Data And Analysis
Tags: datascience, analyst, machinelearning, ai
Published: 2021-09-17 13:49:50

I am excited to try this new format, this time I was thinking of doing this in the form of ques/ans. This is something I am trying for the first time I hope you like it so fingers crossed!!!!

#### Q.1 Over the last decade, data has transformed the way the world works. Describe an area where you think data will change the world in the next five years. Which lesson(s) from the evolution of data in recent years would you draw on to help make sure data changes the world in positive ways going forward?

It is very exciting to see how data is changing the pace and transformative potential of today’s innovative technologies and how much we can do with it. Data is just facts and statistics collected together but how we use it can determine the future and how we apply it can solve the world’s most pressing problems, such as feeding a global and growing population; improving access to and quality of healthcare; and significantly reducing carbon emissions to arrest the negative effects of climate change. The next five years will see profound improvements in addressing these challenges. 
While the COVID-19 pandemic has provided a difficult lesson in just how susceptible our world is today to human and economic turmoil, it has also - perhaps for the first time in history - necessitated global collaboration, data transparency, and speed at the highest levels of government in order to minimize an immediate threat to human life. The data which was collected all over the world during this difficult phase can help us to fight such situations in the future.
I think in the next five years the area which will be most affected by data will be industries, over the next five years, carbon-heavy industries will use machine learning and AI technology to dramatically reduce their carbon footprint. Traditionally, industries like manufacturing and oil and gas have been slow to implement decarbonization efforts as they struggle to maintain productivity and profitability while doing so. However, climate change, as well as regulatory pressure and market volatility, are pushing these industries to adjust. 
For example, oil and gas and industrial manufacturing organizations are feeling the pinch of regulators, who want them to significantly reduce CO2 emissions within the next few years. Technology-enabled initiatives were vital to boosting decarbonizing efforts in sectors like transportation and buildings - and heavy industries will follow a similar approach. 
Indeed, as a result of increasing digital transformation, carbon-heavy sectors will be able to utilize advanced technologies, like AI and machine learning, using real-time, high-fidelity data from billions of connected devices to efficiently and proactively reduce harmful emissions and decrease carbon footprints.
It will be us who will decide how we use DATA in the future and how we use it to change the world in a positive way going forward. One of the major lessons that I learned from the evolution of data in recent years is that we are at a stage where data is used in every industry and how much efficient decision making is now because they all are mostly data-driven which makes the growth of industries much faster but data can also be easily abused which can harm people but at the end, it is us who decides how to use it to make the world a better place to live.

#### In 2 paragraphs, please explain why you’re passionate about Data Science.

Technology is something that has always fascinated me even when I was a kid. When I was in high school that’s when I decided to pursue my bachelor's in technology and when I got into university my first instinct was to pursue software engineering because all the people around me were also pursuing the same then I continued my journey to learn software development skills but there was something I wasn't that engaged in it, I was not excited to work and that was the time when I started looking for different things and got introduced to AI, Machine Learning, Data Analysis, Data science etc and that was something which excited me to work on and than I started to explore these new streams which helped me to get my passion back. 
Who likes to argue? Data analysis provides objective answers that can put an end to an argument. Data and analytics allow us to make informed decisions – and to stop guessing. I was never fond of making decisions based on gut feeling, perhaps because the gut says one thing one day, and something quite different the following day. It’s exciting and really interesting . It satisfies curiosity. It’s mysterious. It can be applied to many different domains. Businesses need to make trade-offs. Data and analytics can have real influence on the decisions a business takes, and on the outcome.

#### We want to better understand your experiences and interests relevant to data and analytics. Describe a previous project you’ve worked on that involved data. Please be sure to share: (1) Which question(s) were you trying to answer? (2) What aspects of the project were most interesting? (3) Which technical aspects of the project were most challenging and how did you work through challenges? (4) What was the conclusion/outcome of the project? (5) Beyond any analysis results/findings, what did you learn from working on the project?

I have worked on many basic projects to find what I was most interested in. What's crucial for me when starting one is to get very clear on the goals right at the start and then create a plan with milestones. I also like dealing with the most difficult parts of the projects early on—that way in case there are any significant issues, I'll still have a nice amount of time to complete before the deadline. I also typically break down large tasks into smaller chunks, so that it is easier to know where to start. Detailed planning is very important to ensure an important project goes smoothly. 
For example, one of my biggest projects was when I challenged myself to work on a dataset provided by quantium, in which I divided my work into three basic tasks   
1. Data preparation and customer analytics
Conducted analysis on client's transaction dataset and identify customer purchasing behaviors to generate insights and provide commercial recommendations.
2. Experimentation and uplift testing
Extended my analysis from Task 1 to identify benchmark stores that allowed me to test the impact of the trial store layouts on customer sales.
3. Analytics and commercial application
Used my analytics and insights from Task 1 and 2 to prepare a report for my client, the Category Manager.
The second task was really exciting extended analysis from Task 1 to help identify benchmark stores to test the impact of the trial store layouts on customer sales was interesting while performing this task I got to know many new things and aspects of how correct data visualization can help us to get businesses to grow much faster.
 
One of the main problems that I struggled with was when I was getting started the amount of data was something that I was scared of because it was such a big dataset, getting started with it was something that I struggled with but once I started it day by day I was getting into a groove which made this project such great for me. The project was completed on time, but looking back, I realize some problems could have been avoided but I learned from them and kept moving forward which is something I am proud of.
I enjoyed how this project made such a big difference in me, it helped me to gain confidence that I too can work on big projects, I learned more about myself, whenever I was stuck on something I communicated with many wonderful people from different tech communities which helped my communication skills and it was a wonderful experience and I feel great about it.

I hope you like it. link for the project above mentioned-[I'm an inline link](https://github.com/ayush8700/Quantium_Data_Analytics_virtual_experience)