Data Science Course Curriculum
Welcome to Databit
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Getting started with Mode Analytics
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Startups Case Study through SQL
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after you enroll
- 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
Available in
days
days
after you enroll
- 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
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days
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after you enroll
- 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
Available in
days
days
after you enroll
- 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
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- 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
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- 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
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- 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
Available in
days
days
after you enroll
- 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
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- 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
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days
days
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- 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
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- 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
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days
days
after you enroll
- 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
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- 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
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days
days
after you enroll
- 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
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- 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
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- 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
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- 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
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days
days
after you enroll
- 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?