Anyone Can Become a Data Scientist. Here’s How?

There is no doubt that data science is one of the most valuable fields in the contemporary job market. Learning data science will enable you not only in establishing a truly fruitful career in the IT industry but also in scores of other industries such as manufacturing, FMCG, food & beverages and even sports.

Data science requires the students to imbibe the basics of the discipline such as machine learning, statistical analysis, systems engineering as well as data mining. It requires one to develop a robust foundation of conceptual understanding of sub-disciplines like numbers and statistics.

Additionally, proficiency in basic mathematics can also help you go a long way in this career. If you want to pursue an education in data science, it would be wise to look into the different applications that require data science professionals. There are further different types of job specializations in Data analytics such as data mining, data engineering and data warehousing. It is vital for students to source, investigate, and interact with data and statistics through empirical analysis.

Here are some ways in which you can become a data science professional

You can start with a bachelor’s degree or a certification course. If you have an educational background in the subjects of information technology, numbers, arithmetic, statistics, science, systems engineering and computer science, learning the advanced intricacies of the subject can be easier and more straightforward.

It may also be prudent to gain the first-hand experience of working with an established data scientist to be able to figure out your specific interests. You can also choose a domain based on your previous academic background or work experiences. Serving as an apprentice to established data science professionals or agencies grants vast awareness of the latest happenings in this field.

It is important to carry out painstaking research into the essentials of a career in data science. You will find several articles on the internet describing shortcuts to obtaining immediate success in this area. However, students must remember that the road to success is a long and gritty one. Whereas many people advocate learning on the job rather than pursuing a specialized academic course in data science and is a complex subject with many folds and layers. However, one of the biggest challenges with such shortcuts is that most of such articles and blogs are authored by individuals who have already made it big in this industry. They place greater emphasis on how they handled the subsequent obstacles in accomplishing what they did rather than motivating aspirants in getting past every next hurdle to reach their goal.

Develop an analytical approach

You need to imbibe an empirical approach to understanding the data. For that, it is advisable to develop a questioning attitude rather than a conformist one. Data science is aimed at solving practical problems of the economy and human life by studying vast volumes of data. You must develop an interrogative attitude to be able to identify certain patterns and trends.

From answering simple questions such as the percentage of flights that are late to how money is spent on an average for every student, it becomes important to have an analytical mindset. Keeping oneself abreast with the current developments as well as being aware of the prevailing situation in the economy can help in apprehending an evaluative perspective.

Study the basics

You must start by identifying the questions that you intend to solve through data analysis. Studying applications that are considered the rudimentary aspects of data science such as python and arithmetic is important if you are serious about acquiring greater heights and echelons in the industry. For instance, Python is a highly versatile programming language that can allow you to perform complex calculations and predictions through machine learning.

It is also equally important to remember that data science concerns itself with answering questions related to driving business value and not as much about tools and applications. Getting yourself familiar with its broad concepts such as data cleaning and linear regression is crucial since new tools and technologies are introduced almost every day. But you must remember that most tools will only have a short shelf life and will be quickly replaced by newer mechanisms.

You will come across complex concepts during your bachelor’s or master’s education. Staying proactive in classroom lectures and taking down notes is the key to learning them. Should you face any challenges, speak with your lecturer or simply avail academic writing help from a subject matter expert.

Build applications and projects

If you are a budding data analyst, you must understand that you will learn from building products and creating applications rather than reading about them in theory. Getting hands-on experience in developing products through programming languages and coding will give you practical experience in understanding data analysis.

Several emerging and aspiring data scientists use data dumps to get more information on the important aspects of data science. As you develop advanced knowledge and skills, you can jump over to solving more complicated problems.

However, the bottom line remains that building products is the best way to learn data science. Many scientists use data to predict outcomes of various phenomena such as the weather, traffic and even NBA matches. Another extremely important application of data science is predicting the stock market.

Studying past data science projects

Data science is all about studying the past trends to predict future outcomes, therefore it goes without saying that students can benefit tremendously from studying previous projects. The students can also consider the idea of studying from the projects curated by industry stalwarts. Some examples of projects are Big data’s research on credit card fraud detection, Uber’s consumer data analysis and JP Morgan Chase’s sentiment analysis on the causes of income disparities.

For instance, the students can study how the IT giant Intel used big data to identify customer behaviour patterns. One can gain a tremendous frame of reference from evaluating how Intel utilizes data science to create purpose-built products and applications.

Bottom Line

Data analytics as science uses several innovative databases and tech mechanisms, cloud engineering systems and enterprise software applications. It offers limitless opportunities to advance careers in the new digital economy. Therefore, students must keep themselves abreast of the developments in the sphere of data science technologies.

Author Bio

Henry Clay, an education consultant, currently associated with many clients in the tutoring industry like He loves to play football and reading books in his free time.

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