Data Scientist role
There are several talks about data scientists: what they do, how can I become one, do I need one for my organization. Let's see what the data scientist do and what you need to become one.
What does data scientist do?
In simple general terms, someone who can analyze the data for actionable insight is called as Data Scientist. By applying statistics and machine learning to a large amount of data, the make the predictions with strong evidence.
The general responsibilities and skills of data scientist are
- Gathering, cleaning, managing, and exploring a large amount of disparate data in order to: make predictions, build data models and algorithms, test hypotheses, and communicate the results to respective decision making personnel’s in the organization.
- Responsibility to generate evidence-based insights that can be communicated in a visual and storytelling fashion in order to aid the business in decision-making.
- They should have versed in statistics, mathematics, economics, programming, and have a solid business-focused background and acumen.
- Offer the greatest opportunities to the organization by Identifying the data-analytics problems
- They are more interested in discovery. Instead of being daunted when you hand them a huge amount of unstructured data, they’re naturally driven to find a truth within it.
How can I become Data Scientist
- Do you have degree in mathematics, statistics, computer sciences, management information system or marketing?
- Do you have significant work experiences in any of this area?
- Do you have an interest in analyzing data?
- Do you enjoy new challenges and problem-solving work?
If your answer is yes to all definitely, you will be a good and successful data scientist.
Below are the data science competencies you should develop
Tools: statistical programming language, like R or Python, and a database querying language like SQL are the basic tools and you should know how to use this tools
Statistics: should have well-applied knowledge of statistics and clear concept of distributions, statistical tests, P-value and maximum likelihood estimators etc.
Machine Learning: these skills you need to work with those companies where the product itself data-driven with huge amounts of information. You must have a clear concept of things like random forests, ensemble methods, k-nearest neighbors, etc.
Multivariable Calculus and Linear Algebra: Understanding of these concepts lead to huge wins by small improvements in projecting performance or system optimization in companies where the product is defined by the data.
Data Visualization and Communication: both of these skills are very important because you are using these skills to present all your work to both technical and non-technical audiences. Strong communicating skills define your findings clearly. Whereas in visualization skills helps to choose correct visualization tools to demonstrate your work according to your audiences.
If you are interested in hiring Data Scientist or you are looking for Data Scientist position, please contact us today!