What is the difference between Data Science vs Data Analytics?
Data science and data analytics are the trending terms of the year. Everyone knows that business success without data is unknown. Many tons of data are generated daily by the user and importance is given to carrying out the day to day activities of the business. If this data can be analyzed in any way and interpreted what the user wants and can innovate accordingly, we can bring a revolutionary system where businesses can solve problems faced by a common man and at a lower cost. This revolution is data science and includes data analytics, machine learning and more.
Data Analytics vs. Data Science
Both data analytics and data science work depend on data, the main difference here is what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and build new processes for data modeling and production using prototypes, algorithms, forecasting models, and custom analysis.
The term data analytics can be defined as the collection of raw data that can be used to analyze in a proper way to achieve business benefits. Such collection of data and receive information from multiple sources further convert it into a meaningful insight to solve business problems.
What are the skills required to become data analyst?
Data analyst should have the ability to take specific questions or topics, being able to analyze how the data looks, showing how relevant the data is to stakeholders in the company. If you are looking for the role of a data analyst, then you should have these seven core key skills:
Structured Query Language: SQL is the universal industry standard database language and is highly recommended for its data analysts. In almost all companies, the SQL database language is required, whether managing and storing data, relating to a manifold database, or building or changing a database structure.
Microsoft Excel: When we talk about Microsoft excel, every data analyst should know the basic of Macros and VBA lookups. These are still widely used for smaller lifts and lighter, quick analytics.
Critical Thinking: If you have chosen the profile of a data analyst then you should have the ability to think like an analyst. It is the role of a data analyst to uncover and synthesize connections that are not always so clear.
R or Python Programming: R and Python programming languages can work much faster than Excel. They are powerful statistical programming languages that are used to perform advanced analysis and predictive analysis over large data sets. So, it's up to you in which language you should go R vs Python.
Data Visualization: Data visualization is a technique that is used to gain insight of data in visual representation. Data visualization is the effect of your data that presents high quality charts and graphs to represent the data. Tableau visualization software is an industry specific analytics tool that features sharing and collaboration, live and in-memory data, advance visualization and many more.
Presentation skills are important because presenting information. But this skill does not come naturally to everyone. It will be a habit while practicing.
Machine learning is a key component of becoming a data analyst. While not every analyst works with machine learning, it is important to know the tools and concepts to advance in the field.
Data science has a wider scope than a data analyst. Data science is a combination of various tools, algorithms and machine learning principles, in which to search for hidden patterns from raw data. They understand data from a business point of view and can provide accurate predictions and insights that can be used to make important business decisions.
The term data science can also be understood by Venn Diagram which contain math and statistics, subject expertise and hacking skills. These three skills make you perfect data scientist.
What skills are required to become a data scientist?
Many organizations have understood the ability to make data-driven decisions. But to become a data scientist you can have desirable skills:
· Knowledge in statistics
· R/Python programming language
· Understanding machine learning algorithm
· Big Data Processing Frameworks
· Data Wrangling and Data Exploration
What are the responsibilities of Data Analyst and Data Scientist?
Responsibilities of Data Analyst:
· Writes SQL queries to find answers for complex business questions.
· Analyze and discover patterns from various data points.
· Implementing new metrics to find the best business solution.
· Identify data quality issues and partialities.
· Map and trace the data from system to system for solving a given business problem.
· Design and create data reports using various reporting tools.
· Applying statistical analysis
Responsibilities of Data Scientist:
· Identify new business questions that can add value.
· Data storytelling and visualization.
· Data Cleansing and processing.
· Develop new machine learning models.
· Correlate disparate datasets.
The difference between data science versus data analytics can actually have a big impact on a company. Data analysts and data scientists have job titles that are deceptively similar given many differences in role responsibilities, educational needs, and career trajectories.
Once you consider factors such as your background, personal interests and desired salary, you can decide which career is right for you and move on to the path of success.
