Becoming a Data Scientist 101

'Data science is an interdisciplinary field of science since it utilizes systems, algorithms, processes, and scientific methods in making insights and extracting knowledge from many unstructured and structural data.'

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Becoming a Data Scientist 101

Being a data scientist is undeniably one of the hottest careers at the beginning of this decade. In this day and age, “big data” can answer anyone’s pressing questions. From small- and medium-sized businesses to non-profit organizations, there is data that needs to be applied, sorted and interpreted for a wide range of purposes.

Finding the answers to the questions of users can be a big challenge for people who are new to the field. How can businesses sort and label sales data for creating an effective marketing plan? Large institutions utilize behavior patterns to create engaging activities? Or non-profit organizations make use of limited marketing budget to find donors and charities?

This is where data scientists come in. Since the average Joe can’t sort, use, and process too much information (big data). Data scientists are educated and trained to collect, analyze, and organize data. In fact, they help people from different industries. This means that data scientists are very in-demand nowadays.

Data scientists hail from different fields, but most of them have technical training of some kind. The degrees related to data science include various IT, computer-related, and psychology majors. This field includes nearly all areas of statistics and math. Training in human behavior or business is also relatively common. Indeed, having a degree directly related to data science can be a big advantage to being a data scientist. However, there are undergrads prospering and excelling in this field.

What Exactly Is Data Science?

Data science is an interdisciplinary field of science since it utilizes systems, algorithms, processes, and scientific methods in making insights and extracting knowledge from many unstructured and structural data.

Unstructured data refers to information that isn’t organized properly or that lacks a pre-defined data model. Disorganized data is often text-heavy, but it may contain facts, numbers, and dates. Unstructured data causes ambiguities and irregularities, which challenge data scientists.

Still, data science doesn’t only deal with messy data but is also related to machine learning, data mining, and big data. As a whole, this interdisciplinary field is a concept used for unifying domain knowledge, machine learning, data analysis, and statistics in order to analyze and understand data phenomena.

Data miners and scientists use theories and techniques based on many fields and models in the context of IT, mathematics, and computer science. Jim Gray, a Turing award winner. He described the field as the 4th paradigm of science. He asserted, “Everything about science has been changing because of the 4th paradigm, which is the data deluge and data science.”

What’s a Data Scientist?

Data science involves dozens of various skills, which make defining the profession a challenge. Data science is a confusing and complex field. Fundamentally, a data scientist is someone who deals with all the intricacies and challenges of data science. Hence, they must-have adequate training, enough experience, and proper knowledge to be able to present visualize, and analyze data efficiently.

Data visualization allows users to see clear patterns. These aren’t noticeable if raw data, such as hard numbers, are presented on a spreadsheet. Data Scientists use highly advanced algorithms to determine patterns and arrange raw data from a jumble of stats and numbers. They do this to make unstructured data useful for an organization or business. Overall, data science looks for substance and meaning in datasets and big data.

Analytics on a laptop

A data scientist in action may be tasked to determine what makes customers switch services. For example, a telephone company hires data scientists to look at their big data. More specifically, they hire analysts or scientists to develop an algorithm that will enable them to look at the data points that are probably related to current and former customers.

Customer retention is very important in any kind of business, as much as lead generation—the process or action of turning prospects into paying customers. Subscribers of Netflix can see a real-time example of data visualization and management. Every time a user accesses his/her account, Netflix will provide suggestions that best fit his preferences. The suggestions will be based on the client’s browsing history and behavior. Similar features are also available on Amazon and Pandora.

Who’s a Good Candidate?

What are the top skills needed by data scientists? Do you have the right skillsets and the raw materials to have a long-lasting career in the field of data science? There are many unique characteristics, certifications, and technical skills required to become a data scientist. Some of them are listed below:


Most data scientists are highly educated or highly intelligent. About 90% have Master degrees and roughly 50% have PhDs. While there are exceptions, in the case of geniuses.

To become a data analyst or data scientist, you could finish a bachelor’s degree related to IT, Math, statistics, or computer science. This is the best option for aspirants. The most studied fields for this profession are Statistics and Mathematics. Engaging in or finishing a degree in any of the aforementioned fields will help you develop the skills and attitude necessary for analyzing and processing big data.

