Data analysis is the process in which data is explored in order to discover patterns that help us make business decisions. It is one of the subdomains of data science. Data analysis methods and tools are widely utilized in several business domains by business analysts, data scientists, and researchers. Its main objective is to improve productivity and profits. Data analysis extracts and queries data from different sources, performs exploratory data analysis, visualizes data, prepares reports, and presents it to the business decision-making authorities.
On the other hand, data science is an interdisciplinary area that uses a scientific approach to extract insights from structured and unstructured data. Data science is a union of all terms, including data analytics, data mining, machine learning, and other related domains. Data science is not only limited to exploratory data analysis and is used for developing models and prediction algorithms such as stock price, weather, disease, fraud forecasts, and recommendations such as movie, book, and music recommendations.
The roles of data analysts and data scientists
A data analyst collects, filters, processes, and applies the required statistical concepts to capture patterns, trends, and insights from data and prepare reports for making decisions. The main objective of the data analyst is to help companies solve business problems using discovered patterns and trends. The data analyst also assesses the quality of the data and handles the issues concerning data acquisition. A data analyst should be proficient in writing SQL queries, finding patterns, using visualization tools, and using reporting tools Microsoft Power BI, IBM Cognos, Tableau, QlikView, Oracle BI, and more.
Data scientists are more technical and mathematical than data analysts. Data scientists are research- and academic-oriented, whereas data analysts are more application-oriented. Data scientists are expected to predict a future event, whereas data analysts extract significant insights out of data. Data scientists develop their own questions, while data analysts find answers to given questions. Finally, data scientists focus on what is going to happen, whereas data analysts focus on what has happened so far. We can summarize these two roles using the following table:
|
Features |
Data Scientist |
Data Analyst |
|
Background |
Predict future events and scenarios based on data |
Discover meaningful insights from the data. |
|
Role |
Formulate questions that can profit the business |
Solve the business questions to make decisions. |
|
Type of data |
Work on both structured and unstructured data |
Only work on structured data |
|
Programming |
Advanced programming |
Basic programming |
|
Skillset |
Knowledge of statistics, machine learning algorithms, NLP, and deep learning |
Knowledge of statistics, SQL, and data visualization |
|
Tools |
R, Python, SAS, Hadoop, Spark, TensorFlow, and Keras |
Excel, SQL, R, Tableau, and QlikView |
Now that we know what defines a data analyst and data scientist, as well as how they are different from each other, let's have a look at the various skills that you would need to become one of them.
