Data Science vs Machine Learning: What’s the Difference?
Data science and machine learning are two concepts in the world of technology that use data to improve how we produce and innovate products, services, and infrastructure systems. Both correspond to highly sought-after and lucrative job options.
In recent years, machine learning and artificial intelligence (AI) have dominated sections of data science, playing significant roles in data analytics and business intelligence. Machine learning automates data processing and goes on to create predictions based on massive amounts of data from specific populations. Models and algorithms are created to do this.
Pursuing a career in either field can deliver high returns. According to US News, data scientists ranked as third-best among technology jobs, while a machine learning engineer was named the best job in 2019 [1, 2]. If you decide to learn programming and statistical skills, your knowledge will be useful in both careers.
Data science is the field of studying data and how to extract meaning from it, while machine learning is a field dedicated to understanding and building methods that use data to improve performance or make predictions for Machine learning is intelligent application has been a branch of the project.
What is data science?
Data science is a multifaceted field that combines various disciplines like mathematics, statistics, computer science, and domain knowledge.
In today's data-driven world, understanding the power of data science is crucial
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Top 10 tools use master in Data Science 2024: Click here
Careers in data science
Data Scientists: use facts to understand and explain events, helping businesses make smarter decisions.
Data Analyst: Collects, continues and research facts sets to assist solve business troubles.
Data Engineer: Design systems that accumulate, manipulate and rework raw records into facts for enterprise analysts and statistics scientists.
Data Architect: Reviews and analyzes organizational records structures to arrange databases and implement answers to shop and control information.
Business Intelligence Analyst: Collects, prepares and analyzes sales and client data, translates and affords findings to enterprise groups.
What is machine learning?
Ever wonder how your phone recommends the perfect playlist or how your email filters out spam? The answer lies in the fascinating realm of machine learning (ML).
It’s because machine learning algorithms are able to improve efficiency and accuracy when it comes to tasks like predicting outcomes or interpreting data .
In simpler terms, ML allows computers to learn from data without needing specific instructions. Imagine a program that gets better at recognizing patterns and making predictions the more information it consumes. That's the essence of machine learning!
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History of Machine learning?
1997: A Machine Defeats a Man in Chess
2002: Software Library Torch
2006: Geoffrey Hinton, the father of Deep Learning
2011: Google Brain
2014: DeepFace
2017: ImageNet Challenge – Milestone in the History of ML
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Skills needed:
Knowledge of computer science, including data structures, algorithms, and architecture.
Strong understanding of statistics and probability.
Knowledge of software engineering and system design.
Programming skills, including Python, R, and others
Ability to perform data modeling and analyze
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Difference Between Data Science and Machine Learning:
| Sr.no | Aspect | Data Science | Machine learning |
|---|---|---|---|
| 1 | Definition | Data science is a multifaceted field that combines various disciplines like mathematics, statistics, computer science, and domain knowledge. | Ever wonder how your phone recommends the perfect playlist or how your email filters out spam? The answer lies in the fascinating realm of machine learning (ML). |
| 2 | Focus | Broad focus on the entire data lifecycle, including data acquisition, processing, exploration, modelling, and communication of insights. | Focused on developing algorithms and models that can learn from data and make predictions or decisions. |
| 3 | Key Tasks | Data collection, data pre-processing, exploratory data analysis, statistical modeling, data visualization, and communicating findings. | Building and optimizing machine learning models, training models on data, evaluating model performance, and deploying models for prediction or decision-making tasks. |
| 4 | Techniques | Statistical analysis, data mining, data visualization, machine learning algorithms, and other analytical methods. | Supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning. |
| 5 | Tools | Python (pandas, NumPy, Matplotlib, Scikit-learn), R, SQL, Tableau, Power BI, Hadoop, Spark, etc. | Python libraries (Scikit-learn, TensorFlow, PyTorch), R packages etc. |
| 6 | Applications | Business intelligence, predictive analytics, risk analysis, recommendation systems, and various domains like finance, healthcare, marketing, and more. | |
| Image recognition, natural language processing, fraud detection, predictive maintenance, recommendation systems, and various other applications across industries. | |||
| 7 | Average Salary | $120,000 - $150,000 (USA) | $110,000 - $140,000 (USA) |
| 8 | Future and Job Prospects | High demand and growth expected as organizations increasingly rely on data-driven decision-making. Job roles include Data Scientist, Data Analyst, Data Engineer, Business Intelligence Analyst, etc. | Strong demand and growth anticipated as machine learning becomes more prevalent in various industries. Job roles include Machine Learning Engineer, AI Researcher, Computer Vision Engineer, Natural Language Processing Engineer, etc. |
