Think of all the data exchanges that are happening around the world every day—all the texts, multimedia files, streaming, browsing, and even Google searches.
It all makes for a huge chunk of data that might seem messy and tangled.
However, understanding these entries can help us make predictions and set strategies. That's why data science is absolutely vital to many industries around us, especially commerce and banking.
Here's where the concept of Data Science for All or DS4A comes in.
So, what exactly is data science all about, and how can you get started in the field? Let's find out!
What Is Data Science?
At its core, data science means processing entries using mathematics, statistics, machine learning, and computer science.
In layman's terms, it is a way for businesses to decipher numbers, draw patterns, and uncover opportunities. Based on the results, the stakeholders set the company's strategic goals.
While it might sound too complicated, it is actually closer to everyday life than you expect. For one, you can see it in how we all get targeted recommendations on every platform we browse.
it is even relevant in image recognition software, text prediction, navigation systems, and gaming.
To tackle all these roles, data scientists work with IT teams that create the infrastructures and the c-suite executives that manage the business. Most importantly, they also have to collaborate with data analysts.
Data Science vs. Data Analytics: What Is the Difference?
Data science and data analytics can be two confusing terms, especially for someone who's still making their way in the field. In fact, some people might even use the two terms interchangeably.
While both work with very intertwined responsibilities, each title comes with its scope, skills, and merits.
Let's take a closer look at the major distinctions between the two terms:
1. Data Science Is the Umbrella Term
Data science is a wider concept that covers several phases and tasks. Meanwhile, analytics is more of a subsection in the process.
Data scientists rely on algorithms and machine learning models to draw results from the raw data. On the other hand, data analysis means using these results to create reports and dashboards. It mostly focuses on the presentation, warehousing, and visualization aspects.
One way to look at it is that the data analytics team answers the questions posed by the data scientists.
2. Data Scientist's Titles Rank Higher
In terms of seniority and salary, a data scientist usually comes first before the analyst. However, this does not mean that one is better than the other since they are both essential parts of the data science equation.
3. Data Analytics' Requirements Are More Flexible
In most cases, you don't really need one particular academic qualification to get into the field of data science and analytics. Anyone with a decent background in STEM or economics can make their way up the ladder.
That said, a data scientist might need to pursue further degrees in machine learning later on in their career path.
For now, you'll need to start with some basic skill sets for both job titles.
For a data analyst, these skills will come in handy:
- Excel proficiency
- Experience with statistical reporting tools like SQL database, Tableau, and BI
- Comfortable knowledge of languages like Python, SAS, R
Besides all the previous, a data scientist might need:
- Knowledge of deep and machine learning algorithms
- Storytelling skills
- Data mining
- Experience in software development and programming
- Proficiency in more advanced big data frameworks like Hadoop
- Excellent emotional intelligence
What Do Data Scientists Do?
So, the analyst tackles the presentation and visualization to answer the questions posed by data science.
Aside from asking the right questions, what do the scientists actually do?
The data scientists reshape the raw and unstructured data into tangible entries that the analyst can work with.
Here's how they do that:
Wrangling the Relevant Raw Data
The process starts by taking in the raw data from multiple sources. This could include click-through rates, session times, service subscriptions, surveys, revenue statistics, and more.
Of course, this raw mush will hardly ever be usable just as-is. The data scientists have to clean, cluster, and sometimes even convert the data format first.
A crucial task here is removing any corrupt sections that might ruin the results before funneling the rest to the next step.
Spotting the Patterns and Trends
A lot of people imagine that designing machine learning algorithms will take on the big bulk of a data scientist's job. However, that's not always the case.
At some point, the team will have a specific set of models they use routinely to spot patterns. The key here is being able to narrow down the parameters for each project to get accurate predictions.
Often, they'll have to compare different tests and metrics to pick the best dataset. This is what they call the A/B testing.
it is all these little tweaks that could differentiate between a super skilled and a mediocre data scientist.
Turning the Dataset Into Actionable Predictions
Once the analysts provide their reports and dashboards, it is time to make sense of what it all means from a practical perspective. It mostly requires using these results to make predictions for the market.
As an example, the main question might be why a certain company is losing clients. Then, the answer could point to an issue in targeted marketing or even lead to building data-driven products.
Some companies apply this to risk assessment, where the data science teams predict budgets and identify which areas are more promising. You can find the concept applied to credit risk and credit score in banks!
On the other hand, the dataset can also be used in fraud detection and cybersecurity by examining the patterns and anomalies.
Regardless of the applications, it is the data scientist's job to reflect the best possible course of action to the stakeholders.
5 Best Data Science for All Courses
Although you don't need any specific college degrees to get started, you might want to consider a beginner's course to help you figure things out.
After all, the right training can give you the skills you need to land a job as a data scientist or a data analyst.
Let's take a look at the top five data science courses out there:
1. Data Science for All Bootcamp by Correlation One
The Data Science for All Bootcamp is a set of 14 live lectures in the field of data analytics, covering SQL, Python, visualization, and more.
