Data Science: Banking career options

By ridhigrg |Email | Feb 20, 2020 | 5676 Views

And why not? Making a career in data science will not just be fruitful but also challenging. Challenging because there is a lot to discover in this field and every project will be an adventure-ready to be unfolded.

Data science already has a market revenue of $2.71 billion and it is expected to touch $20 billion marks by 2025. And if this rate of progress continues, one can only imagine the number of opportunities in the near future.

Whenever there is a lot of data, humans try to make sense out of them. Before it used to be looking at them and derives relationships and patterns out of it, just like observing a graph. Now it is about using several tools and techniques to poke around the data and find meaningful insights that can be proven. These insights help in understanding past trends, analyze the present and predict the future.

Data science includes several areas of expertise:
Data engineering and warehousing: These steps often include sourcing, managing and storing the data into an accessible form.
Data mining and statistical analysis: Here the data is explored and visualized using statistical models. This makes it easy to understand the patterns hidden in the data and where and how it is relevant in decision making.
Machine learning: This is the most important step in analytics. That is to create algorithms using the combed data, using programming and data models. This helps in creating systems that can understand a problem and give predictions based on it.
Data interpretation and presentation: This part involves using business acumen and industry knowledge. So that one can explain when and where the data inferences can be used and how it can help in strategic decision making.

Data scientists are professionals who are responsible for collecting and analyzing raw data, which helps in making a decision and predictive modeling. Data scientists need to have mathematical, statistical, programming and analytical problem-solving skills. And along with technical skills, one should have business skills too, to understand problems and find a solution to it.

One of the most important components of one's behavior should be of inquisitiveness. If one is curious enough, then one can see problems that no one else can comprehend and will be determined to find a solution to it like a detective.

Responsibilities of a data scientist:
  • Conduct open-ended research.
  • Frame relative questions on the matter.
  • Collect the massive amount of data and clean the data of all its gaps.
  • Explore the data from all its angles to understand its strengths and weaknesses.
  • Analyze the data using algorithms and statistical models to answer the asked questions.
  • Build tools and algorithms for automation.
  • Communicate the results and recommend a useful and effective solution.

You can pursue data science, even if you are a student or an experienced professional. Like it is said it's never too late for anything. You can be a mathematics graduate, a software engineer, a statistician or even a masters in economics; you can enter data science as an analyst, programmer, data engineer or architect, etc.

One needs to take up data science as their master's discipline or do a certification course, learn all technical and business skills. And most importantly have a researching and analytical outlook.

Source: HOB