“A data scientist in the evolving field of data science is always ready to take different challenges, provide an unbeatable solution, grows with the demands of technology and adds a value to the business.” A data scientist needs to have an aptitude for mathematics and a creative, problem-solving mentality. The data scientists have the ability to work with large unstructured data sets. The data scientist takes & analyzes the raw data, applies analysis tools & techniques and converts it into end results that become easy to understand by any organization.To extract such valuable insights from a raw data, a data scientist needs a blend of skills like knowledge of statistics, algorithms, programming and analytical skills.
The process of Data Analysis
- Phase 1: Prepare the data: The raw data is captured or acquired and extracted.
- Phase 2: Maintain the data: The extracted data is stored and staged.
- Phase 3:Model Development: A data model is developed to test.
- Phase 4: Analyze: The model is analyzed and explored with experiment data to yield the desired results
- Phase 5: Visualize: The model’s predictions are presented visually to clearly understand and communicate the desired goal to the organization.
- Phase 6: Applying to the Problem: Through the application, the end users or organizations are assisted to understand the usage of predictions for their business.
Main Goal of the Data Scientist:
- To understand the best-fit data model for improved analysis
- To develop and implement statistical learning models based on their research problem
- To collaborate with other fields or areas like artificial intelligence & machine learning to find better solutions
- To connect and communicate the analysis outcomes with the organization’s authority or decision-makers
Above all, a data scientist must be up-to-date with the latest technology to handle and manage the organizational challenges.
Skills to Become A Data Scientist
Data scientists must have the following skills to excel in their career:
- Programming languages: Understanding of basic programming languages such as Hadoop, MATLAB, Python, and SQL.
- Analytical skills: Ability to gather, extract the valuable insight from the data and analyze the results of the developed model in order to provide wide range of solutions to the business problems.
- Mathematics & Statistics: In-depth knowledge of database systems, data mining, data modelling &visualisation and statistical methods.
- Communication and Presentation Skill: Excellent written and verbal communication required to express and present the findings or solutions to the stakeholders.
- Teamwork Spirit: Understanding with all stakeholders is a must to identify the problem, development of the product and delivery of the product that customer wants.
Open-Source Data Science Tools to Learn
- Data Mining and Transformation Tools: These tools are used to know and identify patterns in large data sets. These are also used to collect, extract, store and analyze data or information. Examples are Weka Tool, Scrapy, Pandas etc.
- Data Analysis and Big Data Tools: These tools are used to do analysis on the collected and processed data for further model training and refining predictions. Examples are KNIME, or Konstanz, Hadoop, Spark, Neo4J etc.
- Model Deployment Tools: These tools are used to develop machine learning-based probabilistic or logical models. Examples are TensorFlow.js, MLFlow etc.
- Data Visualization Tools: These tools are used to create interactive visual interfaces required for the users. Examples are Orange, With D3.js or Data-Driven Documents (D3), Ggplot2 etc
- Development Environment or IDE Tools: These tools are similar to the programming platform where the users can write code, test their code, and find the errors easily. Examples are Jupyter Notebooks, Zeppelin Notebooks, R Studio’s etc.
Data Science Applications
There are many applications that are based on the concepts of Data Science. One of the major applications is in Healthcare to detect tumors and artery stenosis using Medical Image Analysis, Advance level of treatment through Genetics & Genomics, Creating virtual assistance and customer support for users or patients, Development of Drugs etc. Other applications include Speech Recognition Tools like Siri, Google Voice, Cortana, Website Recommendations, Gaming & Virtual Reality, Advance Image Recognition, Fraud and Risk Detection etc.
Future of Data Science
The field of Data Science is assisting the organization in identifying their market value, making better decision-makers and offering their services effectively to the customers. The rate of hiring of data scientist with their intensive skills is very high in e-commerce, finance & retail sector. Transport and telecom Industries also target their customer with exciting products and services. Data science is playing a vital role in making sales and operation decisions for Oil and Gas companies. The Bureau of Labor Statistics has predicted that the job growth of data scientist will be three times faster than all other jobs.