Table of Contents
SQL Habit has 2 main parts – course aka "business simulator" and practice exercises. Practice exercises are standalone and based on various datasets, where the course is based on a story of a fictional startup company called Bindle.
Bindle is a subscription service for reading books online. The course starts with a launch of Bindle's website and together with the founders will learn how to analyze website performance, marketing performace, dive into analytics of web and mobile apps and much more.
The course covers the first year of Bindle. Each chapters covers one month focused on a specific topic. During each chapter we'll see many real life example in the life of Bindle, learn how data helps to take decisions and solve them. The most important – we'll learn all nuances of how data is recorded and stored in Bindle's data warehouse.
The course requires no prio knowledge of SQL. It'll take you from absolute 0 to Advanced with real life examples, lessons and exercises.
Introduction
You’ll learn how SQL Habit works and how to get the most out of the course. A quick chapter to set you up for an efficient and effective learning 
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1. Introduction to SQL Habit courseStart
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2. Beyond SQL Habit course: Practice, Chart and Mock InterviewsStart
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3. Supercharge your learning with AI CoachStart
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4. A brief history of SQL and its applicationsStart
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5. How data is stored inside SQL databaseStart
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6. Query anatomy: SELECT and FROMStart
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7. Query anatomy: CommentsStart
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8. Keyboard shortcuts for SQL EditorStart
Fundamentals of Data Analysis
Together with Bindle’s founders you’ll learn SQL basics and how it helps to segment users (by country, time etc), work with basic timelines 
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9. Selecting all records from the tableStart
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10. Sorting recordsStart
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11. Limiting number of records produced by queryStart
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12. Filtering records from a tableStart
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13. Filtering by countryStart
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14. Inspecting a specific user recordStart
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15. Count signups from countryStart
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16. Count signups from the USStart
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17. Vanity metrics: total number of signupsStart
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18. Filtering records by timeStart
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19. Inclusive and non-inclusive comparisonStart
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20. Combining filtersStart
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21. Count signups within a time rangeStart
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22. Count total number of customersStart
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23. Count customers per countryStart
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24. Better filtering records with rangesStart
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25. Number of customers in a monthStart
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26. Using aliases for columnsStart
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27. If/else logic via CASE statementStart
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28. Calculating purchase rate. Part 1Start
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29. Calculating purchase rate. Part 2Start
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30. Integer and float numbers in SQLStart
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31. Calculating purchase rate per countryStart
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32. Counting number of customers per country in a monthStart
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33. Count numbers of published booksStart
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34. Subqueries in SQLStart
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35. Relational model of dataStart
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36. Relation types: one to one, one to many, many to manyStart
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37. Relational model diagramsStart
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38. Comparing dates and datetimesStart
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39. Type-castingStart
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40. Functions in SQLStart
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41. Counting books users started to read in the first weekStart
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42. Measuring users activityStart
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43. Filtering NULL valuesStart
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44. Analyzing feature: counting users who filled their profileStart
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45. Feature acceptance criteria: percentage of users who uploaded avatarStart
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46. Counting in ranges: user ageStart
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47. Fundamentals of SQL recapStart
Running marketing
This chapter focuses on SQL for marketing: what data can help us measure an effectiveness of our campaigns, how to group data, work with time lines and cohorts 
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48. Intro to performance marketingStart
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49. Filtering with OR operatorStart
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50. Counting signups from English speaking countriesStart
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51. Counting signups from DACH regionStart
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52. Counting fiction books in the libraryStart
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53. Listing distinct book categories with DISTINCTStart
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54. Counting unique valuesStart
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55. Count user signup countriesStart
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56. Filtering string values by matchingStart
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57. Matching string values regardless of caseStart
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58. First marketing campaign: counting signups from a custom domainStart
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59. Identifying email domain with SPLIT_PARTStart
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60. Count unique email domainsStart
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61. Сounting signups per country: intro to GROUP BYStart
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62. Order of SQL commands execution in a queryStart
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63. Practicing queries on virtual datasets with UNIONStart
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64. Set operators: UNION and UNION ALLStart
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65. Country with the highest number of signupsStart
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66. Calculate signups per day, per countryStart
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67. Referencing columns by numbersStart
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68. Most popular email domainStart
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69. The smallest book categoryStart
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70. Daily and hourly timelinesStart
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71. Monthly and yearly timelinesStart
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72. The month with the highest number of signupsStart
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73. Counting number users per age groupStart
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74. Biggest age group in USAStart
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75. Introduction to UTM parameters and marketing attributionStart
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76. Calculating number of signups per campaignStart
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77. Calculating number of signups from Twitter adsStart
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78. The biggest marketing campaignStart
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79. Organic vs paid signupsStart
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80. Count organic signups in a monthStart
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81. Introduction to referral programsStart
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82. Identifying power referral usersStart
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83. Best performing Twitter ad messageStart
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84. Marketing metrics: ROIStart
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85. Import of marketing dataStart
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86. Calculating marketing spend per channelStart
