This is the era of data where more than 2.5 quintillion bytes of huge data is processed daily. With the amount of data produced increasing by each passing day, it has become a huge challenge for the companies to store data as it grows. This is why experts who can manage and organize huge amounts of data are the need of the hour.
Enter the data scientists who are also regarded as data wranglers. They take these gigantic mass of data both structured and unstructured, and clean, manage, and organize them. According to the Harvard University business review, data science is a highly ranked professional degree globally. If you are a curious person, who admires everyday challenges then join a data science course and take a big step for the future.
While, in the past, data science was only preferred in the IT industry, it has now spread its wings to different verticals like
- Social Media
The reach of data science is endless.
What is Data Science?
An expert in data science is known as a data scientist. This term was first introduced by DJ Patil and Jeff Hammerbacher in 2008, to define their roles in Facebook and LinkedIn.
According to Wikipedia, the term data science was coined much earlier. Data science can be defined as the study that combines domain prowess and programming skills. Thorough knowledge of statistics and mathematics is required to extract valuable insights. Moreover, data scientist applies ML(Machine learning) algorithms to text, numbers, images, audio, video, and more resources to produce AI(Artificial Intelligence).
Data scientists are basically data experts who use their expertise in both social science and technology to know the trends and manage the data.
Roadmap to a successful data scientist role
The profile of a data scientist requires skill sets to be polymorphic. This is the person who is prowess across significant areas. They have to embody the entrepreneurial spirit all through their learning journey. Their journey starts:
- By learning statistics, and moves on to programming languages. Most common programming languages used are Python, R, and SAS
- The major role of data scientists comes through implementing the concepts of machine learning and deep learning
- As this is the era of big data, having knowledge of some tools and technologies like Hadoop HDPS and Apache spark is vital
- Next step comes in knowing how to ingest, process, and cleaning the data known as data ingestion and data munging
- With the help of Tableau, QlikView, and Google chart, data scientist have to visualize the data and present it in the form of a bar graph, or line chart. This makes even the layman to easily understand
- At last, they have to be keen enough to make data-driven decisions to solve any problems
- In addition, you need to know the basic database knowledge to store and analyze data. Many companies like Cisco, Boeing, Kohler, Spotify etc. are incorporating DBMS tools
Data Science Career Opportunities
Many studies point to the fact that, 40 zettabytes of data will be in existence by 2020, and the career opportunities for data scientists will be skyrocketing. Shortage of skilled professionals is increasingly turning towards data scientists to help businesses in making decisions. As the demand raises, some of the best online programmes are dedicated to helping you gain programming and data skills. Furthermore, many marketing analysts predict that the U.K and India top in data scientist career progressions.
Thus, data scientists are required virtually in every industry. In fact, tech giants like Amazon, Google, Facebook, and Microsoft are recruiting U.S employees who have vast knowledge in data science. However, these figures will be growing in the following years. Data scientists are highly educated and 88% has masters and 46% have PhDs.
Once you gain an understanding of the data, you can land up a job in
BI(Business Intelligence) Analysts figure out the strategies to help business in finding the information quickly. Business managers need to make decisions based on these numbers. The average salary for BI is approximately $89,333.
Data architects work in close proximity with system designers, developers, and users to create outlines and design analytics tools for numerous platforms. Their average salary is $137,630.
Data Engineers execute batch processing on collected and stored data. They ensure that the data is readable by analyzing the data of their own and third party businesses. Their average salary is $151,307.
Data Scientists search, clean, & organize data that is suitable for companies. They analyze large chunks of data which is both raw and processed. Find patterns that will be advantageous to an organization. This helps in taking strategic decisions. Data scientists need to possess more technical skills than data analysts. Their average salary is $139,840.
Apart from the above, you can also be a
- Application Architect
- Infrastructure Architect
- Enterprise Architect
- Machine Learning Scientist
- Machine Learning Engineer
Tips to learn Data science
Studying data science can be interesting. Especially, when you are still a student or want to move on to the next phase. Which tool is the biggest challenge there are many techniques to concentrate? It can be
- Apache Spark
- Hadoop HDFS
Among these python leads the pack. ggplot2 is an advanced tool than an ‘R’ language used for data visualization. In addition to these languages, there are more tips where a data science graduate need to look into.
Take up a course
As we have discussed earlier, there are lot many career opportunities to choose from. Decide upon one and gain logical thinking through a dedicated effort and course. As the demand for data science is on raise, there are thousands of courses and resources available out there. You can either take MOC or register for the accreditation program. The selection between free and paid is not an issue, but the main aim should be to reach your goal or a suitable position.
Join an online peer group
Taking a new role can be daunting at first, but when you’re with a group of friends, this task might seem a little easy. The easiest way is to have a group of people to interact with. Otherwise, join communities on the internet whose goals are alike. Join the online course and communicate with batchmates actively. Even if there is no group which matches your expectations, you can still have valuable technical discussions. There are even online forums which give exposure to this kind of culture.
Improve communication skills
People generally ignore communication skills in data science roles. They think that only technical skills are enough to ace the interview. But this is purely a myth. Ever face an interview wherein the interviewer said, we will get back to you and never give a callback. This is all because of a lack of proper communication skills. In fact, this applies to every position in the organization. Communication skills are important when collaborating within a team. These are vital to share your ideas or prove a strong point in a meeting. You should know how to interact effectively and convince people.
Practical applications are more important
As a matter of fact, in training or with peer group, the first thing that should strike your mind is how far is it useful in real situations. This helps in understanding the concept and have deeper insights into the concepts learnt. A few tips to remember while undergoing a data science course is
- Ensure you complete all the necessary exercises and assignments
- Apply your learnings on a few open-source tools and check your understanding. In the beginning, only work on the assumptions and later move on to maths and other technicalities.
- View the problem solutions, case studies given by the experts online. Build a rapport by interacting with them and ask doubts. They would be able to pinpoint the right approach quicker.
At present, the demand for data science is multiplying. Employers and students are spending their time and money significantly to learn the concepts. So, taking appropriate steps at the right time can help your career grow exponentially. In addition, data science course is suitable for candidates who have the necessary skills in storing, collecting, managing, and analyzing data. Many companies like Amazon, Facebook, and Netflix are using this technology for making data-driven decisions.