The need for Data Science is growing rapidly and companies are investing large amounts to get talented data scientists into their organizations to get their work done.
With the increase in the amount of data, the need for experts to identify patterns and to derive meaningful insights from them is increasing day by day. Harvard called data scientists the Sexiest Job of the 21st Century.
What it takes to be a Data Scientist:
Anyone from any educational background and any work experience can be a data scientist. But the person needs to be an expert in these fields to be a better data scientist.
- Business/Domain
- Mathematics (statistics and probability)
- Computer science (software/data architecture and engineering)
- Communication (written and verbal)
Have you ever thought, about what people having Data Science expertise need to do during their working hours?
Here is a list of some of the common tasks done by a data scientist:
- Prediction (prediction of output based on inputs)
- Classification (important or not)
- Recommendations (based on user activity such as Netflix)
- Pattern detection and grouping
- Anomaly detection (fraud detection)
- Recognition (image, text, audio, video, facial, …)
- Actionable insights
- Automated processes and decision-making
- Scoring and ranking
- Segmentation (demographic-based marketing)
- Optimization (risk management)
- Forecasts (sales and revenue)
Data Science Working Methodology:
- Set a goal and identify the opportunities
- Identify, acquire and prepare the data
- Explore, validate and improve the data
- Get insights, take actions and make decisions based on the data
- Monitor, analyze, and improve the data
The above process is called GABDO Process Model.
G: Goal, A: Acquire, B: Build, D: Deliver, O: Optimize
Tools used by the Data scientists:
- Programming languages such as Python, R, SQL, Java, Julia, and Scala.
- For statistics, mathematics, algorithms, modeling, and data visualization include Scikit-learn, TensorFlow, PyTorch, Pandas, Numpy, and Matplotlib.
- MySQL, PostgreSQL, Redshift, Snowflake, MongoDB, Redis, Hadoop, and HBase.
- APIs are an important part of the data scientists toolbox.
Tons of data are collected these days and getting insights from them is the most important thing to do, to make major decisions, and to get significant business changes. It also leads to customer success and makes the customer get whatever they are looking for at an ease.
“The goal is to turn data into information, and information into insight.”— Carly Fiorina
Taking into consideration Harvard’s opinion, it is one of the sectors that is having major growth these days and will continue to grow in the future, so one should achieve skills in data science to get into this field.