Most of the time, we get confused between data science and data analysis, but these two things have some differences. Although these are very exciting fields, they are also very rewarding. By the way, Data Science is more rewarding than Data Analysis. In India, a data scientist makes an average of 12 to 15 LPA; on the other hand, data analysis has an average salary of 7 LPA.

So, let's understand those differences in detail with an example. Let's take the example of Airbnb.

Data Analysis: Data analysis involves examining, cleaning, transforming, and visualizing data to discover insights, trends, and patterns that can help make informed decisions. It focuses on exploring historical data to answer specific questions and solve immediate problems. Data analysts typically work with structured data and use tools like Excel, SQL, and visualization libraries to create charts, graphs, and reports.

Example in Airbnb context: A data analyst at Airbnb might analyze historical booking data to answer questions like:

• What are the most popular neighbourhoods for guests?

• What is the average price of rentals in different cities?

• How does the availability of amenities affect rental prices?

In this case, the data analyst would primarily work with the existing data to provide insights into past trends and patterns that can inform business decisions.

Data Science: Data science encompasses a broader range of activities that involve extracting knowledge and insights from data using various techniques, including statistical modelling, machine learning, data mining, and more. Data scientists not only analyze data but also create predictive and prescriptive models to forecast future trends and make recommendations. They work with both structured and unstructured data and often use programming languages like Python or R along with specialized libraries for machine learning and data manipulation.

Example in Airbnb context: A data scientist at Airbnb might work on more complex tasks, such as:

• Developing a recommendation system to suggest personalized listings to users based on their preferences and past behaviour.

• Predicting future demand for rentals in different cities to optimize pricing strategies.

• Analyzing user reviews and sentiment analysis to understand guest experiences.

In this scenario, the data scientist would leverage machine learning algorithms, statistical modeling, and more advanced techniques to not only analyze historical data but also create models that can make predictions and recommendations for future actions.

In summary, data analysis is primarily concerned with exploring historical data to provide insights and answer specific questions, while data science involves a broader set of activities that include predictive modeling, machine learning, and the development of algorithms to extract actionable insights from data. Both roles are essential for organizations like Airbnb to make informed decisions and optimize their services.