In order to truly understand how the internet came into existence we have to go way back to the year 1957, before 1957 the computer's were able to do only a single task at a time, this was very ineffective and the computing power available at that time was not enough to handle multiple data exchanges, and as the computer were very huge and had to stored in large air conditioned room so they weren't easily excess able by developers, so a remote connection was needed to be made. On October 4th 1957 the first un-manned satellite (Sputnik) was launched by the Soviet Union, and in order to prove it's lead in technology America, it founded the DARPA (Defense Advanced Research Project Agency) in February 1958, this was a huge network for transferring data an later on this network became the ARPANET.

ARPANET's development began in 1966, in the early days the universities were hesitating to connect directly to the mainframe so they connected the mainframe with small computers. These small computers were knows as IMP (Interface Message Processor), the IMP took control of the networking and the mainframe was just used for the purpose of initializing programs and the data files, since the IMP's were only connected with other IMP's this connection was known as "IMP subnet".For the first connection's between computer's the Network Working Group developed the NCP (Network Control Protocol), Later on the NCP was replaced by the more efficient TCP (Transmission Control Protocol) , the NPL (National Physical Network) network's in England was made upon financial bases a lot of traffic and data transfers were expected, and in order to avoid blockage in the lines the sent files/data was divided in smaller packets which were put together at the receiving end, this cycle of breaking and making of files/data is known as Packet Switching.

CYCLADES computer network was developed in France in the early 1970's, It was one of the pioneering networks experimenting with concept on Packet Switching, it was developed to explore the alternatives to ARPANET design, the main focus of CYCLADES development was on communication with other networks, and thus the term "Internet" was born, which was short form of "Inter network" which basically meant a network of network's.

Later on the phone company's built their X.25 protocol which enabled communication through their server's in exchange of monthly basic charges. DARPA's Transmission Control Protocol (TCP) based on connecting the computer's through gateway. The ISO (International Organization for Standardization) designed the OSI (Open System Interconnection) reference model, the specialty of the OSI model was that it consisted of standardizing the network's from it's ends which means that there was no more need of gateways and each computer was connected with the other computer's directly without any intervention during a communication, this was the the beginning of the era of modern Internet.

Later on many modification's and major break-through's took place before we got the internet that we use today, but those are the topic's for an another time, so what are your thought's on the creation of the internet and what would you do in there were no internet.?

When Jio AirFiber—a slick, plug-and-play wireless broadband service—launched, my mission was clear: drive adoption through in-app promotions. The catch? The Jio app’s prime real estate was already occupied by money-makers like prepaid recharges and JioFiber ( here’s the catch: there was also doubt on adoption of AirFiber when JioFiber was already a known, high-performing option ).

Replacing them out for AirFiber ads meant risking revenue, but not promoting AirFiber meant missing growth targets. Ah, the classic PM dilemma.

The Problem: A High-Stakes Game of Digital Real Estate

1. Limited Ad Space: The app’s most valuable spots were taken by profitable campaigns.

2. Revenue Tradeoffs: Replacing them with AirFiber ads could mean losing serious $$$.

3. Geo Constraints: AirFiber wasn’t available everywhere, so blasting ads nationwide was wasteful.

Key Challenge: How do I promote AirFiber hard where it matters, without blowing up our revenue goals?

The Strategy: Focus on getting Immediate feedback

We considered a few approaches:

• Time-based rotations (AirFiber every 3rd session) → Too inconsistent.

• Targeting heavy data users → Not enough correlation with availability.

• Geo-targeting → Bingo. Show AirFiber only where it was live.

Execution:

• Phase 1 (Pilot): Ran AirFiber ads in 9 cities (where AirFiber was launched in it’s Phase 1), replacing Less than 10% of existing promotions.

• Phase 2 (Analyze): Tracked CTR, conversions, and revenue impact.

• Phase 3 (Scale): Expanded to 20+ cities, using Phase 1 data to justify more ad space.

• Fallback: Non-serviceable areas kept seeing the usual money-making promos.

