Ever heard the phrase “Data is the new oil”? In the year 2006, a British mathematician named Clive Humby devised it. The idea behind this statement points towards the relevance of data to the activities in life – it has become a valuable asset. A lot is done with data, income is also generated from it. He went further to describe the usefulness of data and it is no doubt that data is the new crude in the world now. Data has demonstrated its rawness from the point of collection through classification, analysis, and interpretation and hence requires purification.
A “Data Professional” is someone who is knowledgeable about data and specializes in it. Professional data cleaners, selectors, analysts, and visualizers are all capable of cleaning, selecting, interpreting, and presenting data generally. They might also be able to build machine learning models quickly. The aspiration to be a data professional demonstrates a drive for qualification in that area. A data professional can be a data analyst, data scientist, data engineer, machine learning scientist, statistician, quality analyst and more.
Data scientists are frequently mistaken for data analysts, and vice versa. A data scientist can forecast the future based on historical trends, whereas a data analyst merely curates useful data insights. A data scientist’s job entails estimating the unknown, whereas a data analyst’s job is examining the known from new angles. The data space is wide to explore.
What it takes to be a Data Professional
1. Gain the relevant skills: Data is statistical and analytical in nature. A bachelor’s degree in a field that emphasizes statistical and analytical skills, such as math or computer science, is required. This is not required, however it can help you get started in a data science profession. However, anyone can begin a job in data science without first obtaining a bachelor’s degree from a university.
2. Learn how to analyze data: A knowledge of statistics makes the journey of processing and analyzing data more familiar, giving a head start on the entire concept of data. It is necessary to gain this rudimentary knowledge of data as it enlightens the achievable path. At Kaduna Data School, you are opportune to be taught the rudiments where you do not have prior experience. You are also not left out if you have prior knowledge of data.
3. Seek a job as an entry level in data analysis: Look for a job suitable for the role of beginner level in data analysis or any aspect that is proper with your qualification. This is a good step because it enhances skills already acquired and practice makes expertise. On the job, connect to learn from experts in the field you have in view.
4. Skill Up: As the saying goes “learning never ends”, there’s more to data than just the beginner’s stage. In the quest to be a professional, there is a need to take up advanced learning processes and practices, engage in data projects and training. Learn the languages of data such as Python, R, Structured Query Language (SQL) and Machine learning.
5. Certifications: Gaining more knowledge cannot be over-emphasized. As well, ensure to earn certifications and badges that determine the validity and ability to complete a data-oriented task. Enrolling at Kaduna Data School to learn data certifies you.
6. Design your portfolio: After gaining certifications and earning badges, it is appropriate to build a resume that highlights the accomplishments. This appeals to prospective employers who seek to hire as it provides insights to your abilities and how it aligns with their requirements.
Enroll at Kaduna Data School today and start building your career!
