Big data refers to large volumes of structured, unstructured and semi-structured data with the potential of getting mined for information. Big data can be used in advanced analytics applications and machine learning projects.
Big data provides more information making it possible to gain answers and develop different approaches to tackling problems. It can help enterprises to address business activities such as customer experience and analytics. Big Data is beneficial in various other areas including product development, predictive maintenance, fraud compliance, machine learning, operational efficiency, and drive innovation, among others.
Why do professionals show interest in making a career in Big Data?
Big Data is everywhere, and analytics requires human insights no matter how advanced they may be. There’s always a compelling need for skilled people who will collect and preserve the data that is being generated. As enterprises look for ways to exploit the power of Big Data, the demand for professional analytics grows by the day.
Analytics professionals are in high demand globally. There’s no use for data if the skills to analyze it are missing. The job opportunities in Big Data analytics and management have increased over the years. The deficit on the supply of analytics professionals is huge and fails to meet the skill gap.
The strong demand for data analytics skills has enhanced the salary aspect for qualified professionals. This has made Big Data a hot job that pays big bucks for the professionals.
Most organizations are prioritizing Big Data Analytics because it improves their organizations in areas of recognizing sales and market opportunities, enabling precise business insights and enhancing social media marketing, among others. That is why the adoption of Big Data keeps growing and the need for professionals in this domain.
Steps for a Successful Career in Data Analytics
For you to be an analytics professional, you need to be highly educated. A strong educational background will develop your depth of knowledge that is necessary to become a data scientist. Most adept data scientists have degrees in social sciences, computer science, statistics and physical sciences. The required fields for Big Data Analytics are mathematics and statistics, computer science and engineering.
Learning data science can be quite daunting for beginners. The tools you need to learn (Python or R), the techniques you should focus on, the statistics, coding and much more can leave you exhausted and confused. But this is a path you’ve chosen to take, and you have to face these questions head-on.
The guide below will help anyone starting as a Data Scientist or an Analytics professional. Following the tips below and enrolling in the suggested courses will give you a head start in your career as a data scientist.
Select an appropriate role
The data science industry has many different roles including machine learning experts, data visualization experts, data engineers, and data scientists, to mention a few. Choosing a role will significantly depend on your education background and work experience. For example, a career in software development will not help you shift to data engineering. You have to be clear on the type of job you want to choose.
If you are not clear of your career path, you can do the following:
- Talk to professionals in the industry to find out the details of each of the roles.
- Get someone to mentor you and ask questions.
- Identify what you are good at and if that is what you want, choose a role that befits your field of study. Before selecting a role, clearly understand the requirements of the field and prepare well for it.
Enroll for a course
After deciding on a role, it is time for you to understand your role in detail. Going through the requirements of your role is just the tip of the iceberg. The demand for professional data scientists is soaring by the day, and thousands of courses are available to prepare you for the big job, so learn as much as you can. Although you may have all the learning material at your disposal, it is your efforts to learn that will count eventually.
You can join an accreditation program or take up a MOOC which is available for free. Your primary objective should be to take up a course that will cover all the basics and bring you to the level that you expect in your career quest.
During the course, ensure that you follow the coursework thoroughly, complete assignments and participate in all the discussions about the course. For example, if you are aspiring to become a machine learning engineer, enroll for Andrew Ng’s Machine Learning course and follow the course learning material diligently. Go through the videos, complete the assignments and finish the course to the end for you to have in-depth knowledge in your chosen field.
Select a Tool/Language and adhere to it
It is vital that you get the full experience in the field you are pursuing. If you have difficulty in choosing a language/tool because you are a beginner, you need not worry. Select any tool/language from the mainstream and commence on your data science training. What is important is for you to understand the concepts.
Start with the language you are most familiar with. If you have no clue about coding, take GUI based tools as your preference for now. As you grow and understand the concepts more, you can then revert to coding.
Embrace peer learning
Peer learning will enhance your motivation to learn more. Venturing into a new field can be intimidating especially when you do it alone, but with friends in the same field makes it easy for you. Get acquainted with peers you can physically interact with or join an internet group with people who share similar goals as you such as engaging with batch mates in a massive online course. There are other online forums such as Analytics Vidhya, StackExchange, and Reddit which can engage you just as a peer group would.
Focus on both theory and practical applications
Practical hands-on experience enhances your theory learning, and this will help you to understand the concepts and give you in-depth knowledge on how to apply your skills in reality. Apart from tackling the exercises and assignments, work on several data sets to apply your learning as well. Try to understand the assumptions, its function and how you can interpret the results. Always strive to develop a deeper understanding of everything you learn. Approach your mentor to help you with the right approach.
Learning is a continuous process. Therefore, you have to get more learning resources mostly from the internet from influential data scientists to enhance your knowledge. Improve your communication skills so that you can share your ideas with people. Try also to network with people in the same field by attending industry events, meetings, participating in hackathons and more to establish relationships that will help to advance your career.
Taking the right steps to gain knowledge and skills in data science is essential before you plunge into the demanding job market. The guides above, if followed keenly, will be your breakthrough to a successful career in data science.