What are the basic skills to develop to become a Data Scientist?

Being a data scientist is no easy feat. The job requires high intellect and capacity to understand complex data as well as communicate with people. Contrary to what many people think, data scientists are also well involved in making crucial business decisions, especially data-driven ones. They don’t spend their whole time facing their computers and analyzing data.

Their role is quite crucial to the success of organizations. This is because they are the ones responsible for collecting and analyzing the data on the company’s customers’ behavior, target audience, branding, and even creating a solid business plan. They even help detect irregularities, frauds, and holes in a company’s security system.

It is a rapidly expanding field that requires professionals to constantly learn new tools, methods, and how to apply those creatively. Plus, it involves being knowledgeable about math, science, statistics, chemometrics, and computer science. The amount of complex training and extensive skill set alone is reason enough why data scientists are in high demand and are being paid well-deserved a hefty sum of money.

If you’re interested in becoming a data scientist, here are some of the basic skills you have to develop:

Deep understanding of programming languages

Every data scientist must learn the programming languages used in their line of work as this is what they’ll use to organize, analyze, and manage small to big chunks of data. There are various coding languages used by professionals to deal with both structured and unstructured data sets.

The most commonly used programming languages deal with different areas or have different functions. For instance, there are some used to create libraries upon libraries of data sets, computing complex calculations, making AI smarter, validating statistical models, and some are specially developed to be used in the healthcare industry.

Python, MATLAB, Java, R, SQL, C/C++, Perl, and SAS are some of the most popularly used programming languages.

Machine Learning techniques

Machine learning is a subset of artificial intelligence that harnesses its power. Machine learning makes it easier to identify and extract data more accurately without needing to add new lines of code manually. Having advanced machine learning skills is beneficial to your career because only a few data scientists are proficient in the craft.

To stand out, make sure you know enough about supervised and unsupervised machine learning, decision trees, logistic regression, time series, outlier detection, natural language processing, and recommendation engines. Knowing about these techniques will gain you leverage in discovering opportunities and areas that need to be upgraded in the company.

Random forests, k-nearest neighbors, regression models ensemble methods, and naive bayes are the most commonly used machine learning algorithms.

Grappling Big Data

Big data is not boxes to datasets alone. It also encompasses frameworks, techniques, and tools that a data scientist develops and uses. Huge amounts of data can be gathered and stored in a short time compared to a decade ago. Traditional processing won’t stand a chance against such humongous data that it requires the assistance of specific frameworks to be analyzed. Having a data scientist in a company that is well-versed in tackling big data will give them an edge over competitors because it will help in their decision-making process.

Apache Spark, Apache Hadoop, Apache Kafka, and Apache Airflow are the ones most relied on when it comes to big data extraction and analysis.

Cloud computing tools

The Cloud is a very essential tool for data scientists. Even if large data storages have been invented, data stored in the Cloud are much easier and faster to access. This accessibility led to data science and cloud computing to go hand in hand because (1) data scientists store all sorts of data in the Cloud and (2) because of the influx of structured and unstructured big data stored in the Cloud, there is a need for data scientists to manage it. So, understanding the concept and how to navigate the Cloud is an essential skill for a data scientist to learn.

Data visualization and reporting

Lisa Qian, a data scientist at Airbnb, said that successful data scientists have a strong technical background, but the best data scientists also have great intuition about data. This means that a data scientist should not do their job by the book alone. It also involves interpersonal skills and the ability to convey findings and ideas through both diagrams and words. Every finding is crucial and could help advance the company. So, being able to pass on the information through graphic illustrations without confusing those you are presenting to is a must-have skill

Tableau, PowerBI, Microsoft Excel are the tools data scientists rely on to help them relay their findings to fellow data scientists and even to people outside their field of expertise.

Clear and effective communication

Apart from crafting tables, graphs, and charts to present data, data scientists should also know how to explain their findings clearly. This is because what is on the screen may be incomprehensible to non-technical people. Data scientists would have to communicate what they are seeing and letting them know what it means for the company. Good communicators are also well-sought for in the industry.


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