How to Develop Skills for the Data Science Roles

How to Develop Skills for the Data Science Roles

If you’re running an online business, you likely handle tons of information — average order value, conversion rates, email, social media interaction, prospects, and the list goes on.

You probably know that you should process these data to get the most out of them.

The question is, how do you manage all of the data when there are boatloads of them?

The answer: by applying data science.

Data science is the method of acquiring value out of your data and information.

With data science, you can improve your marketing strategies and customer engagement and loyalty, leading to better conversions and higher sales.

Hence, as a business owner, you must learn and develop data science skills.

Fortunately, doing so is convenient, even online.

In this post, you will know about the skills required for data science roles in your business and how you can develop them with ease.

Let’s hop in.

General Technical Skills

Machine Learning

Machine learning is at the core of data science roles. According to Wikipedia, machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.

Using data and algorithms, computer systems in machine learning train themselves to analyze, predict, and decide intelligently.

With this ability, machine learning becomes a game-changer for your business. 

For instance, by studying your customer’s purchases, machine learning can recommend offers and similar products your customers are likely to say “yes” to.

This action improves your customer’s buying experience, conversions, and sales.

Developing accurate machine learning models also helps you better spot lucrative business opportunities and avoid risks. For example, McKinsey & Company found that lenders can leverage data to determine whether to make a loan. Data such as credit reports and salary history now help lenders to make predictions on loan repayment but at a larger scale and in an increasingly automated way. 

That said, a good place to begin studying machine learning is Kaggle. It has a forum of data scientists and machine learning experts where you can raise questions and get materials if you’re new to the field, among others.

Statistics

Because data science involves a huge amount of data and information, you must have statistical skills.

Data science requires you to gather, organize, analyze, and interpret truckloads of data — all of which come with having statistical skills.

Data analysis, for instance, entails probability theory and descriptive statistics. With these competencies, you can make better-informed decisions for your business.

Others

Along with your statistical skills, you will also need to know how to use computer frameworks to unearth, process, and extract value out of raw and unorganized data.

Fundamentals or a background in mathematics, computer science, programming, and data evaluation, among others, can also benefit you in studying data science for your business.

Technological Skills

Python

Python is one of the best-known and fastest-growing programming languages for years now. 

Generic in its purpose, it is compatible with most modern data science tools and runs on nearly all operating systems. 

Python is a dynamic data and visualization tool that best works with machine learning, so adding this technology to your skillset will highly benefit your business. To enhance your skills, you can enroll in a Python for data science training. From the training, you can receive formal teaching and learning support and completion certificates to beef up your resumés.

SQL

Structured Query Language, or SQL, is also highly sought after. It is how you can primarily engage with relational databases.

Learning SQL for data science is beneficial for your business. 

SQL lets you access and wield boatloads of information in a relational database. You can also transmit, retrieve, break down, and organize your data with it.

If you want to learn SQL, there are also free and engaging tools online that lets you share and test SQL queries in your browser.

Soft Skills

Data science roles certainly require hard skills, but don’t forget to hone soft ones as well.

Soft skills are equally integral as you set yourself up for success in this age of automation, artificial intelligence (AI), and data science.

That said, one of the critical soft skills you should develop to support your technical skills is communication.

Communication

As you take on data science roles in your business, you begin to understand your data more clearly than anyone else.

Your team, however, may not grasp immediately what your data means. For your marketers to respond effectively, you must communicate your data insights and implications. 

You will need to explain these to them in simple terms, devoid of all jargon. And then, you will need to emphasize important information relevant to their tasks, guide them on specific aspects to improve on and allow them to share their thoughts as well.

When you do that, they can better grasp the situation and find the right strategies to help improve your business’ performance. This is why communication is incredibly important in today’s digital marketplace.

Develop your data science skills.

There’s no overemphasizing it, in this digital era, data will be the lifeblood of ecommerce businesses. As the competition intensifies for the consumer’s eyeball and attention, the winners in the future will be business owners who understand how to leverage data science to glean value out of their data.

In case you were wondering, e-commerce is the future of doing business. If you haven’t considered developing skills for data, it’s high time you do.

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About the Author

Jimmy Rodela is a Freelance Writer and a Content Marketer. He is the Founder of the Guild of Bloggers. He is a contributor on websites with millions of monthly traffic like Yahoo.com, Business.com, Monster.com, Business2Community and SocialMediaToday.com. Follow him on: LinkedinTwitter


 



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