3 Tips to Learn Data Science the Easy Way
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3 Tips to Learn Data Science the Easy Way

3 Tips to Learn Data Science the Easy Way The world is becoming data-driven. And it’s not only about businesses making better decisions – smart consumers are also leveraging data to make themselves smarter. From personalized health care to automated insurance quotes, our lives are getting ruled by numbers.

For all of us, trying to make sense of this new world is an uphill struggle. Data science is quite popular right now, but it is still mostly misunderstood. For getting started you just need to have the right focus. Let’s see what that means in practice.

1. Get your hands dirty The best way to learn something new is by doing it yourself – preferably with the guidance of someone who has already done it before. This advice holds especially true for data science since there are so many different tools involved – from SQL and Python to Hadoop and Spark MLlib . While this poses a serious learning curve, leveraging one technology stack for all projects can be both time-consuming and error-prone.


The good news is that big companies like Google, IBM or Amazon are already working on making developers’ lives easier. They have created a variety of cloud-based data science platforms that can be accessed from any web browser.

These tools enable you to access the same algorithms and libraries as their desktop counterparts but without requiring one to install anything – it’s all done on the fly through browser technology. Moreover, some of them offer free trials or even free licenses for open-source projects .


2. Get some business savvy

Since data is produced by people, organizations, and their interactions, it needs to be governed by a set of rules called business logic . Often used interchangeably with the term ‘operationalization’, this involves making sense of raw input data in order to make better decisions.

It may sound easy enough on paper , yet in practice you need to get your hands dirty and implement it yourself. This can be a serious time sink, especially for projects that require cross-functional collaboration.

Luckily, there are service providers out there who have already solved this problem. These businesses specialize in turning raw datasets into clean, structured data that can then be analyzed within a customer’s unique algorithm or model .

Instead of writing your own code from scratch, focus on what differentiates you from other organizations in the market – i.e., what value you add to the equation.

3. Get smart about data visualization If sending numbers back and forth is not so exciting for non-technical folks, why bother with all this effort at all? Data science brings about massive benefits when it comes to decision-making. After all, it is all about leveraging data to make better business decisions.

For instance, the healthcare industry can benefit immensely from using patient’s genetic information in order to personalize their treatment plans. On the flip side of that reality are privacy concerns and questions of whether patients will be willing to share their entire genome with doctors they just met .

Similar issues arise when dealing with customers’ browsing habits on e-commerce websites – companies want to use the data in order to improve their products but individuals are more reluctant due to privacy concerns or simply because they don’t understand how this could positively impact them as consumers. No matter what kind of dataset you’re looking at, people first need insight into its inner workings before they can trust the information to make decisions.


That brings us to data visualization . It is all about making sense of big datasets by representing them in a way that the human eye can quickly process and understand. Making something look pretty is not enough, there should always be a specific purpose behind every decision you make – the end goal determines which colors to use and where to place labels and headers.

This holds even more true for dashboards (and yes, those two terms mean different things) as they serve as your business’s main tool for understanding what’s going on at any given moment . What makes dashboards so ubiquitous?

They present complex information without requiring users to go through lengthy reports or drill down into hundreds of individual charts. Most importantly, everyone from board members and CEOs to customer support reps and system admins can use them.


At the end of the day, data science is all about finding those elusive insights. There’s nothing more exciting than hearing “We’ve been crunching these numbers for a year and we never thought they’d show this!” or reading an article like this one .

And as long as you keep these 3 tips in mind, whether it’s your first time learning about data science or you’re on your way to becoming a professional analyst, there’s no reason why you shouldn’t be able to make sense of , let alone enjoy your data!


3 Tips to Learn Data Science the Easy Way (continued) – ESP: are some tools that are available online to get this data, some are free while some may not be.

– Just because you use a service provider doesn’t mean you can’t learn something new! We encourage our clients to do so. Sometimes the smallest insights lead to the biggest break throughs!

– The article mentions that “everyone from board members and CEOs” can use dashboards. This is true but it is important to know that they were originally built for business analysts and decision makers who need real time information about what’s going on within their organization or company as a whole.

In order for your organization to benefit from dashboards make sure they include the right KPIs (key performance indicators). Having access to raw numbers in an Excel spreadsheet is nowhere near as useful as having the right data in the hands of those responsible for making decisions.

4. – What is Data Science? Because there are many definitions, we’ve decided to share a few from some reputable sources:

– “Data science is a scientific approach to extracting knowledge and insights from data” – by J. W. Foreman (source)

– “A process that involves descriptive statistics, predictive modeling, business understanding, information visualization and validation that uses specialized algorithms on structured and unstructured datasets” – by David Stephenson (source)

5. This guide will walk you through 10 must-have tools for any beginner looking to learn Data Science as quickly as possible .

6. Article Ex: So what’s an aspiring data scientist to do? A better question would be what are your options? The good news is that resources are everywhere, it’s just a matter of knowing where to look. There are some amazing products out there created by developers who understand the challenges of learning data science and have made it their mission to help beginners overcome them.

If you’re spending more time looking for resources than actually working through them, then you’re not alone! This is often referred to as the “Frodo Problem” – “It’s like Bilbo Baggins went on this incredible journey, fought all these incredible battles but nobody knows about it because he didn’t post anything on Facebook.”

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