People often ask: how data analytics and data science differ from each other? For many, it may be hard to differentiate these two disciplines. And today we’ll try shedding the light upon their principal distinctions.
To my mind, it’s not quite right to oppose these disciplines with each other. Instead, it’s more beneficial to consider them as parts of a single whole.
Data Science
Data science is an umbrella term that embraces data mining, data analytics, machine learning, and other disciplines. Its primary goal is to extract insights from massive collections of raw unstructured, semi-structured and structured data sets.
A significant difference between Data Science and Data Analytics lies in their goals and tactics. For example, a data scientist is more concerned with finding the right questions to ask while a data analyst intends to obtain the right answers based on the existing questions to the data.
Also, these disciplines are different in scopes. While data science produces broader insights on the subject and identifies inherent trends in the data, data analytics aims at focusing on more concrete / narrower goals.
Skills to become a proficient data scientist
Hard skills
A skilled data scientist has to know how to collect data sets from diverse sources, preprocess it by cleaning and transforming disordered data, extract required features and implement machine learning techniques to gain insights into the data.
Equally important is to know how to perform predictive analytics, sentiment analysis and integrate analytics with enterprise-scale systems.
Tools and languages
Along with ML techniques, a data scientist has to know Python, R, Scala, or/and SAS/STAT well, have a strong background in maths, probability, and statistics.
Since the data that data scientists work with is most often stored in relational databases, SQL expertise is a critical skill that can’t be underestimated. You need to know how to write basic and complex, nested queries to get the precise data you need.
Understanding how to work with unstructured data is especially valuable – in most cases, you’ll work with messy data. The ability to work with NoSQL databases (MongoDB, etc) is also highly appreciated.
Soft skills
A qualified data scientist should possess not only exceptional technical skills but also outstanding communication, problem-solving, and prioritization skills. Another key skill is a clear understanding of the data from a business point of view.
Given the above, data scientists are proficient data analysts with solid programming skills.
To be a good data scientist, you must be an excellent data analyst. But the opposite is not always true.
Skills to become a proficient data analyst
A data analyst has to be skilled at descriptive statistics, visualizing data, and reporting insights gained from the data.
Like data scientists, data analysts have to be proficient at statistics, working with databases, and looking at the data from multiple angles.
Hard skills
Must-have skills for data analysts are:
- Knowledge of R and Python
- Experience with Tableau, Power BI
- Ability to prepare the data for analysis
- Solid maths and statistics foundation
- The ability to create visualizations with Python/JavaScript libraries (e.g., pivot tables, charting libraries) is a plus.
Soft skills
A skilled data analyst has to expertly communicate their findings to a wide audience of decision-makers by using various data visualization and reporting methods. Data analysts have to constantly cooperate with other team members, be good at problem solving and research.
No less important is to be attentive to the smallest details and be able to present complex concepts in a simple language.
To summarize, data science and data analytics have a lot in common, yet, they have differences that can be nonobvious at first sight. Today we’ve covered the major aspects that differentiate these two powerful disciplines and outlined must-have skills for data scientists and data analysts.