Data Scientist is responsible for collecting, analyzing and interpreting results, through large amounts of data. This process is used to make an important decision for the business that can affect growth and help compete with competitors in the market.
What does a Data Scientist do?
Before knowing about the skills required to become a Data Scientist, you should understand exactly what jobs a Data Scientist must do?
Here are some of the roles and tasks of a Data Scientist:
- Identify the right dataset and variables
- Identify the most challenging data analytics problems
- Collect structured and unstructured data sets, from different sources.
- Clean and validate data for accuracy, completeness and consistency
- Develop and apply models and algorithms to mine big data stores
- Analyze data to recognize patterns and trends
- Interpret data to find solutions
- Deliver results to stakeholders using visual tools
Important skills in Data Science
1. Analysis skill
As a Data Scientist, you must be able to work with tools such as statistical testing, distribution, and maximum likelihood estimation. A good Data Scientist will recognize which technique is optimal to approach the problem.
With statistics, you can help stakeholders make decisions, design, and evaluate experiments.
2. Statistical skills
Statistics will help data scientists have an overview of the data in the data preprocessing step, as well as help them present research results well to colleagues and customers. The supporting tools in statistics are usually statistical tests, distribution functions, and maximum likelihood estimates.
By understanding these tools and concepts, data scientists will choose the best technique that can be applied to their problem. With statistics, you can help stakeholders make decisions, design, and evaluate experiments.
3. Programming skills
Data Scientists must be skilled in the use of programming tools such as Python, R and database query languages such as SQL, on both computational and statistical aspects.
4. Critical thinking
Critical thinking is the use of analysis, survey, and objective estimation of a problem to make a valid and workable judgment. To think critically, Data Scientist always needs to question everything they hear and read, focusing on the important aspect of the problem and leaving out irrelevant details.
5. Knowledge of Machine Learning, Deep Learning and AI
Machine Learning is an area of Artificial Intelligence (AI) that uses statistical methods to give computers the ability to learn from data. Self-driving car technology, voice recognition, efficient web search are all possible.
Deep Learning is a branch of Machine Learning in which data is transformed through many nonlinear transformations before an output is obtained. AI is based on the idea of the ability of computers or computer programs to think, understand, and learn like humans. Data Science has an intersection with AI but is not an area of AI.
6. Knowledge of Python, R, SAS and Scala
Being a Data Scientist requires good knowledge of Python, SAS, R and Scala languages.
7. Presentation skills
Data Scientist needs presentation skills to communicate effectively with stakeholders. They are the people who stand at the intersection of business, technology and data.
Skills such as eloquence and storytelling enable them to translate complex technical information into something simple, understandable, and accurate to colleagues or business leaders.
8. Data preprocessing skills
A lot of data is cluttered. Values may be missing, may have inconsistent formatting. As a result, Data Scientist will need to clean up and reorganize the data.
9. Data visualization
Data visualization is a graphical representation of data to convey relationships between data features. This is an essential part of data science, as it allows the data scientist to describe and deliver their results to colleagues and clients.
The data scientist should be proficient in one of the tools like Matplotlib, ggplot, d3.js, or Tableau.
10. Ability to work with unstructured data
Unstructured data is information that has no predefined data model or is not organized in a predefined way. Unstructured information is often text-heavy, but can also contain data such as dates, numbers, and events. Skills in working with unstructured data are a plus for data scientists.
Above are some of the skills needed to become a Data Scientist. With all the skills mentioned above, you are ready to join the Data Science industry in the future.
Informaiton of this article is summerized from serveral sources: Data Flair, Data Camp