In today’s business environment, data is a necessity and, if well tamed, it can quickly become a competitive advantage. More and more companies are hiring data professionals in order to maximize business revenue, forecast sales, and reduce costs.
Web and mobile apps, the Internet of Things (IoT), and the advancement of AI technology have implemented big data solutions so simple that even small and medium-sized companies can benefit from them. Businesses may use big data analytics to make better decisions and improve operational efficiency in several ways. So what are the main applications and benefits of data science in businesses?
A Data Analyst is responsible for collecting, processing, and performing analysis on large data sets. They deal with data wrangling, data modeling, and reporting, bringing in technical expertise to ensure the quality and accuracy of the data; after this, they process, design, and present their findings in a way that’s meaningful to help the end consumer, businesses, or organizations make better decisions.
After a few years of experience, a Data Analyst can move into a Data Scientist and a Data Engineer, as we will see below.
The first responsibility of a Data Analyst is to recognize and understand the company’s goals. This, in turn, helps streamline the whole analysis process. They are required to assess the available resources, comprehend the business problem, and gather the right set of data. This step is done by collaborating with different members such as Data Scientists, Business Analysts, and Programmers. Other main responsibilities include:
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The backgrounds of data analysts tend to vary a lot. Traditionally, a data analyst would be someone with a bachelor’s or master’s degree in math or computer science. However, the modern data analyst can also have a background in Natural Sciences, something business-related, or any other field with a quantitative component.
The education required to become a Data Analyst is not very strict, and it’s more based on one’s ability to work with and understand data.
This being said, key skills of Data Analysts should include:
The simple answer is no. Some may do it, but a data analyst is not required to code. He doesn’t need to be an expert or know any programming language deeply, though understanding SQL and Python is a competitive advantage. Data Analysts mostly use R or Tableau to create high-quality interactive maps, charts, and other visualizations.
A Data Scientist is a professional who uses different statistical techniques, data analysis methods, and machine learning to understand and analyze data that will help draw business conclusions. We can classify data science professionals as research-focused, business-focused, or development-focused.
They also proactively fetch information from sources galore and analyse it to understand better how the business is performing, building AI tools that automate certain processes within the company.
Simply put, a Data Scientist derives meaning out of messy and unstructured data turning it easier to read and understand.
Data scientists are responsible for cleaning, processing, and manipulating data using several data analytics tools. Besides those mentioned above, other key responsibilities include:
Data scientists have many of the same skills as Data Analysts. Still, they are often a little more IT-heavy, meaning that they’re able to create the databases themselves and pull together a lot of dispaired information that might exist in different sources. Because of these expectations, programming skill expectations for Data Scientists are much higher. They’re required to have good experience in programming languages like Python, C++, or Java, as well as proficiency in SQL.
Other important skills of a Data Scientist include:
The short answer is yes. One of the more effective ways to become a data scientist is to start as a data analyst, as both job roles are relatively similar.
Many people ask which is better, a data analyst or data scientist? But it’s important to clarify that a data science role is not better than a data analyst role. It’s simply a position that leverages a slightly different set of skills.
Simply put, a data science role is more suited for those who enjoy coding more. Many analysts do code, but they leverage other tools like Tableau and Power BI, as we saw above.
Here are a few tips to making the transition from Data Analyst to Data Scientist:
A Data Engineer job description falls into the category of a software engineer that is focused on building and maintaining data infrastructure and data systems. Data Engineers are the ones setting up the data warehouses, data pipelines, and databases that the Data Analysts and Data Scientists use to access and work with the data.
A Data Engineer is also perhaps the most well-defined role of the three, and you can probably see the most consistency with this one. Let’s take a better look at the responsibilities and skills of a data engineer.
Since Data Engineers are architects and caretakers of the data, their role mainly concentrates on database systems. What skills are required for a data enginner? They include:
The key difference between a Data Engineer and a Data Scientist is education and skills. Let’s think of data analytics like a timeline. Data engineers work at the very beginning of it on the back-end, whereas data scientists tend to take over where data engineers leave off, finding meaning and insights from it for the organisation.
As already seen, a data scientist is generally good at mathematics and statistics. He will usually be proficient in programming and have a penchant for machine learning and artificial intelligence modeling. A thorough understanding of the domain he is operating in is also an important skill to have, in order to gather business intelligence that can help the business achieve success. Lastly, a Data Scientist is also good at visually and verbally communicating insights from data with team leaders and business stakeholders.
On the other hand, the Data Engineer is a programmer proficient in Python, Java, and Scala and adept at handling distributed systems to analyse big amounts of data. His primary responsibility, as already explained, is creating free-flowing data pipelines using big data technologies for real-time or static data analytics.
All in all, these two roles use similar skill sets, so it’s safe to say that both data engineers and data scientists work with big data. Nonetheless, the data scientist is typically a better analyst than a programmer while the data engineer is a better programmer than an analyst. The two roles are complementary, not interchangeable, and they work best together when they’re made to perform tasks that match their strengths.
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Raising in-house data professionals might be hard, and hiring one may be something your business is not ready for yet. If data integration is something new in your strategy, then staff augmentation may just be what you’re looking for. And we know just the place to find a solution for all your data needs.
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