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87. Calculating marketing spend per campaignStart
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88. Finding the biggest marketing channelStart
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89. Calculate purchase rate per campaignStart
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90. Calculate purchase rate for organic trafficStart
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91. Marketing data and SQL recapStart
Revenue analytics and unit economics
In this chapter we’ll dive into all things revenue: calculating gross revenue, revenue per country/time period. We’ll see how we can join marketing and revenue data to calculate ROI and business unit economics (revenue per user etc) 
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92. Calculating revenue per subscriptionStart
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93. Calculating gross revenue per monthStart
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94. Joining tables together with INNER JOINStart
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95. Working with joined tablesStart
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96. JOIN + Aggregate Functions =
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97. Order of SQL commands execution in a query with JOINsStart
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98. Identifying the most popular book in the catalogueStart
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99. LEFT JOIN 101Start
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100. Grouping and counting with LEFT JOINStart
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101. Calculating ROIStart
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102. Calculating marketing channel ROIStart
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103. Joining tables without conditions with CROSS JOINStart
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104. Calculating overall performance marketing ROIStart
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105. Intro to Unit EconomicsStart
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106. ARPU: Average Revenue Per UserStart
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107. Calculating ARPUStart
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108. Calculating ARPU per marketing campaignStart
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109. ARPPU: Average Revenue Per Paying UserStart
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110. Calculating ARPPU per countryStart
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111. Differentiating between past and current customersStart
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112. CPA: Cost per acquisitionStart
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113. Calculating CPA per marketing campaignStart
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114. Setting default values with COALESCEStart
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115. CPC: Cost Per ClickStart
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116. Detecting campaign with the highest CPCStart
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117. CAC: Customer Acquisition CostStart
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118. Calculating CAC per campaignStart
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119. Sorting records with NULL valuesStart
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120. Other types of JOINsStart
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121. Calculating revenue or other metric for the last day/week/monthStart
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122. Revenue analytics recapStart
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123. Revenue analytics recap. Part 2Start
Product Analytics. Part 1
Bindle launches a mobile app to allow users read books on the go. In this chapter we’ll learn funnel analysis for product flows (onboarding, purchase), retention metrics and more 
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124. Intro to pirate metrics AARRR: Acquisition, Activation, Retention, Revenue, ReferralStart
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125. Basics of funnel analysisStart
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126. Overview of web tracking or how Gооglе Anаlytics worksStart
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127. How pageviews are collected and storedStart
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128. Inspecting pageviews table. SCHEMAs in SQLStart
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129. Measuring traffic per device typeStart
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130. Web funnels. Part 1: Measuring traffic on a pageStart
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131. Measuring unique visitors on a pageStart
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132. Web funnels. Part 2: Funnel based on referer URLStart
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133. Calculating funnel step churn rateStart
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134. Listing traffic sources based on referer URLStart
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135. Identifying TOP traffic source for a landing pageStart
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136. How events are collected and storedStart
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137. Building web funnels based on eventsStart
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138. Multistep funnels with pageviews and eventsStart
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139. CTR of a “Signup” buttonStart
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140. Validation of signup events consistencyStart
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141. JSON column typesStart
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142. Querying JSON columnsStart
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143. Counting pageviews with AB-test variationStart
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144. Cleaning data: filtering out empty valuesStart
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145. Percentage of mobile traffic on the pageStart
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146. Signup rate per device typeStart
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147. Onboarding funnel analysis on the web. Part 1Start
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148. Onboarding funnel analysis on the web. Part 2Start
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149. Onboarding funnel analysis on the web. Part 3Start
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150. Measuring activation rateStart
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151. Email signups vs Social signupsStart
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152. Introduction to window functionsStart
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153. Deep dive into window functions. Part 1Start
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154. How much users signup via Facebook?Start
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155. Deep dive into window functions. Part 2: Grouping and counting with percentageStart
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156. Deep dive into window functions. Part 3: Cumulative aggregatesStart
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157. Popular window functionsStart
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158. Calculating marketing attribution. Part 1Start
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159. Calculating marketing attribution. Part 2Start
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160. Calculating marketing attribution. Part 3Start
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161. Product Analytics. Part 1 recapStart
Mobile attribution
You’ll learn about mobile attribution. How it works technically and how mixed attribution model works on web and mobile 
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162. Introduction to the second part of the courseStart
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163. Introduction to mobile appsStart
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164. Percentage of users with mobile appStart
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165. Recap: percentages calculationStart
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166. iPhone version distributionStart
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167. iOS version distributionStart
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168. Introduction to mobile attributionStart
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169. Intro to mobile attribution with AdjustStart
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170. Deep dive into mobile attribution data from AdjustStart
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171. Mobile attribution with AdjustStart
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172. Connecting web and mobile analyticsStart
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173. Click to install conversion for websiteStart
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174. Install to signup conversion for websiteStart
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175. Overall click to signup conversion for Adjust linksStart
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176. Measuring CTR of Adjust linksStart
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177. Mobile ad with the highest signup rateStart