The majority of data scientists have a Ph.D. or a master’s degree. Most of them also take online courses to learn special skills, such as the capability to use Big Data querying or Hadoop. Aside from conventional learning methods, you can study the field by yourself in the comfort of your own home. This is possible if you have a high IQ or you have a background in statistics or engineering.

R Programming

Knowledge and experience in programming are a must-have in data science.  Specifically designed for data mining and other related fields. People use R for solving problems in data science. In truth, 43% of data scientists use R to find meaning in big datasets and to solve statistical problems.

Python Programming

Aside from R programming, data scientists are well-versed in Python coding. The good news is that Python is easy to learn. Along with C++, Perl, Java, Python is extremely helpful for data scientists. This is the reason why 40% of data scientists utilize Python as their major programming language.

Python is also very flexible, from creating programs to developing games. With it, developers can solve problems in a short amount of time. Data scientists utilize Python to classify data sets, create online services, build data models, perform data mining, and write algorithms for machine learning.

With Python’s versatility, you can use it in almost all the phases involved in data science projects. Python can handle various data formats. Additionally, you can also inject SQL tables into codes with ease using this programming language.

Hadoop Platform

Technical proficiency with the Hadoop platform. Nevertheless, approximately 65% of data scientists use Pig or Hive. Familiarity with cloud programs is also a must-have when working in the field.

Data scientists may encounter situations where the volume of data they’re handling exceeds the available memory of their system. They may also be required to deliver or extract data from different servers, even though their computer can’t handle such a resource-heavy task.


In such situations, Hadoop can become handy. The Apache Hadoop library is a type of software framework. It allows users to distribute the tasks for processing large data sets across different servers. What’s more, you can also use Hadoop for summarization, filtration, sampling, and data exploration.

SQL Coding/Database

Although Hadoop and NoSQL are integral for data scientists, aspirants should still be able to execute and write complex queries in structured query language (SQL). Like R and Python, SQL is also a programming language that enables users like you to execute operations, such as the extraction, addition, or deletion of data from a server or database.

Apache Spark

Apache Spark is similar to Hadoop. It’s a large data computation software framework. The only difference between the two is the fact that Spark is faster than Hadoop. Hadoop writes and reads to disk, making it slower. Spark, on the other hand, caches computations in memory.

AI and Machine Learning

The majority of data scientists aren’t proficient in machine learning techniques and areas. Machine learning involves adversarial learning, reinforcement learning, and neural networks. There are already too many generic data scientists out there. To stand out in the field of data science, you must be well-versed in machine learning techniques, which include logistic regression, decision trees, and many more.

Data Visualization

Online businesses generate a large amount of data every day. Data scientists translate data into human-readable terms. Graphs and charts are easier to understand than raw data. The idiom “A picture communicates a thousand words” also applies in data science. Data scientists must have the ability to visualize information given to them and convert raw data into human-readable terms.

man looking at code

Essential Resources and Certifications for Data Scientists

  • MOOCs
  •  Advanced degree
  •  Certifications
  •  Programming boot camps
  •  LinkedIn groups
  •  Kaggle
  •  KDnuggets and data science central
  •  The Burtch Works Study

Ways to Become a Data Scientist

In general, there are four ways for becoming a data scientist.

1. Earn a bachelor’s degree in math, IT, physics, computer science, engineering, or any other field.
2. Earn a master’s degree in a field related to data science
3. Study data science by yourself and enroll in refutable bootcamps and courses to earn certificates that could help you land a job in the industry
4. Gain experience in your current field, like a business, physics, healthcare, etc.

Final Words

There are many paths you can take to land a job in the field of data science. However, for all purposes and intents, it’s more advantageous to have a master’s degree in this field than to rely on self-studying. In case you choose the latter, make sure that the online courses you will enroll in our certified and reputable.

As stated above, more than 70% of data scientists in the industry are degree holders of majors related to the field. 38% of them have a Ph.D. Nevertheless, if you can stand out among the crowd, you have a chance to become a professional data scientist. What you need right now is the skills and attitude necessary in working in the field.

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