This is the year you’re making the jump from analyst to data scientist, and we are excited to see it happen (here are some other job titles to look out for too). Data science does make use of many of your current skills, but with a twist. Let’s take a look to see what the difference is and how you can prepare to make the change.
What’s the Difference Between Data Analyst and Data Scientist?
Data analysts do analyze data, but data scientists have skills that allow them to process data in innovative ways. They deal with both structured and unstructured data with a heavy dose of coding and math, allowing them not just data manipulation, but a new program or methodology for processing.
Data scientists often build their own framework for handling multiple data sets, methods, algorithms, and systems. This ecosystem allows them to estimate the unknown, going beyond looking at what is, and finding out what could be.
This difference is crucial. Data analysts are a necessary part of handling and maintaining data stores. Still, data science is what provides businesses with things like continuous intelligence or innovative new products like recommendation engines.
Working As a Data Analyst
Data analysts examine the data as it is. Not everyone has the talent to draw meaningful insights from numbers or manipulate the data to reveal patterns and realities that might be missed.
Data sets are well defined and can potentially answer questions about what is—why did business revenue fall last quarter? Why did a marketing campaign fall flat in one customer segment? Using a variety of tools, data analysts uncover what the data may have to say.
Data analysts need training in statistics and mathematics, but top data analyst skills include warehousing and mining, SQL, and data modeling. R and Python are also excellent skills to have because so much of the analysis ecosystem runs on top of those languages.
Analysts maintain databases, design data systems (i.e., tools for storing and finding data), and find patterns in existing data. You’ll need a good dose of soft skills in the form of visualization and communication to help explain what the data means to decision-makers and stakeholders.
Working as a Data Scientist
Many of your skills as a data analyst translate well to data science. Knowledge of R and/or Python is a must. SQL and data management skills are also a big part of data science. Where the two diverge sharply is the purpose of the question and the method of answering.
Substantial coding skills, along with a better understanding of complex math underlying algorithms, allow data scientists to look beyond what is and build predictive models. They’re answering bigger, unknown questions using undefined data.
You’ll need your data analyst skills but add in unstructured databases like MongoDB, distributed computing frameworks like Hadoop, and tools for object-oriented programming. Machine learning and deep learning are bigger parts of the data science ecosystem than data analysis, as well.
Data scientists often have advanced degrees, PhDs, for example, and are better versed in theoretical aspects of artificial intelligence. They’re designing data modeling processes and using things like unsupervised learning to run fast-paced models.
Data scientists also go beyond visualization to data storytelling. Because they’re able to pull more complex information and answers from a variety of data, not just structured, they’re able to tell stories that provide deeper insights.
Making the Switch to Data Science from Data Analytics
To be ready for your newest position as a data scientist, you don’t necessarily have to have an advanced degree, but there is a bit of work involved in making the switch. Here’s how to go about it.
- Take stock of your current skills—Expert in Python or R? Worked with relational databases like MySQL before? Comfortable with statistics and mathematical skills necessary for data visualization and data scrubbing? Good.
- Make a list of your needed skills—Some common ones needed for data science could be:
- non-relational databases, i.e., MongoDB
- machine learning models (regression, neural networks)
- distributed computing frameworks like Hadoop
- API interaction
- data visualization tools
- cloud computing tools
- Make a list of your ideal companies and find common skills between your list in step 2 and what companies are asking for. You can’t learn everything all at once, so target what your field is asking for.
- Find your resources—You don’t need formal schooling. There are plenty of boot camps and certification courses. There are also lots of online resources from edX, Coursera, Udemy, and others.
- Get experience—This experience could happen by solving a problem at your current workplace or one you have a particular interest in. There are even companies out there that actively crowdsource data science involvement in current issues.
- Join competitions—Hackathons, Kaggle competitions. Join things that get you noticed, and don’t worry about your current rankings. They provide real-time experience in active problems with the chance to get your work in front of people that matter.
- Market yourself—If you don’t have a Github, it’s necessary now. Companies are using Github for version control, and it’s better if you’re already there. You may also want to start your listing on LinkedIn Or AngelList.
Making The Transition
Data analytics is an excellent foot in the door for an aspiring data scientist, and getting to work as soon as you can on a real-world problem is the way to go. You’ll not only master new concepts faster, but you’ll also be able to market your skills and connect with companies and leaders in the field.
If you want to learn more from seasoned data science pros who’ve likely made a similar switch themselves, check out ODSC East 2020 this April 13-17 in Boston, especially the ODSC East Career Expo on April 13-17. Here, you will see what new jobs are available for your skillset, speak with hundreds of other data scientists, and even get your resume reviewed by a hiring manager directly so you can see what you specifically can do to make a transition.