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178. Detecting broken app version with mobile attribution dataStart
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179. Sorting by app’s semantic versionStart
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180. Mobile attribution recapStart
Product Analytics. Part 2
Second part of Product Analytics focuses on analytics for mobile apps, retention rate, churn rate and LTV
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181. Calculating release date of the app from dataStart
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182. Extracting release dates from dataStart
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183. Counting app releases per monthStart
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184. Introduction to mobile analyticsStart
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185. How to measure activation rateStart
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186. Calculating soft activation rateStart
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187. Activation rate per countryStart
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188. Measuring activation rate without analytics events. Part 1Start
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189. Measuring activation rate without analytics events. Part 2Start
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190. Retention rate basicsStart
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191. Calculating D7 retention rate for a daily cohortStart
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192. Aggregating analytics events into daily activityStart
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193. Calculating D1-D30 retention curve. Part 1Start
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194. Calculating D1-D30 retention curve. Part 2Start
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195. Calculating D1-D30 retention curve. Part 3Start
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196. Using helper table for date rangesStart
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197. Product Analytics. Part 2 recapStart
AB-tests analysis
It’s time for the new chapter at Bindle – running AB-tests. Just shipping features is not enough, AB-tests allow learning how and why changes in the product affect users behavior and happiness. This chapter talks about AB-tests analysis with SQL 
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198. Intro to AB-testingStart
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199. AB-testing vocabularyStart
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200. AB-test categorization. Part 1Start
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201. AB-test categorization. Part 2Start
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202. Building AB-test cohortsStart
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203. Calculating activation rate for AB-test variationStart
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204. Building basic AB-test funnelStart
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205. Using multiple metrics in AB-test analysisStart
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206. Calculating ARPU of AB-test variationStart
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207. Unit economics in AB-test analysisStart
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208. Purchased products of AB-test variationStart
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209. Retention in AB-test funnelStart
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210. Calculating uplift for AB-test variationsStart
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211. Looking at AB-test performance in timeStart
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212. Statistical significance, power and further reading on AB-testingStart
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213. AB-test analysis recapStart
Text analysis with SQL
In this chapter we’ll be learning about getting useful insights from text data using Pattern Matching, Regular Expressions; we’ll go over all String functions in SQL 
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214. Introduction to text analysis with SQLStart
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215. Deep dive into pattern matching with LIKEStart
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216. Case insensitive matching with ILIKEStart
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217. Counting number of signups from .biz domainsStart
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218. Percentage of profiles with a specific interestStart
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219. Percentage of profiles with multiple interestsStart
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220. Distinguishing empty strings and NULL valuesStart
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221. Introduction to regular expressionsStart
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222. Regex basicsStart
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223. Implementing LIKE with regexStart
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224. Selecting iPhone usersStart
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225. Regex syntax. Part 1Start
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226. Regex syntax. Part 2Start
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227. Regex for validating US postal codesStart
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228. Number of users with a valid US postal codeStart
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229. Monitoring marketing naming conventionStart
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230. Validating marketing naming conventionStart
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231. Regex and case sensitivity, implementing ILIKEStart
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232. Regex functionsStart
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233. iPhone version distribution via regexStart
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234. Parsing URL parameters with regexStart
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235. String functions. Part 1Start
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236. String functions. Part 2Start
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237. Books title length distributionStart
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238. Checking if text data is case sensitiveStart
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239. Chapter recapStart
Dashboards and alarms
This chapters talks about using SQL in BI tools, analytics dashboards and typical SQL queries behind them. We’ll also discuss automatic error detection using data and SQL. 
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240. Intro to reporting and alarms with SQLStart
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241. Monitoring metrics for the last day/week/month/yearStart
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242. Calculating revenue trendStart
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243. Timeline reportsStart
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244. Simple fraud detectionStart
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245. Timeline reports with trendsStart
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246. SQL alarms basicsStart
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247. SQL alarms with thresholdStart
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248. Notes on anomaly detectionStart
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249. Dashboards and alarms recapStart
SQL at work
This chapter is a collection of tips and tricks from Marketers, Product Managers and Data Analysts that will give you a head start on using SQL at work 
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250. Overview of SQL databases: MySQL, PostgreSQL, etcStart
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251. Choosing SQL client and connecting to a databaseStart
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252. Structuring filters for Ad Hoc analysisStart
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253. Queries formatting aka “developing your style”. Part 1Start
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254. Queries formatting aka “developing your style”. Part 2Start
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255. Random in SQLStart
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256. SQL at work recapStart
Manipulating data
In this chapter we’ll talk about everything we haven’t been talking yet – modifying data in the database. Inserting new rows, updating and deleting 
Appendix
This is SQL Habit’s storage room. It covers the aspects of SQL databases we haven’t discussed yet: rare JOIN types, set theory, lists of scalar, aggregate and window functions 
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265. FULL OUTER JOINStart
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266. Set theory basics and SQL operations: UNION, INTERSECT, EXCEPTStart
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267. Numeric functionsStart
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268. Date and time functionsStart
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269. String functionsStart
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270. Aggregate functionsStart
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271. Window functionsStart
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272. NULL functionsStart
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273. How to generate number or date sequencesStart