The Results: Happy Stakeholders, Happy PM

• 15% higher CTR for AirFiber ads (geo-targeting = relevance = wins).

• <5% revenue drop from displaced campaigns (vs. a projected 20% if we went national).

• 10% faster adoption of AirFiber as the free reach from the ad space (in Jio’s ecosystem) increased user awareness of the product rapidly.

Key Takeaways for Fellow PMs

💡 Geo-targeting is your friend – Prioritize without full displacement.
📊 Data de-risks scaling – Pilot first, expand later.
🤝 Stakeholder love = proof + patience – Show them the numbers, not just the vision.

So, next time you’re stuck between a new product launch and a revenue goal, remember: geo-fencing might just save your sanity (and your quarterly targets). 🚀

Eventually, he decided to create a collection of all his blogs and more, this is how "Psychology of Money" was born.

My top 3 takeaways

• Behaviour over intelligence for financial success

• The importance of Long-term thinking and success

• Simplicity & consistency beats complexity

What I learnt

1. No one is crazy

Your personal experiences with money make up maybe 0.00000001% of what’s happened in the world, but maybe 80% of how you think the world works.

Other people's financial decisions might seem irrational to us, but they make perfect sense to the person making them. It indicates how people’s unique backgrounds and experiences shape their financial behaviour.

2. Understanding Compounding

Staying invested over time and allowing your investments to grow without frequent changes or attempts to time the market is crucial.

3. Happiness & Freedom

   Money should be viewed as a tool for freedom and security rather than a source of happiness. Contentment with what you have is a key to financial peace.

4. Wealth is what you don't see

   True wealth is often hidden from view. People can appear wealthy but be financially insecure, and vice versa. Spending money to show people how much money you have is the fastest way to have less money.

5. Never-ending desires

   Living below your means and avoiding lifestyle inflation are foundational to building wealth. Contentment in of itself is a form of wealth.

Favourite Quotes

The hardest financial skill is getting the goalpost to stop moving.

The hardest financial skill is getting the goalpost to stop moving.

The freedom to do what you want, when you want, with who you want, for as long as you want, is priceless.

Wealth is largely invisible. It’s the money not spent.

Planning is important, but the most important part of every plan is to plan on the plan not going according to plan.

Who should read this?

This should be read by everyone, especially by people who are still young, as understanding the pitfalls of our behaviour concerning money will reap extraordinary rewards as young adults still have a whole life ahead of them for compounding the work its magic.

Resources

• Morgan Housels Blogs - https://collabfund.com/blog/

Before the Gregorian calendar was standardized, people around the world used their own calendars (some being more accurate than others), like most of the Western countries used the Julian calendar, which was named after Julius Caesar, Chinese people used their own lunisolar calendar, In India people used a calendar called Panchanga and it is still used to calculate Hindu festivals.

The reason the Gregorian calendar is the international standard used almost everywhere in the world to mark the passing of time might be a little more complicated than you might have thought. It takes 365 days, 5 hours, 48 minutes, and 46 seconds for the Earth to make a full revolution around the sun; Things would be a lot easier if it was even 365 days.

Most of the Western world used the Julian calendar, The Julian calendar had 365 days every year with an extra day every four years. This was pretty accurate, but it made a year on average 11 minutes and 14 seconds too long. But nobody took notice and this little glitch wouldn't be dealt with until 1582. That's the year that the Gregorian calendar started finally being used. That 11-minute glitch in the Julian calendar created a problem with Easter. It was supposed to be celebrated on the Sunday after the first full moon after the spring equinox, or March 21st. But as it slipped later and later each year, Pope Gregory got concerned, so he commissioned scientists and astronomers to get this right. After calculating, they realized that t**he traditional calendar had fallen 10 full days behind over the years.**

To rectify this, steps needed to be taken.

In 1582, October 5 Never Happened. On Oct. 5, 1582, most of Europe replaced the Julian calendar with the Gregorian calendar; the process of switching required that the 10 days after Oct. 4 should be skipped. The Gregorian calendar is more accurate than the Julian because it still had a leap year every four years, except for years that are divisible by 100, and unless that year is divisible by 400 so 2000 was a leap year, but 2100 will not be (it's messed up). Of course, nothing's perfect, and the Gregorian calendar is no exception: in 4909, is will be a full day ahead, but hopefully by then, we humans will be capable enough to figure things out.

