Q3. Get the Name, Category, Country, & Funding, and output as New

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0 to 1 in Data Science

Welcome to Databit

Welcome and Introductions - what is the course about and who are the teachers? (1:23)

Getting started with Mode Analytics

How to create an account on Mode Analytics (1:58)
How to work with SQL Editor & Python Notebook in Mode Analytics (2:33)

Startups Case Study through SQL

Startups Case Study Through SQL Intro
Q1. Understanding the Startups dataset (3:03)
Q2. Get the Name, Category, & Total Funding from data (1:54)
Q3. Get the Name, Category, Country, & Funding, and output as New Names (1:44)
Q4. Get all data from United States (2:07)
Q5. Find all companies which raised $1Bn+ (1:19)
Q6. Get all data for France and Netherlands (1:26)
Q7. Get data from all countries except United States (1:06)
Q8. Find companies in India which raised $100M+ (1:54)
Q9. Find all companies either in clean tech or in New Zealand (1:37)
Q10. Get all data for mid-stage funding (rounds 2-4) (1:36)
Q11. Output funding raised in millions (2:04)
Q12. Find total No. of Rows from the data (1:31)
Q13. Find the No. of Distinct Categories (1:41)
Q14. Find the overall funding raised by all companies (1:24)
Q15. What is the Maximum & Minimum funding raised (1:29)
Q16. Find total companies by each country (2:45)
Q18. Find total funding raised by each country - Numbered Aliasing (1:58)
Q19. Find total funding raised by each category and sort by lowest funding (2:32)
Q20. Find total funding raised by each country and sort by highest funding (2:07)
Q21. Find the Top 5 countries based on total funding raised (2:26)
Q22. Find length of company names and sort by highest (2:22)
Q23. Extract first 5 characters of company names from the 'mobile' category (2:49)
Q24. Standardize the category codes (Uppercase with no Underscore) (3:22)
Q25. Get all data where company name contains 'mobile' (3:08)
Q26. Get the company name and country in a single column (2:36)
Q27. Get the Year, Month, and Day of the founding date (2:36)
Q28. Get the Year, and Month of the founding date in Date Format (2:43)

Startups Case Study through Python

Startups Case Study Through Python Intro
P0_ Setting up the Python Notebook (2:07)
P1_ Understanding the Startups dataset (2:49)
P2_ Get the Name, Category, & Total Funding from data (3:14)
P3_ Get the Name, Category, Country, & Funding, and output as New Names (2:45)
P4_ Get all data from United States (1:57)
P5_ Find all companies which raised $1Bn+ (2:08)
P6_ Get all data for France and Netherlands (1:32)
P7_ Get data from all countries except United States (1:06)
P8_ Find companies in India which raised $100M+ (2:56)
P9_ Find all companies either in clean tech or in New Zealand (1:41)
P10_ Get all data for mid-stage funding (rounds 2-4) (1:30)
P11_ Output funding raised in millions (2:45)
P12_ Find total No. of Rows from the data (1:24)
P13_ Find the No. of Distinct Categories (2:02)
P14_ Find the overall funding raised by all companies (2:36)
P15_ What's the Maximum & Average funding raised (2:47)
P16_ Find total companies by each country (3:42)
P17_ Find total funding raised by each country (2:16)
P19_ Find total funding raised by each category and sort by lowest funding (2:42)
P20_ Find total funding raised by each country and sort by highest funding (1:56)
P21_ Find the Top 5 countries based on total funding raised (2:33)
P22_ Find length of company names and sort by highest (2:58)
P23_ Extract first 5 characters of company names from the 'mobile' category (2:28)
P24_ Standardize the category codes (Uppercase with no Underscore) (5:10)
P25_ Get all data where company name contains 'mobile' (2:46)
P26_ Get the company name and country in a single column (2:16)
P27_ Get the Year, Month, and Day of the founding date (3:39)

Ecommerce Case Study Through SQL

Ecommerce Case Study Through SQL Intro
Q1 - Understanding the Ecommerce dataset (1:22)
Q2 - How many total orders and units were placed_ (1:41)
Q3 - What is total Gross Merchandise Value (GMV) (1:32)
Q4 - What is the AOV (Average Order Value)_ (1:44)
Q5 - Find the total users and ARPU (Average Revenue per User)_ (1:35)
Q6 - Find the basket size (1:31)
Q7 - Which were the top 5 product categories by gmv? (1:42)
Q8 - Find the monthly trend for GMV (1:38)
Q9 - Calculate the Monthly active users (1:34)
Q10 - Find the percentage distribution of sales per hour (Method 1) (3:28)
Q11 - Find the percentage distribution of sales per hour (Method 2) (2:37)
Q12 - Find the monthly running total of GMV (2:40)

