Future Of Data Scientist

Data science has been a trending field of study in recent times, this is because of the amount of data that we create constantly and the computing power that is available with advancements in technology.

Data is the oil of our generation. Data science is becoming indispensable in today's digitally driven world, helping businesses understand consumer behavior, fine-tune its messaging, and capture new market share.

What is Data Science?

Think about what happens when you book a ride on Uber. You open the Uber app on your phone and tell the app where you want to go. Uber tries to find the nearest cab, sends them the directions to come to pick you up and take you to your destination. That was simple. But in the background, the seemingly simple task is carried out by collecting mountains of data from various sources like the phones, the map, and historic trends of traffic and demand for rides. With this data. Modern-day computers are programmed to calculate the nearest driver to you, the best route to your location and destination, the time it will take, and what you should pay.

In other words, this is made possible with data science. Data science has countless other applications as well and is at the intersection of statistics, data analysis, and machine learning. It is a combination of scientific methods, models, and algorithms working together to extract actionable business insights from data. The U.S. faces a shortage of 1,40,000 to 1,90,000 people with deep analytical skills and one point 5 million managers who can analyze big data to make effective decisions. The average salary of a data scientist is around 1,18,000 Dollars.

Become a Data Scientist

To become a data scientist, you don't need to have a technical background to be a data scientist. What you do need is in-depth knowledge in mathematics, analytical reasoning, the ability to work with large amounts of data. It would also help to have a strong intellectual quest, knowledge of data engineering, visualization ability, and excellent business acumen.

If you are from a technical background, then you could use Python, it is all about understanding the possibilities and asking the right questions, all in the search for the best answers. Every company is flooded with data and they have more data than they know what to do with. So regardless of the industry, vertical data science is likely to play a key role in your organization's future success. Data scientists help find new ways of reducing costs, entering new markets and customer demographics, and launching new products or services.Data science also has found social and medical applications such as child welfare and predictive diagnosis as well.

Data Science Lifecycle

The data discovery step includes the search for different sources of relevant data, structured or unstructured data. Then you make a decision to include specific datasets into your analysis. The data preparation includes converting data from different sources into a common format. You will standardize the data, look for anomalies, and make it more appropriate to work with. The data Science models are built using statistics, logistic and linear regression, differential and integral calculus, among other mathematical techniques. You could use tools like Python, SAS, SQL, Tableau, and so on. Getting things in the action phase includes checking the data models for its effectiveness and ability to deliver the results. You will have to verify the model works. If not, you have to rework on your model. A data scientist needs to liaison with the various teams and be able to seamlessly communicate his findings to key stakeholders and decision-makers in the organization.

Data Science Algorithms

Another critical element of data science are algorithms, which are a process of a set of rules to solve a certain problem. Some of the important data science algorithms include regression, classification and clustering techniques, decision trees and random force, machine learning techniques like supervised, unsupervised, and reinforcement learning. In addition to these, there are many algorithms that organizations develop to serve their unique needs.

Big Data’s Relation to Data Science

Big data is driven by the data science revolution. Big data is the engine propelling the rise of data science. Hadoop is a popular big data framework used by most organizations. Hadoop works in a distributed manner wherein both the processing and storing of data are distributed on commodity hardware. Hadoop is easily scalable, highly economical, fault-tolerant, and secure. Hadoop consists of Hadoop distributed file system or DFS for storing data and uses MAP reduce for processing data.

Another emerging framework is Apache Spark, which is touted to be up to 100 times faster than MAP reduce. Spark stores the data in the RAM, so iterative processing is fast and efficient. It also deploys directly an acyclic graph or DIG for the processing of data.

Demand for Data Scientist

There is a huge demand and supply mismatch when it comes to data scientists. Due to these salaries of data scientists are among the best in the industry. Top companies like Amazon, Google, Facebook, Microsoft in the tech space to others like ExxonMobil, Visa, Boeing, General Electric, and Bank of America are actively hiring data scientists. Now that you have learned about data science, why data science is indispensable. The data science lifecycle, how it relates to big data. It is time you start your journey in this promising domain and see your career soar.