Also, another fun fact, the reason it's 2015 and not 2768 is that in the year 525, Christian Monk Dionysius Exiguus determined that Jesus was born in the Roman year 753, and reset the year from there (although it's generally accepted that he was off by about 5-6 years). We can also blame Exiguus for the denominations BC (before Christ) and AD (Anno Domini, Latin for "The Year of Our Lord").

Academic and scientific communities have been increasingly using the more neutral, less Christianity-centric BCE and CE for "Before Common Era" and "Common Era". Of course, there are tonnes of other calendars that people use, and even some really intriguing calendars that were developed like the Tranquility calendar, which starts on the day we landed on the Moon, and has 13 28-day months, all named after scientists.

In case you want to read/watch some more on this topic.

https://rupertshepherd.info/tag/gregorian-calendar

https://www.youtube.com/watch?v=czgOWmtGVGs

Let's take a trip to the early 1900's New York. The economy was booming, lot of people were flocking in for the new opportunities & business potential, to satisfy this insane demand, the city had went on a spree of building sky scrapers where some of the tallest buildings at the time were being built in this era. 🚀

The Sky-High Problem

As the skyscrapers got taller and taller, one little problem that was non existent in the past, is now becoming a headache:- "Boredom". You see, the elevator tech in the early 1900's was nowhere as sophisticated as we have today, and the speed of elevators couldn't keep up with the rate at which the skyscrapers were getting taller.🏙️

This caused a huge increase in complaints regarding the elevators being slow, as more and more people had to spend more of their time is an elevator, this problem kept on growing. 📈

A Conventional Approach

To address this issue, the city council invited a few consultants most being engineers. The engineers immediately started thinking of different technical scenarios such as installing a newer elevator, using an even more powerful motor etc, but to everyone's dismay, most of the solutions were either not possible due to the technical constraints of that era or were too impractical to be implemented.

Thinking Outside the Box

But there was this one particular consultant who had a weird yet interesting idea of installing mirrors in the elevators. No-one in the council though this as a serious idea, but since nothing else worked, they decided to give it a try and started attaching mirrors in the elevators.

Within a month of installing the mirrors, the complaints dropped by more than 50% 📉, this surprised everyone in the council. They called the psychologist once again to gain insight on his reasoning behind adding mirrors in the elevators.

A Reflection on Success

The psychologist explained his reasoning being simply taking a step back and understanding the root-cause behind the massive number of complaints, which he deduced to be "Boredom", as people had nothing to do except staring a soul-less door of the elevator while they were in there (which was increasing as the building kept getting taller). The psychologist attacked the root cause by providing the people something to do while they were in there.

Conclusion: The Power of Perspective

Rather than jumping directly into solving the problem at hand, we should rather take a step back and ask ourselves: why this problem exists? and how it came to be?

This could give us a different perspective for taking on the problem, and potentially save a lot of time and effort, So next time you face a daunting problem, ask yourself: ***is there a 'mirror' solution that I'm overlooking?***🪞

Referance - https://hbr.org/2017/01/are-you-solving-the-right-problems

Nightingale's best-known diagram is this colourful depiction of causes of mortality, illustrated by overlapping wedges. Part of the chart's enduring success is attributable to its strange yet interesting form; others at the time presented similar data in conventional line graphs, to little effect. As the middle story element of her visual argument, it elevated two comparisons. The first shows that deaths from preventable diseases (blue) greatly outnumbered hospital deaths from wounds (red). The second comparison, repeated from the first diagram, is between the first year (right) and the second year (left) of the war. It shows that mortality declined significantly between the two years, which are linked by a jagged line, and encourages readers to wonder what occurred to make such a difference.

Why use storytelling for presenting data?

