In recent years, the industry demand for data science professionals like data scientists or data analysts has been only matched by the equally rising demand for professionals in the field of Artificial intelligence (AI) or machine learning. According to the LinkedIn social media company, the U.S. industry is facing a shortage of over 150,000 specialists with data science skills while in India, the industry demand for data scientists has grown by over 400% in the year 2018According to IIHT, there is a 60% rise in the demand for AI professionals, while the AI industry is set to create over 2.3 million jobs by the year 2020.

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Despite the distinct role played by data science specialists as against professionals skilled in Artificial intelligence and machine learning (ML), there still exists a great deal of confusion with regards to their required programming skills for these 2 disciplines. While there are similarities in the roles performed by AI engineers and data science specialists, this article aims to highlight both similarities and differences between programming for AI and data science.

Introducing Artificial Intelligence and Data Science

Let’s start by introducing both AI and Data science and entailing what each of these technologies are equipped to perform.

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What is data science? Data science is the study and analysis of large volumes of both structured and unstructured data aimed to derive predictive and causal inference that can enable better decision-making. Data sciences use a variety of disciplines like mathematics and statistics along with techniques like data mining, predictive modelling, data visualization, and even machine learning to achieve desirable outcomes.

As a data scientist, you are expected to collect and analyse a variety of Big data to extract valuable business insights. This can include job functions such as:

Understand business needs or problems and formulate possible solutions.

  1. Develop statistical models for data analysis.
  2. Develop customized data models and algorithms.
  3. Construct predictive models to enhance customer experience and business revenues.
  4. Collaborate with product engineering teams and communicate results with business executives.

 

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In simple terms, AI is a field of computer science that equip computer systems to perform tasks typically performed by human beings. AI capability can include language processing, speech recognition, and visual perception. As a branch of AI technology, machine learning makes use of data algorithms designed for computer applications to predict accurate outcomes with minimum levels of programming.

The typical job functions performed by an ML specialist (or engineer) include:

  1. Collaborate and develop data pipelines with data engineers.
  2. Develop and improve effective machine learning models.
  3. Write and review production-line code.
  4. Analyse complex data sets and derive useful insights.
  5. Develop ML-based algorithms and code libraries.

 In the next section, we shall review the similarities and differences in software programming for these 2 disciplines and debunk some of the common myths associated with data science, machine learning, and Python programming.

Programming in Data Science versus AI/ML

Typically, the required programming skills for a data scientist includes experience in the use of Python, R, Java, SAS, and SQL database coding. Similarly, an AI/ML engineer needs to be well-versed with programming in Java, Python, and R.

Here are a few of the similarities:

Data scientistMachine Learning Engineer
PrototypingPerforms the statistical analysis following by predictive modelling and prototyping of the algorithm.Use the prototyped models and make them suitable for production by running them through software tools.
Technical modelsTranslates a business problem into a technical model.Integrate the technical model by building an API model that can make accurate predictions.
Product featuresDetermine the product features that must go into a data model.Write the actual code to implement the features.

Here are a few of the basic differences between these 2 disciplines:

Data scienceMachine Learning
Responsible forRetrieval, collection, and transformation of Big dataApply a structured approach towards Big data through searching data patterns that can be useful for business decisions.
ScopeComprises of multiple disciplines including software engineering, predictive analytics, and even machine learning.One of the many subsets of artificial intelligence and comprises of 2 algorithms for supervised and unsupervised learning.
TechniquesIncludes techniques like anomaly detection, clustering analysis, regression analysis, and classification analysisIncludes techniques like supervised clustering, anomaly detection, classification, and regression.
Professional knowledge1. Programming skills in Python, Scala, and SQL

2. Ability to work with unstructured data from social media and online sources

3. Understanding of other analytical skills including machine learning

4. Mathematical statistics for data analysis

1. Programming skills in Python and R

2. Probability and statistics

3. Data modelling skills

4.Fluent in computer fundamentals

Debunking common myths

Myth 1 about technical skills: Among the common myths about data science, programming is considered as the only skill required to become a professional data scientist. A good data scientist must be able to apply programming techniques in Python or R and write suitable library code (example, caret and numpy) to become an expert in data sciences. This is not true as data scientists require a mix of both technical and soft skills to be successful at their function. Some of the required soft skills include effective communication skills, problem-defining and solving skills, and a structured approach towards an effective solution.

Myth 2 about Deep learning: Another common myth is that deep learning is the methodology towards any data science or ML-based solution. While deep learning has been very effective in areas like computer vision and natural speech recognition, learning about deep learning frameworks like Tensorflow and Keras is not equal to gaining expertise in ML.

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As illustrated, deep learning is just one subset of ML that derives its concepts from various other fields like neural networks, information retrieval, and statistics. Deep learning is an ML algorithm that uses artificial neural networks, which are interconnected to each other and can process large data volumes.

Myth 3 about Python programming language: Thanks to its requirement in both data science and ML-related projects, Python programming is one skill that is increasingly prevalent among today’s developers. As a core language, Python is powering multiple projects including products like RedLaser barcode scanning app, the OpenStack cloud infrastructure project, and many more.

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Among the common myths about Python programming language is that it is a new programming language that only came into existence in the 21st century. The fact is that Python is over 25 years old with its first release made way back in 1991 (that is 4 years before Java). An additional myth is that Python language cannot be compiled like other programming languages like Java. Python code can be compiled using standard interpreters and compiles like PyPy and CPython.

Thanks to its flexibility, Python is being used in a variety of industry applications including online payments (example, PayPal and balanced payments), natural language processing (example, NLTK), Big data applications (example, Disco and Hadoop support), and machine learning (example, Orange and the SimpleCV computer vision platform).

Conclusion

In summary, data science is a multidisciplinary field that require both technical programming skills and knowledge of how other disciplines like statistics and machine learning operate. While data science is a generalized term that can be applied to any process that can analyse and manipulate data, a combination of AI and machine learning is instrumental in finding relevant information and insights from a large volume of Big data.

Though there is some degree of similarities and differences between data science and machine learning (as outlined in this article), both these disciplines are unique and important for today’s business enterprises. This article attempts to dispel some of the common myths related to programming in both of these disciplines.

That’s all for now, readers! We hope that this blog has provided answers you were seeking. We would love to hear your thoughts too. Don’t hesitate to leave your comments in the section below. You can also check out our Data Science course or our Artificial Intelligence course here.

Reference:

https://www.simplilearn.com/data-science-vs-data-analytics-vs-machine-learning-article

https://www.springboard.com/blog/machine-learning-engineer-vs-data-scientist/

https://www.houseofbots.com/news-detail/3875-1-devil-in-detail-data-science-vs-artificial-intelligence-vs-machine-learning-vs-deep-learning

https://www.analyticsvidhya.com/blog/2019/01/myths-data-science-transition/

https://towardsdatascience.com/general-myths-to-avoid-in-data-science-and-machine-learning-74c627fa4832

https://medium.com/paypal-engineering/10-myths-of-enterprise-python-8302b8f21f82

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