Profile (CAP)
84
How to build a CAP curve in Excel
85
Assessing your model using the CAP curve
86
Get my CAP curve template
87
How to use test data to prevent overfitting your model
88
Applying the model to test data
89
Comparing training performance and test performance
90
Section Recap
91
Intro (what you will learn in this section)
92
Power insights from your CAP
93
Coefficients of a Logistic Regression – Plan of Attack (advanced topic)
94
Odds ratio (advanced topic)
95
Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
96
Deriving insights from your coefficients (advanced topic)
97
Section Recap
98
Intro (what you will learn in this section)
99
What does model deterioration look like?
100
Why do models deteriorate?
101
Three levels of maintenance for deployed models
102
Section Recap
103
Welcome to Part 3
104
Intro (what you will learn in this section)
105
Working with Data
106
What is a Data Warehouse? What is a Database?
107
Setting up Microsoft SQL Server 2014 for practice
108
Important: Practice Database
109
ETL for Data Science – what is Extract Transform Load (ETL)?
110
Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
111
Installing SSDT with MSVS Shell
112
Intro (what you will learn in this section)
113
Preparing your folder structure for your Data Science project
114
Download the dataset for this section
115
Two things you HAVE to do before the load
116
Notepad ++
117
Editpad Lite
118
Intro (what you will learn in this section)
119
Starting and navigating an SSIS Project
120
Creating a flat file source task and OLE DB destination
121
Setting up your flat file source connection
122
Setting up your database connection and creating a RAW table
123
Run the Upload & Disable
124
Due Dilligence: Upload Quality Assurance
125
Intro (what you will learn in this section)
126
Download the dataset for this section
127
How excel can mess up your data
128
Bulletproof Blueprint for Data Wrangling before the Load
129
SSIS Error: Text qualifier not specified
130
What do you do when your source file is corrupt? (Part 1)
131
What do you do when your source file is corrupt? (Part 2)
132
SSIS Error: Data truncation
133
Handy trick for finding anomalies in SQL
134
Automating Error Handling in SSIS: Conditional Split
135
Automating Error Handling in SSIS: Conditional Split (Level 2)
136
How to analyze the error files
137
Due Dilligence: the one thing you HAVE to do every time
138
Types of Errors in SSIS
139
Summary
140
Homework
Faq Content 1
Faq Content 2

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Enrolled: 55 students
Duration: 10 Weeks
Lectures: 140
Video: 72 hours
Level: Advanced

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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed