Data Science Track

Learn the fundamentals of data science, even if you have no prior data science experience! Gain confidence and applicable skills so that you can model, predict, and visualize data to drive strategic impact.
5 Full Courses
$955
Courses: 5
Content: 56.5 hours
Students: 140,000
Average Rating:

Includes these 5 top-rated courses

    Data Science A-Z™: Real-Life Data Science Exercises Included
    $200
    Tableau 10 A-Z: Hands-On Tableau Training For Data Science!
    $200
    Data Science and Machine Learning with Python - Hands On!
    $160
    R Programming A-Z™: R For Data Science With Real Exercises!
    $200
    The Complete SQL Bootcamp
    $195

Course Details

Data Science A-Z™: Real-Life Data Science Exercises Included
210 lectures
21 hours
All Levels

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

Kirill Eremenko, Data Scientist & Forex Systems Expert

My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly - personal freedom.

In my other life I am a Data Scientist - I study numbers to analyze patterns in business processes and human behaviour... Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading :) EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex - Love It All!

Summary

To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

SuperDataScience Team, Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

Sections
1 lectures 5 mins
5 lectures 20 mins
  • Lecture 2. Intro (what you will learn in this section) 00:44
  • Lecture 3. Profession of the future 06:58
  • Lecture 4. Areas of Data Science 05:58
  • Lecture 5. IMPORTANT: Course Pathways Free Preview 05:52
  • Lecture 6. BONUS: Success Story 00:53
1 lectures 2 mins
  • Lecture 7. Welcome to Part 1 01:57
10 lectures 1 hour 1 quiz
  • Lecture 8. Intro (what you will learn in this section) 00:28
  • Lecture 9. Installing Tableau Desktop and Tableau Public (FREE) 06:08
  • Lecture 10. Challenge description + view data in file 02:32
  • Lecture 11. Connecting Tableau to a Data file - CSV file 05:17
  • Lecture 12. Navigating Tableau - Measures and Dimensions 08:42
  • Lecture 13. Creating a calculated field 06:14
  • Lecture 14. Adding colours 07:37
  • Lecture 15. Adding labels and formatting 11:00
  • Lecture 16. Exporting your worksheet 07:40
  • Lecture 17. Section Recap 03:34
  • Quiz 1. Tableau Basics 5 questions
9 lectures 1 hour
  • Lecture 18. Intro (what you will learn in this section) 00:44
  • Lecture 19. Get the Dataset + Project Overview 07:12
  • Lecture 20. Connecting Tableau to an Excel File 03:56
  • Lecture 21. How to visualise an ad-hoc A-B test in Tableau Free Preview 06:29
  • Lecture 22. Working with Aliases 04:05
  • Lecture 23. Adding a Reference Line 04:53
  • Lecture 24. Looking for anomalies 08:35
  • Lecture 25. Handy trick to validate your approach / data 09:13
  • Lecture 26. Section Recap 05:04
11 lectures 1.5 hours
  • Lecture 27. Intro (what you will learn in this section) 00:44
  • Lecture 28. Creating bins & Visualizing distributions 09:55
  • Lecture 29. Creating a classification test for a numeric variable Free Preview 04:25
  • Lecture 30. Combining two charts and working with them in Tableau 08:31
  • Lecture 31. Validating Tableau Data Mining with a Chi-Squared test 10:29
  • Lecture 32. Chi-Squared test when there is more than 2 categories 08:15
  • Lecture 33. Visualising Balance and Estimated Salary distribution 11:04
  • Lecture 34. Bonus: Chi-Squared Test (Stats Tutorial) 19:12
  • Lecture 35. Bonus: Chi-Squared Test Part 2 (Stats Tutorial) 09:10
  • Lecture 36. Section Recap 05:44
  • Lecture 37. Part Completed 01:38
1 lectures 4 mins
  • Lecture 38. Welcome to Part 2 03:54
6 lectures 32 mins
  • Lecture 39. Intro (what you will learn in this section) 00:29
  • Lecture 40. Types of variables: Categorical vs Numeric 05:26
  • Lecture 41. Types of regressions 08:09
  • Lecture 42. Ordinary Least Squares 03:11
  • Lecture 43. R-squared Free Preview 05:11
  • Lecture 44. Adjusted R-squared 09:56
6 lectures 23 mins
  • Lecture 45. Intro (what you will learn in this section) 00:37
  • Lecture 46. Introduction to Gretl 02:34
  • Lecture 47. Get the dataset 04:03
  • Lecture 48. Import data and run descriptive statistics 04:25
  • Lecture 49. Reading Linear Regression Output 06:48
  • Lecture 50. Plotting and analysing the graph 04:22
10 lectures 1.5 hours
  • Lecture 51. Intro (what you will learn in this section) 01:15
  • Lecture 52. Caveat: assumptions of a linear regression 01:47
  • Lecture 53. Get the dataset 04:12
  • Lecture 54. Dummy Variables 08:05
  • Lecture 55. Dummy Variable Trap 02:10
  • Lecture 56. Ways to build a model: BACKWARD, FORWARD, STEPWISE Free Preview 15:41
  • Lecture 57. Backward Elimination - Practice time 16:08
  • Lecture 58. Using Adjusted R-squared to create Robust models 10:17
  • Lecture 59. Interpreting coefficients of MLR 12:47
  • Lecture 60. Section Recap 04:15
8 lectures 1 hour
  • Lecture 61. Intro (what you will learn in this section) 01:34
  • Lecture 62. Get the dataset 04:13
  • Lecture 63. Binary outcome: Yes/No-Type Business Problems 09:09
  • Lecture 64. Logistic regression intuition Free Preview 17:03
  • Lecture 65. Your first logistic regression 08:04
  • Lecture 66. False Positives and False Negatives 08:01
  • Lecture 67. Confusion Matrix 04:57
  • Lecture 68. Interpreting coefficients of a logistic regression 10:03
10 lectures 1 hour
  • Lecture 69. Intro (what you will learn in this section) 01:01
  • Lecture 70. Get the dataset 07:32
  • Lecture 71. What is geo-demographic segmenation? 05:05
  • Lecture 72. Let's build the model - first iteration 08:26
  • Lecture 73. Let's build the model - backward elimination: STEP-BY-STEP 11:11
  • Lecture 74. Transforming independent variables 10:09
  • Lecture 75. Creating derived variables 06:09
  • Lecture 76. Checking for multicollinearity using VIF 08:11
  • Lecture 77. Correlation Matrix and Multicollinearity Intuition 08:20
  • Lecture 78. Model is Ready and Section Recap 06:27
10 lectures 1 hour
  • Lecture 79. Intro (what you will learn in this section) 00:37
  • Lecture 80. Accuracy paradox 02:11
  • Lecture 81. Cumulative Accuracy Profile (CAP) Free Preview 11:16
  • Lecture 82. How to build a CAP curve in Excel 14:47
  • Lecture 83. Assessing your model using the CAP curve 07:11
  • Lecture 84. Get my CAP curve template 06:20
  • Lecture 85. How to use test data to prevent overfitting your model 03:34
  • Lecture 86. Applying the model to test data 08:09
  • Lecture 87. Comparing training performance and test performance 11:33
  • Lecture 88. Section Recap 03:33
7 lectures 1 hour
  • Lecture 89. Intro (what you will learn in this section) 00:34
  • Lecture 90. Power insights from your CAP 13:52
  • Lecture 91. Coefficients of a Logistic Regression - Plan of Attack (advanced topic) 03:47
  • Lecture 92. Odds ratio (advanced topic) 08:29
  • Lecture 93. Odds Ratio vs Coefficients in a Logistic Regression (advanced topic) 07:08
  • Lecture 94. Deriving insights from your coefficients (advanced topic) 13:15
  • Lecture 95. Section Recap 03:26
5 lectures 31 mins
1 lectures 2 mins
  • Lecture 101. Welcome to Part 3 02:24
8 lectures 33 mins
  • Lecture 102. Intro (what you will learn in this section) 00:23
  • Lecture 103. Working with Data Free Preview 01:15
  • Lecture 104. What is a Data Warehouse? What is a Database? 03:28
  • Lecture 105. Setting up Microsoft SQL Server 2014 for practice 08:05
  • Lecture 106. Important: Practice Database 09:44
  • Lecture 107. ETL for Data Science - what is Extract Transform Load (ETL)? 02:01
  • Lecture 108. Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ? 04:04
  • Lecture 109. Installing SSDT with MSVS Shell 04:24
6 lectures 12 mins
  • Lecture 110. Intro (what you will learn in this section) 00:48
  • Lecture 111. Preparing your folder structure for your Data Science project 02:20
  • Lecture 112. Download the dataset for this section 01:27
  • Lecture 113. Two things you HAVE to do before the load 04:56
  • Lecture 114. Notepad ++ 01:00
  • Lecture 115. Editpad Lite 01:11
7 lectures 23 mins
  • Lecture 116. Intro (what you will learn in this section) 00:50
  • Lecture 117. Starting and navigating an SSIS Project 01:46
  • Lecture 118. Creating a flat file source task and OLE DB destination 01:53
  • Lecture 119. Setting up your flat file source connection 06:08
  • Lecture 120. Setting up your database connection and creating a RAW table 07:43
  • Lecture 121. Run the Upload & Disable 02:39
  • Lecture 122. Due Dilligence: Upload Quality Assurance 02:02
16 lectures 2 hours
  • Lecture 123. Intro (what you will learn in this section) 00:50
  • Lecture 124. Download the dataset for this section 00:46
  • Lecture 125. How excel can mess up your data 03:46
  • Lecture 126. Bulletproof Blueprint for Data Wrangling before the Load 07:13
  • Lecture 127. SSIS Error: Text qualifier not specified 07:15
  • Lecture 128. What do you do when your source file is corrupt? (Part 1) 18:01
  • Lecture 129. What do you do when your source file is corrupt? (Part 2) 06:09
  • Lecture 130. SSIS Error: Data truncation 15:56
  • Lecture 131. Handy trick for finding anomalies in SQL 03:45
  • Lecture 132. Automating Error Handling in SSIS: Conditional Split Free Preview 08:20
  • Lecture 133. Automating Error Handling in SSIS: Conditional Split (Level 2) 09:03
  • Lecture 134. How to analyze the error files 16:40
  • Lecture 135. Due Dilligence: the one thing you HAVE to do every time 04:57
  • Lecture 136. Types of Errors in SSIS 04:00
  • Lecture 137. Summary 19:06
  • Lecture 138. Homework 03:39
17 lectures 1 hour
  • Lecture 139. Intro (what you will learn in this section) 00:31
  • Lecture 140. Download the dataset for this section 00:38
  • Lecture 141. Getting To Know MS SQL Management Studio 02:14
  • Lecture 142. Shortcut to upload the data 04:20
  • Lecture 143. SELECT * Statement 05:52
  • Lecture 144. Using the WHERE clause to filter data 05:50
  • Lecture 145. How to use Wildcards / Regular Expressions in SQL (% and _) 04:38
  • Lecture 146. Comments in SQL 02:43
  • Lecture 147. Order By 05:49
  • Lecture 148. Data Types in SQL 07:54
  • Lecture 149. Implicit Data Conversion in SQL 03:35
  • Lecture 150. Using Cast() vs Convert() 03:51
  • Lecture 151. Working with NULLs 05:03
  • Lecture 152. Understanding how LEFT, RIGHT, INNER, and OUTER joins work 06:18
  • Lecture 153. Joins with duplicate values 02:32
  • Lecture 154. Joining on multiple fields 05:21
  • Lecture 155. Practicing Joins 05:00
16 lectures 1.5 hours
  • Lecture 156. Intro (what you will learn in this section) 00:57
  • Lecture 157. RAW, WRK, DRV tables 05:54
  • Lecture 158. Download the dataset for this section 01:32
  • Lecture 159. Create your first Stored Proc in SQL 03:30
  • Lecture 160. Executing Stored Procedures 02:49
  • Lecture 161. Modifying Stored Procedures 08:25
  • Lecture 162. Create table 09:30
  • Lecture 163. Insert INTO Free Preview 05:42
  • Lecture 164. Check if table exists + drop table + Truncate 05:59
  • Lecture 165. Intermediate Recap - Procs 04:16
  • Lecture 166. Create the proc for the second file 11:36
  • Lecture 167. Adding leading zeros 07:29
  • Lecture 168. Converting data on the fly 10:21
  • Lecture 169. How to create a proc template 07:52
  • Lecture 170. Archiving Procs 04:38
  • Lecture 171. What you can do with these tables going forward [drv files etc.] 13:50
12 lectures 1 hour
  • Lecture 172. Intro (what you will learn in this section) 00:53
  • Lecture 173. Download the dataset for this section 00:46
  • Lecture 174. Upload the data to RAW table 11:02
  • Lecture 175. Create Stored Proc 05:09
  • Lecture 176. How to deal with errors using the isnumeric() function 07:45
  • Lecture 177. How to deal errors using the len() function 07:36
  • Lecture 178. How to deal with errors using the isdate() function 07:40
  • Lecture 179. Additional Quality Assurance check: Balance 03:51
  • Lecture 180. Additional Quality Assurance check: ZipCode Free Preview 03:17
  • Lecture 181. Additional Quality Assurance check: Birthday 04:08
  • Lecture 182. Part Completed 09:52
  • Lecture 183. ETL Error Handling "Vehicle Service" Project 07:45
1 lectures 2 mins
  • Lecture 184. Welcome to Part 4 01:31
8 lectures 29 mins
  • Lecture 185. Intro (what you will learn in this section) 00:44
  • Lecture 186. Cross-departmental Work 04:13
  • Lecture 187. Come to me with a Business Problem Free Preview 02:10
  • Lecture 188. Setting expectations and pre-project communication 03:30
  • Lecture 189. Go and sit with them 05:20
  • Lecture 190. The art of saying "No" 05:24
  • Lecture 191. Sometimes you have to go to the top 02:37
  • Lecture 192. Building a data culture 05:07
11 lectures 1 hour
  • Lecture 193. Intro (what you will learn in this section) 01:42
  • Lecture 194. Case study Free Preview 02:00
  • Lecture 195. Analysing the intro 03:33
  • Lecture 196. Intro dissection - recap 09:26
  • Lecture 197. REAL Data Science Presentation Walkthrough - Make Your Audience Say "WOW" 16:29
  • Lecture 198. My brainstorming method 03:03
  • Lecture 199. How to present to executives 05:27
  • Lecture 200. The truth is not always pretty 02:45
  • Lecture 201. Passion and the Wow-factor 01:59
  • Lecture 202. Bonus: my full presentation | LIVE 2015 16:20
  • Lecture 203. Bonus: links to other examples of good storytelling 00:10
6 lectures 1 hour
  • Lecture 204. Advanced Data Mining with Tableau: Visualising Credit Score & Tenure 05:44
  • Lecture 205. Advanced Data Mining with Tableau: Chi-Squared Test for Country 04:18
  • Lecture 206. ETL Error Handling (Phases 1 and 2) 19:51
  • Lecture 207. ETL Error Handling "Vehicle Service" Project (Part 1 of 3) 19:09
  • Lecture 208. ETL Error Handling "Vehicle Service" Project (Part 2 of 3) 10:41
  • Lecture 209. ETL Error Handling "Vehicle Service" Project (Part 3 of 3) 14:34
1 lectures 0 mins
  • Lecture 210. ***YOUR SPECIAL BONUS*** 02:28
Tableau 10 A-Z: Hands-On Tableau Training For Data Science!
66 lectures
7.5 hours
All Levels

Learn data visualization through Tableau 10 and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks.

You'll learn all of the features in Tableau that allow you to explore, experiment with, fix, prepare, and present data easily, quickly, and beautifully.

Use Tableau to Analyze and Visualize Data So You Can Respond Accordingly

  • Connect Tableau to a Variety of Datasets
  • Analyze, Blend, Join, and Calculate Data
  • Visualize Data in the Form of Various Charts, Plots, and Maps

Convert Raw Data Into Compelling Data Visualizations Using Tableau 10

Because every module of this course is independent, you can start in whatever section you wish, and you can do as much or as little as you like.

Each section provides a new data set and exercises that will challenge you so you can learn by immediately applying what you're learning.

Content is updated as new versions of Tableau are released. You can always return to the course to further hone your skills, while you stay ahead of the competition.

Contents and Overview

This course begins with Tableau basics. You will navigate the software, connect it to a data file, and export a worksheet, so even beginners will feel completely at ease.

To be able to find trends in your data and make accurate forecasts, you'll learn how to work with data extracts and timeseries.

Also, to make data easier to digest, you'll tackle how to use aggregations to summarize information. You will also use granularity to ensure accurate calculations.

In order to begin visualizing data, you'll cover how to create various charts, maps, scatterplots, and interactive dashboards for each of your projects.

You'll even learn when it's best to join or blend data in order to work with and present information from multiple sources.

Finally, you'll cover the latest and most advanced features of data preparation in Tableau 10, where you will create table calculations, treemap charts, and storylines.

By the time you complete this course, you'll be a highly proficient Tableau user. You will be using your skills as a data scientist to extract knowledge from data so you can analyze and visualize complex questions with ease.

You'll be fully prepared to collect, examine, and present data for any purpose, whether you're working with scientific data or you want to make forecasts about buying trends to increase profits.

Kirill Eremenko, Data Scientist & Forex Systems Expert

My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly - personal freedom.

In my other life I am a Data Scientist - I study numbers to analyze patterns in business processes and human behaviour... Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading :) EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex - Love It All!

Summary

To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

SuperDataScience Team, Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

Sections
4 lectures 16 mins
8 lectures 1 hour 1 quiz
  • Lecture 5. The Business Challenge - Who Gets the Annual Bonus? 03:43
  • Lecture 6. Connecting Tableau to a Data File - CSV File 06:10
  • Lecture 7. Navigating Tableau 08:54
  • Lecture 8. Creating Calculated Fields 06:27
  • Lecture 9. Adding Colors 07:47
  • Lecture 10. Adding Labels and Formatting 11:09
  • Lecture 11. Exporting Your Worksheet 06:26
  • Lecture 12. Get The Viz 01:13
  • Quiz 1. Tableau Basics 5 questions
6 lectures 1 hour 1 quiz
  • Lecture 13. Section Intro 02:15
  • Lecture 14. Working with Data Extracts in Tableau 08:07
  • Lecture 15. Working with Time Series 09:42
  • Lecture 16. Understanding Aggregation, Granularity, and Level of Detail 09:29
  • Lecture 17. Creating an Area Chart & Learning About Highlighting Free Preview 08:55
  • Lecture 18. Adding a Filter and Quick Filter 08:58
  • Quiz 2. Timeseries, Aggregation, and Filters 5 questions
7 lectures 1 hour 1 quiz
  • Lecture 19. Section Intro 01:14
  • Lecture 20. Joining Data in Tableau 10:01
  • Lecture 21. Creating a Map, Working with Hierarchies 11:09
  • Lecture 22. Creating a Scatter Plot, Applying Filters to Multiple Worksheets 10:24
  • Lecture 23. Let's Create our First Dashboard! 07:25
  • Lecture 24. Adding an Interactive Action - Filter 08:29
  • Lecture 25. Adding an Interactive Action - Highlighting 09:33
  • Quiz 3. Maps, Scatterplots, and Your First Dashboard 5 questions
9 lectures 1.5 hours 1 quiz
  • Lecture 26. Section Intro 04:26
  • Lecture 27. Understanding how LEFT, RIGHT, INNER, and OUTER Joins Work 06:26
  • Lecture 28. Joins With Duplicate Values 02:41
  • Lecture 29. Joining on Multiple Fields 05:31
  • Lecture 30. The Showdown: Joining Data v.s. Blending Data in Tableau 10:57
  • Lecture 31. Data Blending in Tableau 15:25
  • Lecture 32. Dual Axis Chart Free Preview 12:16
  • Lecture 33. Creating Calculated Fields in a Blend (Advanced Topic) 13:24
  • Lecture 34. Section Recap 07:11
  • Quiz 4. Joining and Blending Data, PLUS: Dual Axis Charts 7 questions
11 lectures 1 hour 1 quiz
  • Lecture 35. Section Intro 00:56
  • Lecture 36. Downloading the Dataset and Connecting to Tableau 04:02
  • Lecture 37. Mapping: how to Set Geographical Roles 06:38
  • Lecture 38. Creating Table Calculations for Gender 05:09
  • Lecture 39. Creating Bins and Distributions For Age 06:38
  • Lecture 40. Leveraging the Power of Parameters 08:00
  • Lecture 41. How to Create a Tree Map Chart 02:21
  • Lecture 42. Creating a Customer Segmentation Dashboard 06:11
  • Lecture 43. Advanced Dashboard Interactivity 04:21
  • Lecture 44. Analyzing the Customer Segmentation Dashboard * Free Preview 10:57
  • Lecture 45. Creating a Storyline 12:45
  • Quiz 5. Table Calculations, Advanced Dashboards, Storytelling 7 questions
7 lectures 40 mins 1 quiz
  • Lecture 46. Section Intro 02:11
  • Lecture 47. What Format Your Data Should Be In 05:19
  • Lecture 48. Data Interpreter 11:33
  • Lecture 49. Pivot Free Preview 02:47
  • Lecture 50. Splitting a Column into Multiple Columns 03:21
  • Lecture 51. MetaData Grid 07:04
  • Lecture 52. Fixing Geographical Data Errors in Tableau 07:25
  • Quiz 6. Advanced Data Preparation 3 questions
12 lectures 1.5 hours 1 quiz
  • Lecture 53. Section Intro Free Preview 06:38
  • Lecture 54. The Challenge: Startup Expansion Analytics 08:13
  • Lecture 55. Custom Territories Via Groups 09:17
  • Lecture 56. Custom Territories Via Geographic Roles 04:31
  • Lecture 57. Adding a Highlighter 05:48
  • Lecture 58. Clustering In Tableau Free Preview 07:10
  • Lecture 59. Cross-Database Joins 08:22
  • Lecture 60. Modeling With Clusters 12:20
  • Lecture 61. Saving Your Clusters 03:43
  • Lecture 62. New Design Features 04:23
  • Lecture 63. New Mobile Features 04:19
  • Lecture 64. Section Recap 05:19
  • Quiz 7. What's new in Tableau 10 5 questions
2 lectures 9 mins
  • Lecture 65. Course overview and roadmap download 08:51
  • Lecture 66. ***YOUR SPECIAL BONUS*** 01:59
Data Science and Machine Learning with Python - Hands On!
71 lectures
9 hours
All Levels

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning and data mining techniques real employers are looking for, including:

  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multivariate Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests


...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. I think you'll enjoy it!




Sundog Education by Frank Kane, Training the World in Big Data and Machine Learning

Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. 

Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Frank Kane, Founder, Sundog Education

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computingdata mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Sections
6 lectures 40 mins
  • Lecture 1. Introduction Free Preview

    What to expect in this course, who it's for, and the general format we'll follow.

    02:44
  • Lecture 2. [Activity] Getting What You Need

    We'll show you where to download the scripts and sample data used in this course, and where to put it.

    02:36
  • Lecture 3. [Activity] Installing Enthought Canopy

    We'll install our Python 3.5 environment, Enthought Canopy, and install the Python libraries and packages we'll need for this course. When we're done, we'll do a quick test of running a real Python notebook!

    05:10
  • Lecture 4. Python Basics, Part 1

    In a crash course on Python and what's different about it, we'll cover the importance of whitespace in Python scripts, how to import Python modules, and Python data structures including lists, tuples, and dictionaries.

    15:58
  • Lecture 5. [Activity] Python Basics, Part 2 Free Preview

    In part 2 of our Python crash course, we'll cover functions, boolean expressions, and looping constructs in Python.

    09:41
  • Lecture 6. Running Python Scripts

    This course presents Python examples in the form of iPython Notebooks, but we'll cover the other ways to run Python code: interactively from the Python shell, or running stand-alone Python script files.

    03:55
12 lectures 1.5 hours
  • Lecture 7. Types of Data Free Preview

    We cover the differences between continuous and discrete numerical data, categorical data, and ordinal data.

    06:58
  • Lecture 8. Mean, Median, Mode

    A refresher on mean, median, and mode - and when it's appropriate to use each.

    05:26
  • Lecture 9. [Activity] Using mean, median, and mode in Python

    We'll use mean, median, and mode in some real Python code, and set you loose to write some code of your own.

    08:30
  • Lecture 10. [Activity] Variation and Standard Deviation Free Preview

    We'll cover how to compute the variation and standard deviation of a data distribution, and how to do it using some examples in Python.

    11:12
  • Lecture 11. Probability Density Function; Probability Mass Function

    Introducing the concepts of probability density functions (PDF's) and probability mass functions (PMF's).

    03:27
  • Lecture 12. Common Data Distributions

    We'll show examples of continuous, normal, exponential, binomial, and poisson distributions using iPython.

    07:45
  • Lecture 13. [Activity] Percentiles and Moments

    We'll look at some examples of percentiles and quartiles in data distributions, and then move on to the concept of the first four moments of data sets.

    12:33
  • Lecture 14. [Activity] A Crash Course in matplotlib

    An overview of different tricks in matplotlib for creating graphs of your data, using different graph types and styles.

    13:46
  • Lecture 15. [Activity] Covariance and Correlation

    The concepts of covariance and correlation used to look for relationships between different sets of attributes, and some examples in Python.

    11:31
  • Lecture 16. [Exercise] Conditional Probability

    We cover the concepts and equations behind conditional probability, and use it to try and find a relationship between age and purchases in some fabricated data using Python.

    10:16
  • Lecture 17. Exercise Solution: Conditional Probability of Purchase by Age

    Here we'll go over my solution to the exercise I challenged you with in the previous lecture - changing our fabricated data to have no real correlation between ages and purchases, and seeing if you can detect that using conditional probability.

    02:18
  • Lecture 18. Bayes' Theorem Free Preview

    An overview of Bayes' Theorem, and an example of using it to uncover misleading statistics surrounding the accuracy of drug testing.

    05:23
4 lectures 34 mins
  • Lecture 19. [Activity] Linear Regression Free Preview

    We introduce the concept of linear regression and how it works, and use it to fit a line to some sample data using Python.

    11:01
  • Lecture 20. [Activity] Polynomial Regression Free Preview

    We cover the concepts of polynomial regression, and use it to fit a more complex page speed - purchase relationship in Python.

    08:04
  • Lecture 21. [Activity] Multivariate Regression, and Predicting Car Prices

    Multivariate models let us predict some value given more than one attribute. We cover the concept, then use it to build a model in Python to predict car prices based on their number of doors, mileage, and number of cylinders. We'll also get our first look at the statsmodels library in Python.

    09:52
  • Lecture 22. Multi-Level Models

    We'll just cover the concept of multi-level modeling, as it is a very advanced topic. But you'll get the ideas and challenges behind it.

    04:36
13 lectures 1.5 hours
  • Lecture 23. Supervised vs. Unsupervised Learning, and Train/Test

    The concepts of supervised and unsupervised machine learning, and how to evaluate the ability of a machine learning model to predict new values using the train/test technique.

    08:57
  • Lecture 24. [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression

    We'll apply train test to a real example using Python.

    05:47
  • Lecture 25. Bayesian Methods: Concepts

    We'll introduce the concept of Naive Bayes and how we might apply it to the problem of building a spam classifier.

    03:59
  • Lecture 26. [Activity] Implementing a Spam Classifier with Naive Bayes Free Preview

    We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!

    08:05
  • Lecture 27. K-Means Clustering

    K-Means is a way to identify things that are similar to each other. It's a case of unsupervised learning, which could result in clusters you never expected!

    07:23
  • Lecture 28. [Activity] Clustering people based on income and age

    We'll apply K-Means clustering to find interesting groupings of people based on their age and income.

    05:14
  • Lecture 29. Measuring Entropy

    Entropy is a measure of the disorder in a data set - we'll learn what that means, and how to compute it mathematically.

    03:09
  • Lecture 30. [Activity] Install GraphViz

    In order to run the next lecture on decision trees, you'll need some software called "GraphViz" installed. Here's how.

    00:43
  • Lecture 31. Decision Trees: Concepts Free Preview

    Decision trees can automatically create a flow chart for making some decision, based on machine learning! Let's learn how they work.

    08:43
  • Lecture 32. [Activity] Decision Trees: Predicting Hiring Decisions

    We'll create a decision tree and an entire "random forest" to predict hiring decisions for job candidates.

    09:47
  • Lecture 33. Ensemble Learning

    Random Forests was an example of ensemble learning; we'll cover over techniques for combining the results of many models to create a better result than any one could produce on its own.

    05:59
  • Lecture 34. Support Vector Machines (SVM) Overview

    Support Vector Machines are an advanced technique for classifying data that has multiple features. It treats those features as dimensions, and partitions this higher-dimensional space using "support vectors."

    04:27
  • Lecture 35. [Activity] Using SVM to cluster people using scikit-learn

    We'll use scikit-learn to easily classify people using a C-Support Vector Classifier.

    05:36
6 lectures 1 hour
  • Lecture 36. User-Based Collaborative Filtering Free Preview

    One way to recommend items is to look for other people similar to you based on their behavior, and recommend stuff they liked that you haven't seen yet.

    07:57
  • Lecture 37. Item-Based Collaborative Filtering

    The shortcomings of user-based collaborative filtering can be solved by flipping it on its head, and instead looking at relationships between items instead of relationships between people.

    08:15
  • Lecture 38. [Activity] Finding Movie Similarities

    We'll use the real-world MovieLens data set of movie ratings to take a first crack at finding movies that are similar to each other, which is the first step in item-based collaborative filtering.

    09:08
  • Lecture 39. [Activity] Improving the Results of Movie Similarities

    Our initial results for movies similar to Star Wars weren't very good. Let's figure out why, and fix it.

    07:59
  • Lecture 40. [Activity] Making Movie Recommendations to People Free Preview

    We'll implement a complete item-based collaborative filtering system that uses real-world movie ratings data to recommend movies to any user.

    10:22
  • Lecture 41. [Exercise] Improve the recommender's results

    As a student exercise, try some of my ideas - or some ideas of your own - to make the results of our item-based collaborative filter even better.

    05:29
6 lectures 1 hour
  • Lecture 42. K-Nearest-Neighbors: Concepts

    KNN is a very simple supervised machine learning technique; we'll quickly cover the concept here.

    03:44
  • Lecture 43. [Activity] Using KNN to predict a rating for a movie

    We'll use the simple KNN technique and apply it to a more complicated problem: finding the most similar movies to a given movie just given its genre and rating information, and then using those "nearest neighbors" to predict the movie's rating.

    12:29
  • Lecture 44. Dimensionality Reduction; Principal Component Analysis

    Data that includes many features or many different vectors can be thought of as having many dimensions. Often it's useful to reduce those dimensions down to something more easily visualized, for compression, or to just distill the most important information from a data set (that is, information that contributes the most to the data's variance.) Principal Component Analysis and Singular Value Decomposition do that.

    05:44
  • Lecture 45. [Activity] PCA Example with the Iris data set

    We'll use sckikit-learn's built-in PCA system to reduce the 4-dimensions Iris data set down to 2 dimensions, while still preserving most of its variance.

    09:05
  • Lecture 46. Data Warehousing Overview: ETL and ELT

    Cloud-based data storage and analysis systems like Hadoop, Hive, Spark, and MapReduce are turning the field of data warehousing on its head. Instead of extracting, transforming, and then loading data into a data warehouse, the transformation step is now more efficiently done using a cluster after it's already been loaded. With computing and storage resources so cheap, this new approach now makes sense.

    09:05
  • Lecture 47. Reinforcement Learning Free Preview

    We'll describe the concept of reinforcement learning - including Markov Decision Processes, Q-Learning, and Dynamic Programming - all using a simple example of developing an intelligent Pac-Man.

    12:44
6 lectures 1 hour
  • Lecture 48. Bias/Variance Tradeoff

    Bias and Variance both contribute to overall error; understand these components of error and how they relate to each other.

    06:15
  • Lecture 49. [Activity] K-Fold Cross-Validation to avoid overfitting

    We'll introduce the concept of K-Fold Cross-Validation to make train/test even more robust, and apply it to a real model.

    10:55
  • Lecture 50. Data Cleaning and Normalization Free Preview

    Cleaning your raw input data is often the most important, and time-consuming, part of your job as a data scientist!

    07:10
  • Lecture 51. [Activity] Cleaning web log data

    In this example, we'll try to find the top-viewed web pages on a web site - and see how much data pollution makes that into a very difficult task!

    10:56
  • Lecture 52. Normalizing numerical data

    A brief reminder: some models require input data to be normalized, or within the same range, of each other. Always read the documentation on the techniques you are using.

    03:22
  • Lecture 53. [Activity] Detecting outliers

    A review of how outliers can affect your results, and how to identify and deal with them in a principled manner.

    07:00
10 lectures 1.5 hours
  • Lecture 54. [Activity] Installing Spark - Part 1

    We'll present an overview of the steps needed to install Apache Spark on your desktop in standalone mode, and get started by getting a Java Development Kit installed on your system.

    07:02
  • Lecture 55. [Activity] Installing Spark - Part 2

    We'll install Spark itself, along with all the associated environment variables and ancillary files and settings needed for it to function properly.

    13:29
  • Lecture 56. Spark Introduction

    A high-level overview of Apache Spark, what it is, and how it works.

    09:10
  • Lecture 57. Spark and the Resilient Distributed Dataset (RDD)

    We'll go in more depth on the core of Spark - the RDD object, and what you can do with it.

    11:42
  • Lecture 58. Introducing MLLib

    A quick overview of MLLib's capabilities, and the new data types it introduces to Spark.

    05:09
  • Lecture 59. [Activity] Decision Trees in Spark Free Preview

    We'll take the same problem for our earlier Decision Tree lecture - predicting hiring decisions for job candidates - but implement it using Spark and MLLib!

    16:00
  • Lecture 60. [Activity] K-Means Clustering in Spark

    We'll take the same example of clustering people by age and income from our earlier K-Means lecture - but solve it in Spark!

    11:07
  • Lecture 61. TF / IDF Free Preview

    We'll introduce the concept of TF-IDF (Term Frequency / Inverse Document Frequency) and how it applies to search problems, in preparation for using it with MLLib.

    06:44
  • Lecture 62. [Activity] Searching Wikipedia with Spark

    Let's use TF-IDF, Spark, and MLLib to create a rudimentary search engine for real Wikipedia pages!

    08:11
  • Lecture 63. [Activity] Using the Spark 2.0 DataFrame API for MLLib

    Spark 2.0 introduced a new API for MLLib based on DataFrame objects; we'll look at an example of using this to create and use a linear regression model.

    07:57
5 lectures 33 mins
  • Lecture 64. A/B Testing Concepts

    Running controlled experiments on your website usually involves a technique called the A/B test. We'll learn how they work.

    08:23
  • Lecture 65. T-Tests and P-Values

    How to determine significance of an A/B tests results, and measure the probability of the results being just from random chance, using T-Tests, the T-statistic, and the P-value.

    05:59
  • Lecture 66. [Activity] Hands-on With T-Tests

    We'll fabricate A/B test data from several scenarios, and measure the T-statistic and P-Value for each using Python.

    06:04
  • Lecture 67. Determining How Long to Run an Experiment

    Some A/B tests just don't affect customer behavior one way or another. How do you know how long to let an experiment run for before giving up?

    03:24
  • Lecture 68. A/B Test Gotchas Free Preview

    There are many limitations associated with running short-term A/B tests - novelty effects, seasonal effects, and more can lead you to the wrong decisions. We'll discuss the forces that may result in misleading A/B test results so you can watch out for them.

    09:26
3 lectures 4 mins
  • Lecture 69. More to Explore

    Where to go from here - recommendations for books, websites, and career advice to get you into the data science job you want.

    02:59
  • Lecture 70. Don't Forget to Leave a Rating!

    If you enjoyed this course, please leave a star rating for it!

    00:21
  • Lecture 71. Bonus Lecture: Discounts on my Spark and MapReduce courses!

    Let's stay in touch! Head to my website for discounts on my other courses, and to follow me on social media.

    01:06
R Programming A-Z™: R For Data Science With Real Exercises!
76 lectures
10.5 hours
All Levels

Learn R Programming by doing!

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!

I can't wait to see you in class,

Sincerely,

Kirill Eremenko

Kirill Eremenko, Data Scientist & Forex Systems Expert

My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly - personal freedom.

In my other life I am a Data Scientist - I study numbers to analyze patterns in business processes and human behaviour... Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading :) EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex - Love It All!

Summary

To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

SuperDataScience Team, Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

Sections
4 lectures 21 mins
10 lectures 1.5 hours 1 quiz
  • Lecture 5. Welcome to this section. This is what you will learn! 01:11
  • Lecture 6. Types of variables 10:10
  • Lecture 7. Using Variables 10:23
  • Lecture 8. Logical Variables and Operators Free Preview 07:42
  • Lecture 9. The "While" Loop 07:33
  • Lecture 10. Using the console 04:04
  • Lecture 11. The "For" Loop 05:29
  • Lecture 12. The "If" statement 12:15
  • Lecture 13. Section Recap 05:22
  • Lecture 14. HOMEWORK: Law of Large Numbers 15:13
  • Quiz 1. Core Programming Principles 5 questions
10 lectures 1.5 hours 1 quiz
  • Lecture 15. Welcome to this section. This is what you will learn! 01:04
  • Lecture 16. What is a Vector? 04:17
  • Lecture 17. Let's create some vectors 13:12
  • Lecture 18. Using the [] brackets 10:31
  • Lecture 19. Vectorized operations Free Preview 05:49
  • Lecture 20. The power of vectorized operations 16:22
  • Lecture 21. Functions in R 17:58
  • Lecture 22. Packages in R 09:19
  • Lecture 23. Section Recap 06:19
  • Lecture 24. HOMEWORK: Financial Statement Analysis 07:42
  • Quiz 2. Fundamentals of R 5 questions
14 lectures 2 hours 1 quiz
  • Lecture 25. Welcome to this section. This is what you will learn! 01:10
  • Lecture 26. Project Brief: Basketball Trends 09:22
  • Lecture 27. Matrices 06:30
  • Lecture 28. Building Your First Matrix 13:41
  • Lecture 29. Naming Dimensions 03:34
  • Lecture 30. Colnames() and Rownames() 10:02
  • Lecture 31. Matrix Operations 06:12
  • Lecture 32. Visualizing With Matplot() 11:42
  • Lecture 33. Subsetting 09:20
  • Lecture 34. Visualizing Subsets 04:13
  • Lecture 35. Creating Your First Function 08:47
  • Lecture 36. Basketball Insights Free Preview 12:38
  • Lecture 37. Section Recap 06:04
  • Lecture 38. HOMEWORK: Basketball Free Throws 07:13
  • Quiz 3. Matrices 5 questions
14 lectures 1.5 hours 1 quiz
  • Lecture 39. Welcome to this section. This is what you will learn! 01:47
  • Lecture 40. Project Brief: Demographic Analysis 04:16
  • Lecture 41. Importing data into R 05:52
  • Lecture 42. Exploring your dataset 10:18
  • Lecture 43. Using the $ sign 06:23
  • Lecture 44. Basic operations with a Data Frame 09:47
  • Lecture 45. Filtering a Data Frame 09:04
  • Lecture 46. Introduction to qplot 09:09
  • Lecture 47. Visualizing With Qplot: Part I Free Preview 06:22
  • Lecture 48. Building Dataframes 10:02
  • Lecture 49. Merging Data Frames 07:38
  • Lecture 50. Visualizing With Qplot: Part II 06:50
  • Lecture 51. Section Recap 07:19
  • Lecture 52. HOMEWORK: World Trends 06:16
  • Quiz 4. Data Frames 5 questions
16 lectures 2 hours 1 quiz
  • Lecture 53. Welcome to this section. This is what you will learn! 01:23
  • Lecture 54. Project Brief: Movie Ratings 04:02
  • Lecture 55. Grammar Of Graphics - GGPlot2 Free Preview 11:26
  • Lecture 56. What is a Factor? 07:13
  • Lecture 57. Aesthetics 06:54
  • Lecture 58. Plotting With Layers 05:18
  • Lecture 59. Overriding Aesthetics 07:49
  • Lecture 60. Mapping vs Setting 08:09
  • Lecture 61. Histograms and Density Charts 07:08
  • Lecture 62. Starting Layer Tips 08:41
  • Lecture 63. Statistical Transformations 10:38
  • Lecture 64. Using Facets 09:30
  • Lecture 65. Coordinates 10:28
  • Lecture 66. Perfecting By Adding Themes 11:04
  • Lecture 67. Section Recap 09:50
  • Lecture 68. HOMEWORK: Movie Domestic % Gross 07:05
  • Quiz 5. Advanced Visualization With GGPlot2 5 questions
6 lectures 1.5 hours
  • Lecture 69. Homework Solution Section 2: Law Of Large Numbers 12:01
  • Lecture 70. Homework Solution Section 3: Financial Statement Analysis 18:35
  • Lecture 71. Homework Solution Section 4: Basketball Free Throws 16:11
  • Lecture 72. Homework Solution Section 5: World Trends 16:30
  • Lecture 73. Homework Solution Section 6: Movie Domestic % Gross (Part 1) 11:41
  • Lecture 74. Homework Solution Section 6: Movie Domestic % Gross (Part 2) 11:09
2 lectures 14 mins
  • Lecture 75. BoxPlots 13:32
  • Lecture 76. **YOUR SPECIAL BONUS** 02:05
The Complete SQL Bootcamp
88 lectures
8.5 hours
All Levels

