Why Data Science?

Why I Chose Data Science 

    I often get asked why I chose to make such a career change and specifically, why data science. After much thought and research the decision was an easy one; data science was the natural next step for me as I found myself at a crossroad in my career journey. Data science is one of the most dynamic, ever changing industries and I am one who loves a challenge.    

My Journey

    My name is Sameeha Ramadhan and I am a part time data science student with Flatiron School. My career background is diverse: I briefly studied computer science in college before starting a children's special occasion clothing line. I was always intrigued about how information is used in obtaining and maintaining customers, a topic that I've encountered often when marketing my products. In the middle of last year when most weddings and special occasions were canceled or postponed due to the pandemic, my business came to a screeching halt. I saw this as an opportunity to grow and learn new skills that could arm me with tools I could use should I later restart my business, or make a new career change entirely. I absolutely love to learn, I love to solve problems and put pieces together to produce something useful; so I researched what could be brain stimulating enough while offering me the ability to be versatile and use my newfound knowledge to benefit as many fields as possible. I discovered data science. 

    Data science is one of the most dynamic industries today. In my research I've learned that the key to success in it is to understand that it is a constantly changing field, and I'd need to be up to a challenge and updating my knowledge at all times. What really excited me the most was the prospect of being able to apply the tools of this field to that of fashion, as I came across an interesting article about data science in fashion.  The following excerpt particularly stood out:  

"The traditional closed-book method of analyzing retail data meant that a number of fashion brands missed out on a lot of crucial information, such as data related to pricing, trends, insights and other must-have details. This may seem baffling to us now, given the competitive nature of the fashion industry and the importance of staying relevant, but it took a long time for brands to start using technology to their advantage.

In today’s market, that has all changed, and the fashion industry is now more reliant on data science than ever before. For example, specially trained data scientists can now predict whether a new collection is likely to be a success or not, simply by assessing previous sales data. This, in turn, helps companies ensure their money is being spent wisely. "

    While working on my clothing line, most of the time I'd use software and services that allowed me to make decisions in a few clicks but rarely examined how the data was analyzed. After understanding how data science can be applied to any professional field, I was further cemented in my decision to pursue this field of study and to take the first steps of becoming a data scientist.

My First Experience

    I've recently completed my first project at Flatiron School and got to enjoy my first experience as an up-and-coming data scientist. I was able to first-hand understand and use some of the tools of the industry in a real world application. After working through and completing this assignment, I'm more than excited to continue on this learning journey and to be equipped to apply what I've learned in any given field, regardless of whether or not that field is fashion.

    In the project, I was given the task of analyzing a number of datasets and databases and concluding what would be my recommendations on how Microsoft could enter into the movie making business. My analysis attempted to satisfy the informational needs of Microsoft by investigating the film industry to determine which types of films their new studio should produce. I observed a number of factors to better understand what contributes to the success of a movie, and in the end offered a few recommendations on the best strategies to get started. Microsoft can use the analysis to adjust planning, production, and marketing to hit the ground running as they enter this highly competitive space.

    I chose to focus my efforts on helping Microsoft to enter the movie making sector while successfully standing out from fierce competition by choosing to create films that their target audiences have shown to thoroughly enjoy. Doing so will allow them to produce movies that will instantly become hits, which will in turn allow them to improve on and produce even more content, setting them up to be a studio force to be reckoned with. Using data from well-known industry sources such as Imdb and RottenTomatoes, I analyzed and explained patterns in popular movie types based on best times of release, as well as budgeting decisions to help predict what audiences want from a film and thus, guaranteeing its success.

    In my project I used descriptive analysis, including description of movie trends based on the months in a year. This provided a useful overview of the movie industries' profits and profit margins based on the timing of a movie's release. I exclusively used the Python library Pandas to manipulate and analyze my data and was able to provide a number of conclusions that offered a number of results, including but not limited to, the following: 

    As is seen in the visualization, my analysis led to the conclusion that the summer months, namely June and July, as well as November and December, are the most profitable months in the movie industry. This can be largely owed to the fact that children have breaks from school and parents have more time off of work which allows for more opportunities for movie watching. Information such as this can prove to be valuable to a movie studio, as now they can plan to release their productions when they know they'll have greatest chance at high profits.

Note: Click here to review my full analysis in my Jupyter Notebook or my presentation.

    It's incredibly rewarding to have the ability to provide insight and provide solutions, such as the one above, using code and analyzation techniques. I am incredibly grateful for the opportunity to make this career change and embark on this exciting and ever-morphing journey. I know that the road ahead is challenging but I know that I am able to conquer it.