Imagine you are a CEO in an ecommerce platform and you want to understand what your customers think about your services. First you need to do a survey to get views and opinions from your customers. Next step will be to explore your data to draw meaningful insights. The insights will now inform how the customer service will be improved.
That is data science for you. Let's get started and understand what this term "data science" is all about.
Data science is the process of drawing meaningful insights from large data sets. It acts as a third eye by foreseeing problems and creating solutions before they occur. This is how it acts as a third eye.
Based on the field you're in, data can help you find solutions to your problems. For instance, in an ecommerce company, data can help you know:
- What products are more preferred by customers
- How to improve customer service
The roles of a Data Scientist
As a data scientist in an organization, your roles will range from:
- identifying crucial areas for research
- knowing where to get your data from
- data cleaning, data exploration
- creating statistical models
- presenting the findings to stakeholders
Data science is crucial in the decision making in all aspects of our daily lives. The following are some applications of data science in various sectors:
Education: Data science is being used by teachers to assess the comprehension of students in various units in order to come up with better teaching mechanisms to improve the students' performance.
Business: Data science is used in businesses to predict future market trends, improve quality of products based on customer preferences and enhance product marketing based on customer previous purchase history and browsing behavior.
Meteorology: Data science is applied in this sector to improve the level of accuracy in weather forecasting to help save lives from extreme weather events.
Transportation: In the transport sector, data science is applied to help optimize routing, improve safety, and reduce emissions.
Energy: Data science is used in energy to optimize energy production and distribution, reduce costs, and improve efficiency.
To be a Data Scientist, you require skills in the following fields:
Programming languages :As a beginner you need to learn the basics of programming. In data science some of the programming languages include:
You need have a foundation in calculus, linear algebra and statistics. Calculus is useful in learning how to design optimization algorithms for machine learning. Learning linear algebra helps you work with vectors and matrices. This will be very crucial in conducting analysis on data.
This is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic how humans learn, gradually improving accuracy.
You might wonder where to get the resources to start learning. There are numerous resources available to get you started, including online courses, YouTube videos and Bootcamps. The following are a few resources you can check out:
Here are a few tips for you to accelerate your learning:
Start with the basics- Try to learn only some things at a time. Focus on learning the basics of programming, statistics, and machine learning first.
Practice regularly- The best way to learn data science is by practicing. You can check out Hackerrank and kaggle for practice.
Always reach out for help- There are many online communities and forums where you can ask questions and get help from other data scientists.
Data science is a dynamic and rewarding field that opens up countless opportunities. Whether you're driven by curiosity, the potential for a lucrative career, or the desire to make data-driven decisions, data science has something to offer. With a growing job market and diverse roles, such as data wrangler, machine learning engineer, and business intelligence analyst, there's a niche for everyone in this exciting field.
As you embark on your data science journey, remember that continuous learning and hands-on experience will be your best allies. Enjoy the adventure!