Data Science is needed wherever there is big data. As more and more industries begin to collect data on customers and products, the need for data scientists will continue to grow.
The organization in today’s digital world, constantly looking for ways to turn that data into insight that improve business performance and profit.
Notwithstanding, the companies are hiring data scientist who well versed in business, math, statistics, and computer science to leverage bid data.
Data scientist need to build statistical model ,algorithms and present actionable insight as visualizations.
What are Data scientist Skill set ?
Statistics, Machine Learning and Programming
The basis of a data scientist’s knowledge is a good hand on statistical concepts and machine learning models.
These are the basic constructs through which a data scientist delivers insights.
Beyond that, a data scientist must be proficient in at least one programming language.
The most commonly used today is Python, but some data scientists use other languages like R, Java, or Node.js.
Data Preparation
The data preparation pipeline consists of the following steps
Access the data.
- Ingest (or fetch) the data.
- Cleanse the data.
- Format the data.
- Combine the data.
And finally, analyze the data.
Model building
The model building process involves setting up ways of collecting data,
understanding and paying attention to what is important in the data to answer
the questions you are asking, finding a statistical, mathematical or a simulation model
to gain understanding and make predictions.
Machine Learning Operations
Machine learning operations (MLops) is a work method inspired by modern development practices,
which enables data scientists to communicate better with DevOps, to create a streamlined workflow
for machine learning development. This includes automation of
processes like data ingestion, training and deployment in production.
Big Data
The majority of organizations deal with massive amounts of unstructured and structured data. It is typically the responsibility of the data scientist to handle big data operations. This typically involves preparing the data, working with multiple data sources, understanding the data ecosystem and its components.
If you have a degree, then you just need to kick it into overdrive by taking some extra courses on the following:
- Data Science approach
Statistics
Machine learning - Data visualization
- Data storytelling
- Python for data science
- R programming
- SQL
Data Science Career Path and Progression
The data Science Career Path from junior to senior data scientist varies greatly in skill level, responsibilities, daily tasks, and everyone’s favorite topic- total compensation.
Data Science Intern & Entry-Level Data Scientist
At this stage, data scientists are super raw and mainly work on developing their core technical skills, like SQL and Python. As such, tasks are generally straightforward and have a clear objective goal.
Mid-Level Data Scientist
After around one to two years of experience as an entry-level data scientist, you can transition into a mid-level data scientist role. Mid-level data scientists are advanced individual contributors that can take up larger project scopes and more ambiguous business problems.
Senior Data Scientist Career Path
The skills of a data scientist can be measured in interviews by testing speed and accuracy on technical problems, but also by evaluating communication skills. Then, on the job, many of these skills are more observable.Your value as a data scientist then corresponds to how much value you can add and the solutions you build relating to data.
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