Ecommerce Case Study Through Python

Ecommerce Case Study Through Python Intro
P0 - Setting up Python Notebook & Getting the Dataset (1:01)
P1 - Understanding the Ecommerce dataset (1:07)
P2 - How many total orders and units were placed_ (2:07)
P3 - What is total Gross Merchandise Value (GMV) (2:01)
P4 - What is the AOV (Average Order Value)_ (1:44)
P5 - Find the total users and ARPU (Average Revenue per User)_ (1:23)
P6 - Find the basket size (1:45)
P7 - Which were the top 5 product categories by gmv? (2:38)
P8 - Find the monthly trend for GMV (2:34)
P9 - Calculate the Monthly active users (2:44)
P10 - Find the percentage distribution of sales per hour (3:38)
P11 - Find the monthly running total of GMV (3:05)

Crypto Case Study Through SQL

Crypto Case Study Through SQL Intro
Q1_ Understanding the Crypto dataset (1:27)
Q2_ Create a column with the market cap in $ Billion (1:52)
Q3_ What are the top 5 crypto currencies based on marketcap in Billions on 1st April 2022 (3:14)
Q4_ What is the Circulating Supply for the top 5 cryptos on 1st April 2022 (3:05)
Q5_ How many coins have a market cap of $100Mn+ (measured on 1st April) (1:48)
Q6_ Which is the most expensive crypto (measured on 1st April 2022) (1:32)
Q7_ What was the average price for Bitcoin in 2021 (2:03)
Q8_ What was the range (min, max) of price for Bitcoin in 2021 (1:51)
Q9_ What was the price volatility (standard deviation) Bitcoin in 2021 (1:25)
Q10_ What was the monthly average price for Bitcoin (2:03)
Q11_ What was the month-on-month change in average price for Bitcoin (3:45)

Crypto Case Study Through Python

Crypto Case Study Through SQL Python
P0. Setting up Python Notebook & Getting the Dataset (1:50)
P1_ Understanding the Crypto dataset (0:57)
P2_ Create a column with the market cap in $ Billion (1:59)
P3_ What are the top 5 crypto currencies based on marketcap in Billions on 1st April 2022 (2:35)
P4_ What is the Circulating Supply for the top 5 cryptos on 1st April 2022 (3:14)
P5_ Which is the most expensive crypto (measured on 1st April 2022) (1:53)
P6_ How many currencies have a market cap of $100Mn+ (measured on 1st April) (3:18)
P7_ What was the average price for Bitcoin in 2021 (2:11)
P8_ What was the range (min, max) of price for Bitcoin in 2021 (2:26)
P9_ What was the price volatility for Bitcoin in 2021 (1:34)
P10_ What was the monthly average price for Bitcoin (2:49)

Clickstream Case Study Through SQL

Clickstream Case Study Through SQL Intro
Q1 - Understanding the Visits database (2:09)
Q2 - What are the total number of sessions on the website_ (0:54)
Q3 - What are the total number of orders on the website_ (1:33)
Q4 - What is the Overall Conversion Rate for the website_ (2:00)
Q5 - What is the Cart Conversion Rate for the website_ (1:54)
Q6 - What is the Bounce Rate for the Website_ (2:37)
Q7 - Build a funnel analysis for the website Overall Sessions (3:00)
Q8 - Build a funnel analysis for the website Percentage Conversion (2:34)
Q9 - What is the Checkout Abandonment Rate (2:30)

Clickstream Case Study Through Python

Clickstream Case Study Through Python Intro
P0 - Setting up Python Notebook and Getting the Dataset (0:57)
P1 - Understanding the Visits database (1:16)
P2 - What are the total number of sessions on the website_ (1:03)
P3 - What are the total number of orders on the website_ (1:08)
P4 - What is the Overall Conversion Rate for the website_ (2:08)
P5 - What is the Cart Conversion Rate for the website_ (1:57)
P6 - What is the Bounce Rate for the Website_ (3:16)
P7 - Build a funnel analysis for the website Overall Sessions (3:15)
P8 - Build a funnel analysis for the website Percentage Conversion (3:03)
P9 - What is the Checkout Abandonment Rate (2:01)

