Data Science : Unlocking the secrets of future

By Iqra Rizvi | 21 October 2024

What is Data Science ?

In simple terms, data science is all about analysing data and converting raw data into useful results. Companies can find out any mishaps by going through the data, and they can also perceive what's liked by the majority of users. Basically, it enhances the overall user experience. 

 However, the practice of gathering data and improving user experiences is not a recent discovery. Companies have been doing this for decades. So, what's new? To acknowledge this, it's crucial to understand its impact and how it evolved the working methods.

How is data science shaping the future? 

What makes data science a life-changing tool is its integration with modern technology. It’s a multidisciplinary field that merges advanced statistics and mathematics with dynamic programming. The advancement in technology, along with powerful algorithms and tools, makes data science a very powerful tool. Earlier, collecting data alone was so burdensome, then processing it and producing useful insights was another challenge. Nowadays, the amount of data we can collect through social media and digital platforms within minutes is much more than collectively gathered in decades. The ease of manipulating and storing data these days is unprecedented.

As for the title—'Data Science: Unlocking the secrets of the future' (What's the secret it's talking about? Oh! Don't overthink this, it's just a metaphor.) By unveiling the secrets of the future, it means, through techniques like predictive analytics and machine learning, businesses can analyse historical data to identify trends and forecast future outcomes. Instead of relying on past intuitions and working for years for simpler things, we can use advance data science techniques and predictive models made using it, which is less time and cost effective.

How it works ?

 Firstly, data is collected from different sources like databases, apps, or devices. But raw data is usually a mess, so it goes through data preprocessing or data wrangling to handle missing values, remove duplicates to check if the data is clean and ready to go. 

 Next, it moves into exploratory data analysis (EDA) to find patterns, correlations, and data distributions. This step prepares the dataset for model building. 

 After that, it creates a model using machine learning or statistical methods, training it to predict outcomes or find insights.

 Before the model is trusted, it gets tested for accuracy—this is model evaluation. It checks how well it performs on unseen data.

 After validation, the results are transformed into data visualizations—charts, graphs, or dashboards to make them easy to understand and representable. Finally, the model is ready for real-world use, helping businesses to make decisions and to predict future trends.

Data Science in every day life

Without us even realizing it, we are already incorporating data science into our daily lives. Who would have believed in the past that people today are texting with AI as if it were a regular person, providing emotional support and advice to humans? From searching the web for hours to simply typing a problem and receiving a customized response—sounds unbelievable, right? But look, it is data science, particularly advanced machine learning techniques, that has made this possible. We all encounter its practical applications in everyday situations, too. Whenever we want to purchase something, all it takes is typing the name of that item, and suddenly, our phones are flooded with that product and related items. Your phone knows your preferences better than your friends now. From recommending your favorite movies and songs to suggesting products in your favorite colors, the level of personalization offered by companies today is mind-blowing.

Data science and job opportunities

 As history suggests, with every innovation, the need for labour increases. All we need to do is evolve and adapt. With data science being a broad discipline offers a wide range of work opportunities. Be it the high salaries or the titles, these jobs are even attracting already working IT professionals. Like any other field, there's a hierarchy present in this job market too.  Based on experience and skills, the roles it can offer are: Data Analyst, Business Analyst, Junior Data Scientist, Senior Data Scientist, Lead Data Scientist, Data Engineer, Machine Learning Engineer, etc. You can search for those in detail to know more. As the popularity and dependency on data-driven results expand among companies, the demand for skilled professionals in data science continues to rise, making it a promising career path.

Conclusion

This article provides you with a surface level understanding of data science and its impact. In this rapidly changing world, where new technologies are introduced every then and now, data science offers a lot to explore. It's not fully discovered yet, and we still don't know what much more advance applications it can bring to life. If you're good with handling data and manipulating the data fascinates you, continue your research. Best wishes from me!  And as I mentioned about the answer will be at the end, 'Is AI actually going to take over our jobs?' Ask yourself.

What do you think? Don't forget to comment down your views. See you in next blog.