Is Statistics and Machine Learning all you Need to know Data Science?

Data science has evolved significantly over the last few years. It’s not the same as it used to be. You require a more sophisticated skillset to work in the capacity of a data scientist. Just knowledge of statistics and machine learning isn’t enough to deliver expected business results.

Data Scientists Of the Past

It’s well-known that demand for data science professionals grew steeply in the industry a decade ago, right after DJ Patil and Jeff Hammerbacher threw the word data science. The fancy title caught the attention of job aspirants and graduates from quantitative disciplines- computer science, economics, statistics, maths, physics, and more.

Before we knew it, there were thousands of aspirants flocking to data science roles. While the supply was dim, the demand for data science professionals was touching the sky. In the present time, the influx of MOOCs and course-educated data scientists has increased, while the demand is steady. Many people still flock to data science, but with incomplete knowledge or no knowledge, all because of hype.

The Hype Around Data Science

Data science has gained popularity among job aspirants. A few reasons that make the field promising are –

Big business impact
High job satisfaction
Rated as the hottest job in the U.S. three years in a row
Cutting edge developments
A constant influx of data generation
Ease access to data science education
Constantly increasing access to data science benefits

Not to mention, the big paychecks that are constantly advertised across social media, news, job review sites, etc., and the prestige that comes with the title.

The Myth About Being a Data Scientist

Until now, every time we hear the word data scientist, we think the job entails building machine learning models. At least the beginners think so. Experienced data scientists can easily tell that is not the case. The role and responsibilities can differ widely while the title remains the same.

Many employers have started to alter their job titles to suit the needs of the prospective employees. Roles like product analyst, business intelligence analyst, supply chain analyst, data analyst, statistician, and similar roles have the same responsibilities as a data scientist. Some of these roles don’t require knowledge of machine learning algorithms.

There are instances that people were leaving job titles because of the fancy job title. So companies have started altering their titles by including data scientists in the designation. For instance, data scientist-growth, data scientist- product development, data scientist- analytics, data scientist- people, and strategy.

In brief, data scientist’s role isn’t limited to making machine learning models, there’s much more they can do. So to all the data science aspirants out there, an ideal solution is to move beyond this myth and prepare themselves for the next generation role of data scientists.

The Next Generation Cohort of Data Scientists – Machine Learning

The next generation of data scientists requires a lot more than knowing how to apply machine learning algorithms to datasets. Here are a few more crucial things that help you nail the data scientist’s skills for the new advanced role.

Distributed Data Processing/ Machine Learning:
In the new role, data scientists are expected to create machine learning and data pipelines at scale. Experience with tools like Apache Spark, Apache Hadoop, DASK, etc. is required. Having experience with Apache Spark (in Python or Scala) is recommended.
Production ML/ Data Pipelines: Experience with Apache Airflow is another critical skill for creating data and machine learning pipelines by orchestrating jobs. Apache Airflow is a recommended skill to acquire.
DevOps- DevOps is an essential skill for data scientists. It is also one of the most neglected skills and is often missing in a data scientist’s coursework. The importance of knowledge of DevOps can be understood from the fact that you can’t build ML pipelines without an infrastructure.
You need to learn to write code that can scale across the infrastructure created by you or other members in the team. Many companies might not have the ML infrastructure laid out and might not be looking for someone who can build it effectively. Learning Docker, Kubernetes, and other ML applications with Flask should be made a priority.
Databases: SQL is another essential skill for data scientists. SQL is the standard query language for both cloud-based and traditional databases that allow data scientists to extract and collect data. Data scientist aspirants can practice SQLs on Leetcode, Hacker Rank, Hacker Earth, or any other platform of choice to practice coding. These platforms will also help to practice for coding interviews. An important part of the data scientist’s job is to collect data from the warehouse with on-the-go preprocessing before running models. Further, a major part of the feature engineering can be done on-the-go while retrieving data to models with SQL, this is an essential skill that data scientist aspirants should learn to thrive in the new role.
Programming language: Knowledge of programming languages—Python, R, Scala, and Java is essential to contribute successfully in the new role.

Best Data Science Certification 2021

MOOCs and data science certifications are perhaps the best methods to learn data science skills. Globally –recognized data science certifications add credibility to your profile and demonstrate your skills and competence. Adding certifications to your CV will accelerate the pace of getting into your first job or/and internship, which is now essential for data science aspirants. The following five certifications are recommended for aspiring data science professionals.

1. ABDA^TM(Associate Big Data Analyst) – An entry-level certification for fresh graduates and data science beginners.
2. SBDA^TM (Senior Big Data Analyst) –A mid-level certification for graduates and working data science professionals.
3. SDS^TM (Senior Data Scientist) – A high-level certification for working data science professionals.
4. IBM Professional Data Science Certificate – A beginner to a mid-level certificate for data science aspirants.
5. Harvard’s Professional Certificate in Data Science – For data science aspirants looking to break into data science fast and conveniently.