Storytelling with data is the art of using data, facts, and statistics to communicate a compelling message that informs, educates, and inspires. In today’s data-driven world, storytelling with data has become an essential tool for organizations, businesses, and individuals looking to communicate their ideas and insights effectively.

Start with the why!

The first step in storytelling with data is to identify the story that you want to tell. This could be a message about a problem you want to solve, an opportunity you want to highlight, or a trend you want to explain. Once you have identified your story, you need to gather and analyze the data that will support it. This may involve collecting data from a variety of sources, such as surveys, studies, and databases.

Analyze...

Once you have the data, it’s time to start visualizing it. Data visualization is a powerful tool that can help to bring your story to life. There are many different types of data visualization, including bar charts, line graphs, scatter plots, and heat maps. The type of visualization you choose will depend on the type of data you have, the story you want to tell, and the audience you want to reach.

It’s important to remember that storytelling with data is not just about presenting facts and figures. It’s also about putting those facts and figures into context, making them relevant and accessible to your audience. To do this, you need to craft a narrative that guides your audience through the data, helping them to understand the story you are trying to tell.

and Visualize!

One effective way to do this is by using data visualization to highlight key trends and patterns. For example, you could use a bar chart to show the growth of a particular industry over time, or a line graph to show how a particular metric has changed over the years. Another way to put data into context is to use anecdotes or examples to illustrate your points. This could involve telling a story about a person who has been affected by the issue you are discussing, or highlighting a case study that demonstrates the impact of a particular solution.

Keep evolving!

Finally, it’s important to remember that storytelling with data is not a one-time event. You should continue to update your data and refine your story as new information becomes available. This could involve adding new data points, revising your visualizations, or tweaking your narrative to reflect new insights or trends.

In conclusion, storytelling with data is a powerful tool for communicating complex ideas and insights. Whether you are trying to educate your audience about a particular issue, highlight an opportunity, or explain a trend, storytelling with data can help you to communicate your message effectively and make a lasting impact. To be successful, it’s important to identify your story, gather and analyze your data, visualize your data, craft a narrative, and continue to refine your story as new information becomes available.

We will follow these steps on Given Dataset

1. Trying to make sense of the data.
2. Checking if there's any dirty data.
3. Visualizing the clean data.
4. Final Conclusion.

Step 1: Understand the data

• First we will load the Data
• We will create a rough Flowchart of Insights we can get from the Data
• Move to next Step

# First of all we'll load all the libraries

import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

Then we'll go ahead and read the source file from which we'ed be getting the data.

You can download the .csv file from here - https://github.com/realharshbhatt/Pythonproject/blob/main/Stores.csv

df=pd.read_csv('/Projects/Stores/Stores.csv')
df.head()

(Note - change the path to where you've stored the .csv file.)

Store ID     Store_Area     Items_Available     Daily_Customer_Count     Store_Sales
0     1     1659     1961     530     66490
1     2     1461     1752     210     39820
2     3     1340     1609     720     54010
3     4     1451     1748     620     53730
4     5     1770     2111     450     46620

Step-2: Data Cleaning

To find the missing and in correct values we generally use 3 Functions

• df.isnull( ).sum( ): It will give you total NULL values in each column
• df.info( ): This is very useful, This will give you all information like 'Non-Null Values' & 'Data Type' for each column
• df.unique( ): Gives you all unique values of a function

df.isnull().sum()

The Output we get would look something like this -

Store ID                0
Store_Area              0
Items_Available         0
Daily_Customer_Count    0
Store_Sales             0
dtype: int64

For more info about the columns, we will use info() function

df.info()

We can see that all the columns data is integer type

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 896 entries, 0 to 895
Data columns (total 5 columns):
 #   Column                Non-Null Count  Dtype
---  ------                --------------  -----
 0   Store ID              896 non-null    int64
 1   Store_Area            896 non-null    int64
 2   Items_Available       896 non-null    int64
 3   Daily_Customer_Count  896 non-null    int64
 4   Store_Sales           896 non-null    int64
dtypes: int64(5)
memory usage: 35.1 KB

Cleaning the individual data.