The lectures are only held on Saturdays, with a total of around 15 hours or so. This means that the program could also work for people with full-time jobs.
As a plus, the graduates can turn their course certification into college credits for a bachelor's degree at the Miami Dade College. However, the degree will focus more on data analytics than data science in general.
All in all, it is more of a sponsored empowerment initiative that selects the participants based on merit and potential. That means that you can't just enroll and take the course. You have to be eligible, apply, pass the filtration process, and then wait for the next round to start.
Pros:
- Fully free of charge
- Comes with career coaching
- Eligible for college credits at MDC
Cons:
- Available only for people in Canada, Latin America, and the U.S.
2. Data Science for Everyone by Datacamp
If you're looking for something that covers data literacy in the wider sense, Datacamp's Data Science for Everyone course might be the right move for you. it is very beginner-friendly and does not dig into coding all that much.
Although it is mostly non-technical, there are still 48 hands-on exercises that quiz your knowledge of the field.
Meanwhile, the educational material itself is divided into 15 videos. It should give you an overview of the field as a whole. By the end, you'll get to know all the different titles, roles, and applications for data science.
Google, Paypal, Uber, Microsoft, eBay, and other companies make use of this course in empowering their staff.
Pros:
- Free course
- Beginner-friendly
- Covers a big-picture view of data science as a field
Cons:
- Takes a non-technical approach that does not cover heavy coding aspects
3. Data Science Foundation by Codecademy
Codecademy is one of the best platforms that offer online courses when it comes to coding and programming.
For a beginner who's looking to get into coding, the Data Science Foundation should help you see how Python basics, Syntax, and Pandas apply to data science.
It also steers a bit into data cleaning and visualization through Matplotlib. This should help you differentiate between good and bad trends when you're presenting the results to stakeholders.
While you can access the material for a free trial period, you might need to upgrade to the Pro subscription to enroll.
Pros:
- Free trial available
- Digs deep into Python and its application in data science
- Prepares you for Codecademy's data scientist's career path
Cons:
- Focuses mainly on the coding aspects of data science
4. Introduction to Data Science by Coursera
The Introduction to Data Science is a collaboration between IBM and Coursera that tackles the field from a beginner's perspective. The whole thing should take around four months to finish, provided that you put in at least five hours every week.
The main appeal here is that you can audit for free or check if you're eligible for financial aid on the platform to get help with the fees.
As one of the Specialization programs, it offers a hands-on project to test your skills and understanding of the principles you'll learn.
However, this course does not dig deep into the visualization aspects of analytics. If that's what you're looking for, consider the alternative Introduction to Data Analytics, also supported by IBM.
Pros:
- Free auditing option
- Financial aid programs
- Awards you the IBM recognition badge as a specialist in data science foundations
Cons:
- does not dig deep into data analytics
5. Data Science A-Z by Udemy
Udemy is notorious for its data-science-related content, and this one ranks high on the list. In fact, many companies like Nasdaq, NetApp, and Eventbrite offer the Data Science A-Z course as an investment in their employees!
All in all, it is a set of 21 hours of on-demand lectures with real-life datasets and regular assignments.
The only downside here is that it can be a bit pricey for someone who's just getting started in the field. Once your 7-day free trial ends, you'll need to upgrade to a paid subscription to keep up with the content.
However, you will not have to purchase any special software for this course since it is all either free or provided as a Demo.
Pros:
- 30-day money-back guarantee
- Full lifetime access
- Covers a wide range of lessons
Cons:
- Requires a decent background in Excel
What Is DS4A?
The DS4A (Data Skills for All) is an empowerment initiative by Correlation One that aims to encourage minorities to join the fields of data science and analytics.
As we have covered, Correlation One tackles this with the 14-week Data Science for All Bootcamp. Although it is a rare opportunity, it can help push your career further. So far, over 3,000 trainees graduated from the program!
Data Science Salary
Just like any other tech job, the data science salary range will vary according to location and industry.
According to the University of Wisconsin, you can expect an annual salary of $85,000 as a data scientist. As you wrap up more experience, that number can go up to $170,000.
On the other hand, an entry-level data analyst can expect around $50,000 as a start.
As you can tell, data science is still a lucrative field for both scientists and analysts. After all, it wasn't dubbed one of the most in-demand skills by Forbes for nothing!
Final Thoughts
Although data science is a relatively new field in the world of tech and business, it is growing steadily with a promising future.
With initiatives like the Data Science for All, it is getting easier and easier to join the buzz.
If you're looking for something with a heavy focus on coding, the Data Science Foundation by Codecademy might be best for you. On the other hand, the Data Science for Everyone course by Datacamp offers a wider scope of the field.
As always, you can just audit the Introduction to Data Science on Coursera for free or enroll in the Data Science A-Z by Udemy for a fee.
All you need is some passion for machine learning, a basic understanding of statistics, and the right course to send you on your way!