Data science is a field that combines knowledge of mathematics, statistics, and programming skills to extract useful insights from raw data. These knowledge and skills help in analyzing and organizing data to discover new information and predict future events. The data is garnered from smartphones, customers, sensors, the web, and other sources.
Data science helps businesses process this data, understand it, extract value from it, and communicate it. As a data scientist, you should present your findings and offer useful insights that businesses can rely on to make informed decisions. Such decisions help businesses to create more innovative products and services.
Since data science will form a part of many future businesses, it’s necessary to know how it can add value. Read on to find out more…
What Does a Data Scientist Do?
Over the past decade, data scientists have become a crucial asset in most organizations. Their duties revolve around building algorithms to organize and process data. To become a successful data scientist, you need experience in leadership, communication, and technical skills. Good communication allows you to convey your findings to stakeholders within an organization. Typically, as a Data Scientist, you are required to:
- Ask the right questions during data collection and gathering
- Operate with stakeholders to come up with better ways to use business data for valuable solutions
- Explore different ways to get new data sources and assess their accuracy
- Data investigation and exploratory data analysis to simplify and improve product development, marketing methods, and business processes
- Create one or more data models and algorithms
- Use data science techniques, like statistical modeling, Machine Learning, and artificial intelligence to improve customer experience, revenue generation, ad targeting, and more
- Create the organization’s test model quality and A/B testing framework
- Cooperate with various technical teams to implement models and monitor results
- Make the necessary adjustments based on feedback
- Repeat the process to solve a new problem
Uses of Data Science
Data science helps businesses achieve goals that required more time and energy in the past. Here are seven examples of the way organizations can benefit from using data science.
With the help of data science, medical professionals can better understand diseases, offer diagnosis, and provide preventive medicine with accuracy. For example, clinical databases and fitness trackers offer real-time data about patients. This data can be processed to reveal underlying health problems. Health experts can then use the findings to know the best approach to treatment.
While using the internet, you don’t want to expose yourself to malware or other cybersecurity threats. Cybersecurity firms use Machine Learning (ML) to identify and prevent these threats. Coders and programmers can rely on data science to improve their techniques, create better programs to protect against cyber-attacks.
Companies such as Volkswagen and Tesla are working on predictive analytics for autonomous vehicles. The technology relies on tiny cameras and sensors to send data for processing in real-time. Machine learning technology is used to process the data. The processed data is then converted to commands, which control aspects like speed, collision avoidance, lane changes, and even picking the shortest route.
If you use streaming platforms like Spotify and Netflix, you probably get recommendations that match your taste. The streaming companies use data science to curate a list of songs or films based on your past activity. For example, if you are into cars, the Netflix algorithm will recognize your need for such content and recommend relevant shows.
UPS relies on data science for efficiency during delivery. The company uses on-road integrated navigation and optimization tools. These tools are backed by data science and create routes considering traffic, weather, construction, etc. By using data science, it’s estimated the company saves more than 30million gallons of fuel annually.
The financial industry saves millions of dollars by using data science. For example, banks use natural language processing to extract data from thousands of credit card agreements. That would require hundreds of thousands in labor hours. However, by using algorithms, the task is completed in a few hours. Besides, companies like PayPal use Machine Learning tools to detect and prevent fraudulent activities.
StreetLight uses Machine Learning to model traffic patterns for cars, bicycles, and pedestrians on various streets in America. Based on the influx of trillions of data from in-vehicle navigation devices, smartphones, and more, streetlight’s traffic maps are updated regularly. Besides map apps, there are granular that can, for instance, identify groups of commuters that use a number of transit modes to get to their destination, like a train followed by a scooter. This helps with city planning, as planners can come up with multiple transit designs.
Skills and Education Required To Become a Data Scientist
Here are a few of the pillars and skills you need to get started as a data scientist.
- Strong soft skills: These are personal attributes that help you to interact with other people effectively. Top of the list in this category is communication, both written and verbal. As a scientist, you should be able to deliver results in an understandable, insightful, and compelling manner.
- Computer programming skills: A data scientist must also have experience in programming skills, statistics, mathematics, and probabilities. With a combination of these skills, you’re better placed when it comes to choosing the right approach to analyze data.
- Education: You can become a data scientist without having a traditional university degree. You can find certification programs that will take you through the key stages to becoming a data scientist. However, individuals with a computer science degree have an added advantage. Many universities also offer data science or analytics-specific programs, which are for master’s students.
- Besides certifications and a degree program, you can also join boot camps or other online learning platforms that offer varied data science courses. Regardless of the path you choose, a data scientist greatly dependent on technical skills, especially in statistics, computer science, and mathematics.
There are many areas in businesses where data science is applicable. Companies collect a ton of data that requires processing to extract meaningful information. Data scientists assist such companies in interpreting and managing data as well as resolving complex matters using expertise in a variety of data niches.