We will use unique() function to find all the unique values in store area column.

df.Store_Area.unique()

Below we can see all the unique values in the column.

array([1659, 1461, 1340, 1451, 1770, 1442, 1542, 1261, 1090, 1030, 1187,
       1751, 1746, 1615, 1469, 1644, 1578, 1703, 1438, 1940, 1421, 1458,
       1719, 1449, 1234, 1732, 1475, 1390, 1642, 1715, 1439, 1250, 1331,
       1784, 1375, 1871, 1174, 1839, 1270, 1435,  965, 1665, 1780, 1009,
       1227, 1769, 1660, 1472, 1408, 1514, 1565, 1074, 1864, 1570, 1417,
       1734, 1470, 1761, 1756, 1704, 2011, 1310, 1544, 1707, 1881, 1416,
       1631, 1318, 1692, 1152,  891, 1468, 1539, 1635, 1267, 1720, 1462,
       1431, 1441, 1572, 1287, 1931, 1252, 1238, 1479, 1590, 2169, 1838,
       1385, 1921, 1975, 1853, 1816, 1785, 1579, 1096, 1919, 1262, 1374,
       1309, 1207, 1929, 1573, 1415, 1162, 1485, 1897, 1607, 1909, 1274,
       1157, 1712, 1500, 1682, 1525, 1947, 1164, 1787, 1718, 1365, 1368,
       1342, 1076, 1396, 1713, 1370, 1667, 1638, 1581, 1795, 1179, 1978,
       1688, 1214, 1504, 1498, 1229, 1936, 1369, 1662, 1548, 1649, 1393,
       1450, 1613,  775, 1275, 1740, 1372, 1414, 2044, 1823,  955, 1465,
       1232, 1481, 1343, 1007, 1762, 1527, 1356, 1536, 1605, 1626, 1612,
       1923, 1702, 1398, 1437, 1524, 1302, 1666, 1391, 1778, 1652, 1841,
       1496, 1148, 1321,  992, 1540, 1807, 1526, 1406, 1575, 1488, 1658,
       1863, 1604, 1620, 1251, 1647, 1829, 1852, 1699, 1325, 1350, 1347,
       1397, 1245, 1366, 1378, 1767, 1184, 1257, 1329, 1557, 2007, 1185,
       1657, 1294, 1296, 1733, 1641, 1373, 1550, 1583, 1428, 1648, 1025,
       2001, 1145,  913, 1199, 1875, 1153, 1240, 1381, 1701, 1206, 1476,
       1189, 1837, 1319, 1617, 1517, 1764, 1855, 1327, 1533, 1856, 1000,
       1313, 1494, 1386, 1979, 1057,  902, 1314, 1513, 1305, 1180, 1142,
       1471, 1075, 1585, 1577, 1092, 1523, 1614, 1566, 2019, 1766, 1293,
       1236, 1332, 1208, 1190, 1448, 1771, 1510, 1808, 1640, 1060, 1633,
       1222, 1619, 1624, 1887, 1320, 1455,  966, 1922, 1678, 2004, 1954,
       1362, 1886, 1291, 1584, 1445, 1433, 1269, 1798, 1015, 1495, 1759,
       1219, 1571, 1404, 1124, 1484, 1111, 1078, 1876, 1412, 1121, 1691,
       1599, 1454, 1555, 1554, 1491, 1487, 1339, 1509, 1264, 1905, 1209,
       1546, 1689, 2063, 1848, 1480, 1576, 1948,  896, 1625, 1303, 1410,
       1432, 1891, 1322, 1440, 1280, 1161, 1175, 1395, 1443, 1247, 1788,
       1138, 1709, 1777, 1618, 1311, 1249, 1744, 1297, 1908, 1721, 1243,
       1307, 1628, 1556, 1768, 1685, 1474, 1794, 1086, 1501, 1353, 1165,
       1845, 1172, 1436, 1738, 2229, 1490, 2015, 1611, 1430, 1543, 1836,
       1463, 1213, 1244, 1745,  933, 1675, 1842, 1316, 1760, 1323, 1230,
       1167, 1429, 1609, 1109, 1191, 1335, 1382, 1588, 1918, 1334, 1832,
       1271, 1735, 1627, 1351, 1520, 1537, 1622, 1001, 1857, 1552, 1700,
       1482, 1192, 1601, 2214, 1141,  869, 2049, 1883, 1723,  932, 1137,
       1231, 1237, 1371, 1748, 1212, 1466, 2026, 1772, 1511, 1834, 1143,
       1586, 1860, 1424, 1710, 1553, 1814, 1847,  946, 1563, 1664, 1241,
       1690, 1289, 1608, 1344, 1529, 1677, 1426, 1503, 1971, 1383, 1508,
       1411, 1545, 1505, 1422, 1564, 1568, 1933, 1593, 1812, 1580, 1100,
       1534, 1299, 1989, 1809, 1193, 1348, 1132, 1427, 1211, 1717, 1068,
       1755, 1591, 1797, 1519, 1637, 1203, 1884, 1160, 1317, 1560, 1645,
       1225, 1655, 1872, 1434, 1791, 1693, 1502, 1360, 1246, 1990,  967,
       1582, 1827, 1118, 1799, 1493, 1033, 1854, 1937, 1743, 1098, 1358,
       1336, 1418,  854, 1361, 1085, 1453, 1389, 1486, 1464, 1197, 1878,
       1150, 1562, 1596, 1119, 1116, 1934, 1235, 1497, 1444, 1477, 1041,
       1226, 1489, 1549, 1073, 1473, 2013, 1561, 1574, 1388, 1349, 1862,
       1200, 1722, 1850, 1447, 1679,  994, 1354, 1597, 1873, 1218, 1822,
       1716, 1072, 1330, 1425, 1363, 1671, 1379, 1724,  986, 1651, 1916,
       1528, 1409, 1419, 1535,  919, 2067, 1977, 1653, 1939, 1846,  780,
       1775, 1800, 1859, 1259, 1205, 1792, 1263, 1819, 1357, 1156, 1387],
      dtype=int64)

