Data Analytics vs Data Science: Main Differences

Introduction
Nowadays, data has become one of the most precious assets for companies. Every day businesses collect data from their websites, applications, social media, transactional systems, and other activities. But the ability to collect data is not enough; businesses require professionals who are capable of turning raw data into valuable insights. This is where Data Analytics and Data Science come into play.
Data Analytics and Data Science have much in common but are different fields. Both involve data processing but differ in their goals, techniques, tools, and duties.

What is Data Analytics?
Data Analytics is an approach to analyzing existing data to find patterns, trends, and insights. As a rule, data analysts deal with historic or recent data to know what was happening and why.
For example, an online retailer can analyze the sales data to find out which products bring the maximum profit during a certain period of time. This information will be helpful for improving inventory management and marketing strategies of the company.
Typical duties of a data analyst include:
Data collection and cleaning
Business information analysis
Report and dashboard generation
Trends identification
Information communication to decision-makers
Tools: Excel, SQL, Power BI, Tableau, Python.

What is Data Science?
Data Science is a wider discipline, consisting of statistics, programming, mathematics, machine learning, and data analysis. The data scientist uses data to find complicated patterns and create models that can predict future results.
For instance, an online store can use Data Science to understand which items are more likely to be bought by the customer depending on their search and purchase history.
Data scientists may work with:

• 1. Predictive modeling
• 2. Machine learning
• 3. Artificial intelligence
• 4. Statistical analysis
• 5. Data preparation
• 6. Automation and experiments
• 7. Python, R, SQL, TensorFlow, and other machine learning tools are frequently used. Main Differences The main distinction is their main focus. Data Analytics usually focuses on the current data analysis, while Data Science focuses on finding the deep understanding and predicting the future. Data analysts can answer the following questions:

1. What happened?
2. Why did it happen?
3. What are the trends? Data scientists can answer: What will happen? Can we predict the future behavior? Can we solve this problem using the machine learning model? It can be also different in career positions. Data Analysts, Business Analysts, BI Analysts, and Reporting Analysts are usually working with analytics. Data Scientists, Machine Learning Engineers, and AI Specialists are usually working with data science.

Conclusion
Both Data Analytics and Data Science are important subjects in the present-day digital economy. Data Analytics is useful for understanding data and its history in an organization, whereas Data Science is employed to make predictions and solve difficult problems by applying sophisticated methods.
The best option for those who are interested in reports, dashboards, business analysis, and visualization will be Data Analytics. If someone likes to code, study statistics, artificial intelligence and machine learning, then Data Science will be a better choice.
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