Ride Sharing Case Study Through SQL

Ride Sharing Case Study Through SQL Intro
Q1 - Understanding the Rides data
Q2 - What are the total number of rides in the dataset_ (0:38)
Q3 - What is the split of rides between classic, electric, & docked bikes (4:15)
Q4 - What is the split of rides between member and casual users (3:12)
Q5 - What are the top 5 routes that users use the most (3:36)
Q6 - Calculate the ride time for each ride (in minutes) (2:35)
Q7 - What percentage of rides were false starts_ (3:29)
Q8 - What is the average ride time _ Exclude all False Start rides (2:02)
Q9 - What is the IQR of ride time_ Exclude all False Start rides (3:56)
Q10 - What is the average daily rides that happen on the platform_ Method 1 (3:22)
Q11 - What is the average daily rides that happen on the platform_ Method 2 (3:37)
Q12 - What is the average weekly rides that happen on the platform_ (2:44)

Rides Case Study Through Python

Ride Sharing Case Study Through Python Intro
P0 - Setting up Python Notebook and Getting the Dataset (0:58)
P1 - Understanding the Rides database (1:22)
P2 - What are the total number of rides in the dataset? (1:16)
P3 - What is the split of rides between classic, electric, & docked bikes (4:03)
P4 - What is the split of rides between member and casual users (2:27)
P5 - What are the top 5 routes that users use the most (3:53)
P6 - Calculate the ride time for each ride (in minutes) (3:16)
P7 - What percentage of rides were false starts defined as ride time (in mins) is _ 1 _ (4:22)
P8 - What is the average ride time_ Exclude all False Start rides (2:21)
P9 - What is the IQR of ride time_ Exclude all False Start rides (3:08)
P10 - What is the average daily rides that happen on the platform_ (2:49)
P11 - What is the average weekly rides that happen on the platform_ (2:27)

Climate Case Study Through SQL

Climate Case Study Through SQL Intro
Q1 - Understand the Climate Dataset (1:44)
Q2 - For how many blocks do we have PM25 levels missing_ (1:40)
Q3 - What's the Total Population & no. of blocks in each state_ (2:05)
Q4 - Find the number of Blocks with Ozone levels greater than 50 in each state (1:42)
Q5 - Get count of blocks in states with Ozone greater than 60 or PM25 greater than 10 (2:14)
Q6 - What is the total population exposed to higher than average Ozone & PM25 Levels (2:32)
Q7 - Which state has maximum population exposed to higher than average PM25 Level (1:59)
Q8 - What is the percentage of low income population in the dataset? (2:06)
Q9 - What percentage of the low income population in the US have greater than mean air toxic cancer (2:27)
Q10 - Find out Rank for each block within each state based on PM25 Level (3:04)
Q11 - Find out the worst block in each state based on PM25 Level (3:50)
Q12 - Find out the best block in each state based on Ozone Level (3:29)

Climate Case Study Through Python

Climate Case Study Through Python Intro
P0 - Setting up Python Notebook and Getting the Dataset (1:06)
P1 - Understand the Climate Dataset (2:13)
P2 - List down the column names and datatype of each column (1:34)
P3 - For how many blocks do we have PM25 levels missing_ (2:48)
P4 - For how many blocks do we have PM25 levels missing_ Pandas (1:37)
P5 - What's the Total Population & no. of blocks in each state_ (2:25)
P6 - Find the number of Blocks with Ozone levels _ 50 for each state (2:08)
P7: Find the number of Blocks with Ozone levels > 50 for each state (2:30)
P8 - Get count of blocks in states with Ozone > 50 or PM25> 10? (2:12)
P9 - What is the total population exposed to higher than average Ozone Levels (2:40)
P10 - Which state has maximum population exposed to higher than average PM25 Level (2:34)
P11 - What is the percentage of low income population in the dataset_ Method 1 (2:41)

Health Case Study Through SQL

Health Case Study Through SQL Intro
Q1 - Understanding the Health dataset (1:35)
Q2 - How many countries are present in the data? (0:49)
Q3 - Which indicators are present in the data? (1:15)
Q4 - What is the Adolescent Fertility Rate in India_ (1:23)
Q5 - Find top 5 countries with the highest Adolescent Fertility Rate in 2020 (2:10)
Q6 - Find out the Top 5 countries with highest per capita health expenditure (PPP adjusted) in 2010_ (1:53)
Q7 - Rank countries by per capita health expenditure in 2010, lowest first(Using RANK function) (3:08)
Q8- Rank countries by per capita health expenditure in 2010, lowest first. Dense Rank (2:40)
Q9 - Rank countries by per capita health expenditure in 2010, lowest first. Row Number (3:13)

