In a world dominated by data, Data Science is the ladder to building a promising career in unique and challenging job positions. Kickstarting your career in Data Science is now easier than ever thanks to the vast pool of online platforms offering Data Science courses. These courses are specially designed to walk you through the concepts and intricacies of Data Science.
But, do you know the exact way to climb the ladder? Fret not, for we’re here to show you how!
So, let’s begin, shall we?
1. Decide – What Role Would Best Fit You?
The Data Science industry is a dynamic one, and hence, it demands a lot of varied roles. There are data scientists, data analysts, data engineers, statisticians, machine learning engineers, and so much more. Do your groundwork and research about the kind of skillset and job responsibilities each of these job positions demands. Choose the one that is closest to your background and professional experience.
If you feel overwhelmed and confused by the choices, you can always take help from outside. Try connecting with professionals in the industry and ask them about their job responsibilities and requirements. Find a trusted mentor and seek his/her guidance. This will help you gain an outsider’s perspective and help you choose the role that’d best fit you.
2. Choose – A Specialization Course
So, now that you have sorted out the ideal job role for yourself, you need to find a data science course that caters to the specific demands of that job role. There are many helpful MOOCs that you could choose from.
3. Develop – Technical and Analytical Skills
If you choose to leap into the world of Data Science having the right set of technical and analytical skills is a must. SAS and SPSS are excellent open-source platforms for nurturing your analytical skills. Whereas open-source tools like R and Python are great options for coding.
If you are a rookie at coding, we suggest you start off with GUI-based tools and then move on to sophisticated programming languages like Java, R, and Python. When working with these tools, always remember that the most efficient way of learning is through a hands-on approach. So, try to write your own codes and run them on dummy data sets. This way you’ll not only learn faster, but you will also get better with time.
Apart from programming languages, a good data professional is expected to have the basic know-how of SQL and MySQL.
4. Fortify – Your Statistics And Machine Learning Foundation
Dealing with enormous amounts of data on a daily basis and extracting meaningful information from it requires a strong statistical and mathematical background, especially for a data scientist. You will be awestruck and inspired to see what your statistical knowledge can do when combined with the right ML tools and algorithms.
The incorporation of statistics and ML algorithms into the emerging field of Data Science allows data analysts and scientists to extract information faster and obtain better results from Big Data projects.
Keeping this in mind, you should build on your statistical skills and knowledge and as for Machine Learning, you can start with basic concepts such as normal distribution, hypothesis testing, central limit theorem, and move your way up to more advanced ML techniques like linear regression, logistics regression, decision trees, cluster analysis, to name a few.
5. Read – About The Business Applications of Data Science
Data Science is ever-evolving, with newer applications and technologies cropping up from time to time. Thus, to stay relevant in the field of Data Science, it is essential that you continually assimilate knowledge about the changing dynamics of Data Science.
Being a Data Science professional is not just about possessing the requisite technical knowledge, you must also have extensive knowledge about the business implications of Data Science. Data scientists, data analysts, data visualizers all have something in common – to extract meaningful patterns and trends from massive chunks of data and use that information to transform businesses for the better. Thus, as a data professional it is expected that you can decide what kind of data approach should you take to solve a particular business issue.
Reading about business applications of Data Science such as how cluster analysis can be utilized for segregating customer segments; how market basket analysis helps retailers in product bundling, how logistic regression is a resourceful tool for fraud detection, and so o, can help you understand how the tools of Data Science function in the business landscape.
6. Invest – In Networking
Once you’ve gotten the hang of all the basics of Data Science, it’s time to expand your network. Data Science conferences, industry events, and tech meets are where you should be. These meets are nothing less than a talent pool, clubbed in one place. You can not only learn from the leaders in the industry, but you also get a chance to interact with peers and mentors and engage in fruitful conversations.
Also, the more events you attend, the more involved you become with the Data Science community. The contacts you make here might open the doors to new possibilities and career opportunities for you in the future.
As the demand for skilled and expert professionals in Data Science is on the rise, the key to success would be to follow a structured approach to the field. By following these steps, you can build the right set of skills that are demanded of Data Science specialists, and with time, you can emerge as one of the best in your field of expertise. As Thomas Edison had wisely stated,
“Genius is 1% inspiration and 99% perspiration.”
So, sweat it out now and make your way to the top!
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