Learn how to use SQL quickly and effectively with this course!

You'll learn how to read and write complex queries to a database using one of the most in demand skills - PostgreSQL. These skills are also applicable to any other major SQL database, such as MySQL, Microsoft SQL Server, Amazon Redshift, Oracle, and much more.

Learning SQL is one of the fastest ways to improve your career prospects as it is one of the most in demand tech skills! In this course you'll learn quickly and receive challenges and tests along the way to improve your understanding!

Check out the free preview videos for more information!

Jose Portilla, Data Scientist

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions.

Sections
7 lectures 26 mins
5 lectures 17 mins
  • Lecture 8. Mac OS X - Quick Note

    Quick note regarding mac setup.

    00:26
  • Lecture 9. Mac OS Installation 05:56
  • Lecture 10. Mac Sample Database Download 00:52
  • Lecture 11. Mac Database Set-up 10:42
  • Lecture 12. Review of Set-up 00:35
4 lectures 24 mins
  • Lecture 13. Windows - Quick Note

    Quick Note for Windows

    00:36
  • Lecture 14. Windows Installation

    Overview of windows installation.

    04:07
  • Lecture 15. Sample Database Download

    Sample Database to Download

    00:32
  • Lecture 16. Windows Command Line Installation Lecture 19:45
7 lectures 17 mins
  • Lecture 17. Databases and Tables Overview 01:12
  • Lecture 18. Creating and Restoring a Database 06:21
  • Lecture 19. Overview of Challenges! Free Preview 01:36
  • Lecture 20. Challenge: Restore a Database!

    Challenge to test your skills!

    00:42
  • Lecture 21. Restoring a Table Schema 06:13
  • Lecture 22. Challenge: Restore a Table Schema! 00:43
  • Lecture 23. Review of Section 00:26
16 lectures 1.5 hours
  • Lecture 24. SQL Cheat Sheet

    A cheat sheet for you!

    00:18
  • Lecture 25. SQL Statement Fundamentals 00:42
  • Lecture 26. SELECT Statement 10:48
  • Lecture 27. Challenge: SELECT task. 02:45
  • Lecture 28. SELECT DISTINCT 05:46
  • Lecture 29. Challenge: SELECT DISTINCT 02:05
  • Lecture 30. SELECT WHERE 14:03
  • Lecture 31. Challenge: SELECT WHERE 03:45
  • Lecture 32. COUNT Free Preview 05:00
  • Lecture 33. LIMIT 02:59
  • Lecture 34. ORDER BY 10:05
  • Lecture 35. Challenge: ORDER BY 03:38
  • Lecture 36. BETWEEN 08:44
  • Lecture 37. IN 08:27
  • Lecture 38. LIKE 10:24
  • Lecture 39. General Challenge 1 09:54
6 lectures 1 hour
  • Lecture 40. MIN MAX SUM and AVG Free Preview 08:06
  • Lecture 41. GROUP BY 18:10
  • Lecture 42. Challenge:GROUP BY 12:15
  • Lecture 43. HAVING 10:17
  • Lecture 44. Quick Note

    Quick correction for the next lecture.

    00:12
  • Lecture 45. Challenge:HAVING 03:43
3 lectures 1 min
  • Lecture 46. Overview of Assessment Test 1 01:08
  • Lecture 47. Assessment Test 1 00:21
  • Lecture 48. Solutions to Assessment Test 1 00:14
8 lectures 1 hour
  • Lecture 49. Overview of JOINS 00:21
  • Lecture 50. AS Statement 03:37