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Data science has been called the sexiest job of the 21st century. But what exactly does a data scientist do? In this comprehensive beginner‘s guide, we will demystify data science and explain exactly what it entails.
A Simple Definition
Data science is the field of studying data to extract insights and make data-driven decisions. It incorporates skills from mathematics, statistics, programming, problem-solving, capturing data, data cleaning, data visualization, machine learning, and more. The goal of data science is to gain actionable insights from raw, unstructured data that can inform business strategy and outcomes.
The data science process typically follows the following pipeline –
- Data Capture and Collection
- Data Cleaning and Exploration
- Model Development and Training
- Model Evaluation and Improvement
- Model Deployment for Organizational Use
Let‘s explore each of these stages in more detail:
Data Capture and Collection
This step focuses on identifying relevant datasets, capturing and collecting data from disparate sources, and storing data in databases.
Data Cleaning and Exploration
Also known as data wrangling, this step prepares raw data for analysis by cleaning, structuring, enriching, and formatting data. Exploratory analysis helps understand data distributions and relationships in the dataset.
Model Development and Training
Data scientists develop machine learning models that can learn patterns from data and make predictions. Models are trained using training datasets to optimize their parameters.
Model Evaluation and Improvement
Models are thoroughly tested on test datasets to evaluate their performance. The evaluation metrics and business requirements determine if further model improvement is required.
Model Deployment
Once models are approved, they are deployed into production within business applications and processes to enable data-driven decision making.
Now that we have understood the overall pipeline, let‘s go deeper into understanding some of the fundamental building blocks.
Key Concepts in Data Science
Some technical concepts that are very common in the data science domain include –
Big Data – Massive, high velocity, complex datasets that traditional computing techniques cannot efficiently store and process. Examples – social network data, web logs, IoT sensor data.
Machine Learning – Algorithms that can automatically learn patterns and insights from data without explicit programming. Helps uncover hidden insights. Two types of techniques:
- Supervised learning – Uses input and output data to train predictive models
- Unsupervised learning – Finds hidden patterns and groupings without training labels
Deep Learning – A subset of machine learning based on neural networks, helps tackle complex problems like image recognition, language translation, anomaly detection.
Data Mining – Using algorithms and techniques to identify interesting, non-trivial patterns in data. Can be descriptive or predictive.
Data Visualization – Using visual representations like plots, dashboards, and infographics to better understand trends and communicate insights from data analysis.
Business Analytics – Applications of strategies and technologies to gather, store, access, and analyze business data to help corporations make better business decisions.
Predictive Analytics – Using historical data and analytical techniques like statistical modeling and machine learning to identify risks and opportunities by predicting future outcomes.
Why is Data Science Important?
We live in an increasingly data-driven world. For modern organizations across sectors, data has become one of the most vital assets to accelerate growth. Data science empowers organizations to –
- Optimize processes for efficiency
- Enhance customer experiences
- Develop innovative products
- Identify new revenue streams
- Minimize risks in decision-making
With fierce competition, organizations cannot afford to make decisions based on gut feel and assumptions anymore when data can provide objective, calculated recommendations. Data science provides the tools and technologies for smart, evidence-based decision making.
According to reports by LinkedIn, data science jobs have grown over 650% since 2012. Companies are not only hiring more data scientists but also integrating data science throughout business functions.
Data science does not just have business impacts. In sectors like healthcare, data science is helping accelerate medical research and improve patient outcomes through better diagnostics and treatment plans. Data science also has applications in education, public policy making, transportation and urban planning, and more.
What Does a Data Scientist Do?
Now that we know what data science is and why it matters, what exactly does a data scientist do on a day-to-day basis?
Data scientists don‘t spend their days just building fancy machine learning models! Rather, they focus on understanding business problems, and identifying how data can provide solutions.
Here are some of the key responsibilities of a data scientist –
- Deeply understand the key business priorities and problems of an organization
- Identify what types of data analysis can yield relevant insights to solve these business problems
- Collect, clean, and validate relevant datasets from diverse sources
- Perform in-depth exploratory analysis using statistical techniques to surface patterns, trends and interconnections in data
- Select and try different data science techniques like predictive modeling, data mining, etc. based on the problem statement
- Develop machine learning models to automate certain prediction tasks
- Rigorously evaluate models for robustness, accuracy and other relevant metrics
- Clearly communicate data insights and recommendations to key stakeholders with the help of data visualization
- Monitor model performance and retrain models when new data comes in
Data scientists have to combine business acumen, analytical skills, statistical knowledge and programming skills with effective communication and storytelling abilities.