Nothing looks out of place, so now we can move ahead to create a visualization out of it. But before we move forward, if you'd like you can go through the articles that i've attached below if you want to know more details about data cleaning.

1. Complete Guide to Data Cleaning with Python - (https://medium.com/analytics-vidhya/complete-guide-to-data-cleaning-with-python-8c686104686c)

2. Data Cleaning with Python - (https://medium.com/bitgrit-data-science-publication/data-cleaning-with-python-f6bc3da64e45)

Ok then, let's get moving, shall we..!

Step-3: Data Visualization

Let's begin with getting a glimpse of how many columns we have to work with.

df.columns

This will show us the Titles of all the columns in our data set.

Index(['Store ID ', 'Store_Area', 'Items_Available', 'Daily_Customer_Count',
       'Store_Sales'],
      dtype='object')

Now let's make our 1st Viz. Trying to find the relation between Store ares and Items available in the store.

plt.figure(figsize=(6,6))
sns.scatterplot(x='Items_Available',y='Store_Area', data=df)

Here we can see that there's a clear co-relation between store area and number of items available.

You can create various different types of graphs using seaborn library, some of those graph types are:-

• Heatmap
• Histogram
• Bar Plot
• CatPlot (Categorical Plot)
• Density Plot
• Joint Distribution Plot

To know more about types of graphs, you can go through this article:- (https://towardsdatascience.com/14-data-visualization-plots-of-seaborn-14a7bdd16cd7)

To Plot multiple graph at once, we use pairplot

df1=df
df1.drop(['Store ID '],axis=1,inplace=True)
plt.figure(figsize=(6,6))
sns.pairplot(df1)

From the graphs shown below, we can tell that there is no direct relation between any other Parameter other than store area and items available.

If you want to learn more about creating visualization using seaborn, you can check out this article that explains viz creating using seaborn in detail using Pokemon dataset:- (https://elitedatascience.com/python-seaborn-tutorial)

This was a basic Data Analysis project i made to help me get familiar with the basics.

Thank You!