Health Case Study Through Python

Health Case Study Through Python Intro
P0 - Setting up Python Notebook and Getting the Dataset (1:01)
P1 - Understanding the Health Dataset (2:18)
P2 - How many and which indicators are present in the data_ (1:58)
P3 - What is the Adolescent Fertility Rate in India_ (1:53)
P4 - Find top 5 countries with the highest Adolescent Fertility Rate in 2020 (2:35)
P5 - Find the Top 5 countries with the highest per capita health expenditure in 2010_ (2:33)
P6 - Rank countries by per capita health expenditure in 2010, lowest first. Rank (4:04)
P7 - Rank countries by per capita health expenditure in 2010, lowest first. Dense Rank (3:57)
P8 - Rank countries by per capita health expenditure in 2010, lowest first. Row Number (4:10)

Risk Case Study Through SQL

Q1 - Understanding the Credit Risk Dataset (1:35)
Q2 - What percentage of users have atleast one overdue payment? (2:33)
Q3 - What is the total exposure for company in millions? (1:13)
Q4 - What is the mean age for users with 90D delinquency? (1:03)
Q5 - What is the average utilization rate for customers? (1:27)
Q7 - Compare the mean credit limit for each gender. Understanding equity in financial inclusion (1:40)
Q7. What is Exposure at Default (EAD) for the company? (1:19)
Q8 - What is the percentage of good users in the portfolio? (2:27)

Risk Case Study Through Python

P0. Setting up Python notebook - Risk dataset (1:18)
P1. Understanding the risk dataset (2:31)
P2. What percentage of users have atleast one overdue payment (4:30)
P3. What is the total exposure for company in millions? (1:06)
P4. What is the mean age for users with 90D delinquency? (2:32)
P5. What is the average utilization rate for customers? (2:41)
P6. What percentage of users in each age bracket are 90 days delinquent? (6:47)
P7. Compare the mean credit limit for each gender (1:06)
P8. What is the total exposure at default? (2:14)
P9. What is the percentage of good users in the portfolio? (3:11)

Fraud Case Study Through SQL

Q1 - Understanding the fraud dataset (1:39)
Q2 - How many users and transactions are present in data? (1:05)
Q3 - What is average revenue per user (ARPU)? (1:49)
Q5 - Find max and min txn date (1:00)
Q6 - Find average monthly transactions (1:51)
Q7 - Find the Dispute Rate by Volume (1:48)
Q8 - Find dispute rate by amount (2:04)
Q9 - Find the overall dispute rate by volume broken down by customer age (2:17)
Q10 - Find the top 5 customers with max disputes along with dispute count (1:50)
Q11 - Find dispute count broken down by dispute reason (1:24)
Q12 - Categorize all unauthorized txns as fraud (1:27)
Q13 - What is the overall fraud rate in the dataset? Assume all unauthorozed txns as fraud (3:13)
Q14 - Find total number of users who have raised > $1K in dispute (2:03)
Q15 - Find monthly dispute rate (1:57)
Q17. Calculate the Dispute win rate for customers (2:25)

Fraud Case Study Through Python

P0- Python setup (1:12)
P1 - Understanding the dataset (1:31)
P2. How many users are present in data? (1:21)
P3. What is Average transaction volume per user (ATPU)? (2:45)
P4. Extract Month & Year from transaction date (1:52)
P5. Fin the range of transaction date from the dataset. Find Min & Max date (2:02)
P6. Find the average monthly transaction volume (1:02)
P7. Find the overall dispute rate by volume (3:32)
P8. Find the overall dispute Rate by amount (2:55)
P9. Find the overall dispute rate by volume broken down by customer age (4:49)
P10. Find the top 5 customers with max disputes along with dispute count (3:13)
P11. Find dispute count broken down by dispute reason (2:34)
P12. Categorize unauthorized txns as fraud (2:50)
P13. Find total number of users who have raised > $1K in dispute (4:38)
P14. What is the overall fraud rate? Assume all unauthorized transactions as fraud (3:37)
P15. Find total number of users with greater than $1K in txn volume and with more than 1 dispute (7:58)
P16. Find monthly dispute rate and plot the trend (0:34)
P17. What is the customer win rate?

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Q3. Get the Name, Category, Country, & Funding, and output as New Names

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