Data Science Skill Set
With data science finding applications across domains, professionals from diverse backgrounds are transitioning into data science. While previous experience in data analysis, statistics or software programming provide an advantage, professionals from non-technical backgrounds can also become data scientists.
Here are some fundamental skills you need to have:
Math and Statistics – Having a foundation in math, statistics, linear algebra, and probability is key to quantitatively analyzing data and building models
Programming – Scripting languages like R and Python with their specialized data science libraries are must-have programming skills
Data Wrangling – The ability to collect, clean, restructure and enrich raw data from diverse sources.
Analytics and Algorithms – Applying techniques like machine learning, predictive analytics to uncover patterns and build models.
Data Visualization – Using impactful graphs, charts, and visual assets to effectively communicate critical findings and insights.
Databases and Big Data – Understanding relational databases and newer big data technologies to manage large, unstructured datasets
Subject Matter Expertise – Domain knowledge of the industry you are working in is invaluable to formulate relevant hypotheses and validate findings.
Aspiring data scientists – don‘t be intimidated thinking you need advanced degrees. While academic credentials help, hands-on experience, online courses and data science certifications can be great alternatives to launch or advance your career.
Real-World Applications of Data Science
While internet giants like Google, Facebook, and Amazon are known for their use of data science, data science is truly revolutionizing every domain. Here are some common applications:
Retail – Product recommendations, forecast demand, pricing optimization, customer segmentation, store layout optimization
Banking – Predictive analytics for risk modelling, algorithmic trading, dynamic credit limits, fraud detection etc.
Insurance – Automated claim processing, predictive analytics to forecast losses, personalized premiums, fraudulent claim detection
Healthcare – Predict outbreaks, improve diagnostics with AI models, optimize hospital operations, develop personalized medicine
Media – Personalized content recommendations, sentiment analysis, predictive analytics to foresee viewer interest
Government – Inform public policy decisions with socio-economic insights, evidence-based governance, smart cities applications
And these are just a few examples! Data science applications are constantly evolving as organizations integrate data insights into innovative products and services.
The potential impact data science can drive makes this an incredibly exciting time for aspiring data science professionals to jump in.
Frequently Asked Questions
How much do data scientists earn?
Data science is one of the hottest career options today. According to Glassdoor, in 2022 the average base pay for a data scientist in the US is $120,931 per year with average cash bonuses of $18,244. With growth opportunities, salaries can go up to $200-300K for experienced leadership roles.
What academic background do you need?
Most data scientists hold a Bachelor‘s degree in STEM fields like Computer Science, Applied Mathematics and Statistics. Higher education like a Master‘s degree or PhD focused on Machine Learning, Data Science etc. is preferred for senior roles. Hands-on experience and certifications can supplement academic credentials.
What industries employ data scientists?
Every modern data-driven organization employs data scientists but some top hirers are tech, information services, finance, insurance, healthcare, academia and government agencies.
How long does it take to become a data scientist?
With full-time, focused learning, it takes 6-9 months to learn fundamental skills from scratch. But most employers prefer 2+ years of hands-on work experience on real projects. Some academic programs are 1-2 year Master‘s degrees. Learning data science is continuous as technologies progress.
What is the future outlook for data science careers?
The Harvard Business Review has termed data science the sexiest job of the 21st century! With businesses relying more and more on data insights, data science jobs are predicted to grow by over 28% from 2016 to 2026 per BLS statistics. Top technology trends like Internet of Things (IoT), AI, Big Data will drive demand. Career opportunities are excellent for skilled data science professionals.
Conclusion
I hope this beginner-friendly introduction helped you understand – what is data science, why it matters, what data scientists do, key concepts involved, applications across industries, and the promising career growth. With information being called the new oil, data science professionals hold the keys to extract powerful insights from this invaluable resource.
To summarize, data science incorporates skills from mathematics, statistics, programming, databases, machine learning, and domain experience to extract impactful, actionable insights from data. Data science helps drive smarter decision-making to give companies a competitive edge.
If you are motivated to learn data science, online education platforms offer great resources to start your journey today. Real-world expertise will come from working on projects and building a portfolio.
The potential to drive business impact through data is unlimited. As data science advances, so will professional opportunities in this space. Take the first step and begin upskilling if you want to ride this next wave of innovation.
Good luck and happy learning!