Know this before starting a career in AI/ML...

The human mind is one of the most uncanny objects to exist in the entire universe. It can perceive the environment by the simple process of visualization. This visualization enables the human mind to recollect events and uses it as an inspiration to define future events. Without this ability, we are doomed as living beings as our ability to decide the next move based on past events ceases to exist.

Inception of Data with the essence of Data Science -

Similarly, we are surrounded by facts and statistics, basic mathematics which is generally used for setting a reference or for analysis purposes. This entire process sums up to formulate the existence of data. A piece of data could be anything about the knowledge and application of its user. Unlike, the human mind which cannot store data forever due to its long-term memory lapse tendency, a computer network is a perfect platform to store data for eternity.

Adhering to the incompetence of the human mind, a computer could be used to crunch these unused silos of data existing somewhere in 3D space. Data in raw form is classified as structured, semi-structured, and unstructured datasets. The basic fundamental behind this classification is that the more structured the data, the easier it is to analyze the data because of its addressable and organizational quality. When data is analyzed to find patterns, and trends and predict events with the aid of computational methods for decision-making processes, we onboard the most trending tech field of Data Science.

The necessity for Data Crunching -

In today’s world, there is a hefty number of firms that produce and dump monumental amounts of data every year. The underlying problem with this is that this data becomes more of a liability rather than an asset to the company. Considerable effort is required to interpret data at this level which could potentially add value to the firm by inculcating a better decision-making process and innovation behind in-demand products. A hawk's eye is required to find these minute intricate details from these never-ending data packets.

Getting Acquainted with Data Science –

Data Science dates back to the 1960s when the effect of modern-day electronic computing on data analysis was predicted as an empirical science. There were several attempts to use computer science and data science interchangeably because of their extremely close common attributes. Interestingly, data science was solely defined using statistical models. Over the years, this field has diversified and evolved to incorporate new concepts of machine learning and artificial intelligence.

Essentially, a data scientist is a person who has an extremely powerful knowledge base revolving around statistics and software. In addition to understanding multiple programming languages, a data scientist needs to be equally competent in software architecture and mathematics. From defining the main problem and designing algorithms for extracting relevant data to implementing multiple software to process raw data and deriving relevant insights, data science finds application in multiple industries because of its versatile and decision-making nature.

The Pillars of Data Science –

The data scientist profile is one of the most demanded and reputed professions because it requires mastery of skill sets pertaining to the combined intersection of mathematics, statistics, and computer science to formulate data-driven business solutions –

• Mathematics and Statistics – In exact terms, statistics is a branch of mathematics that revolves around the collection, analysis, interpretation, and finally presentation of data. The essential gain after mastering this subject is to enhance the decision-making capability to produce better data-driven business solutions.

• Programming – A programming language becomes vital to translate the mathematical-based models and algorithms, for programming execution to obtain intricate data insights. Python and R are the most used languages in data science because of the high number of packages comprised by them for computation. A package is a directory that structures many modules in it. These modules contain the coding definition and statements for any language

• Modalities associated with Data – This involves the retrieval, transformation, and processing of data. For example, SQL is a programming language based on the concept of the database management system. A database management system stores data and provides access to all the linked data. Thus, data could be easily modified and retrieved using these sources and platforms.

The next step involves the transformation of data for making it accessible for analysis and processing. The transformed data is then loaded into the data warehouse where algorithms would be applied for proper analysis. Hadoop platform could be an effective entity here as large datasets could be quickly processed across clusters of computers. Scaling up and connecting servers are a few core competencies of Hadoop.

Last but not the least, it is the simplification of these complex datasets to unify them as an entity for easy analysis. As data processing commences, it becomes imperative to understand and make some sense out of the data for manipulating data jargon to procure tangible answers. This highly depends on the competency of the data scientist.

• Machine Learning and Artificial Intelligence – ML significantly helps data scientists by automating and reducing their data load by empowering machines to analyze large chunks of data. There exist certain algorithms, including supervised and unsupervised which are used for creating models to minimize the risk factor in decision making and drive maximum profits for the firm. Moreover, python programming and the TensorFlow python library are highly used in this domain.

Advanced machine learning processes like deep learning, have paved the way for mimicking the human brain and creating a large network of artificial neurons. This has enhanced the application scope of machine learning and further extended deep learning to neural networks as well.

• Data Visualisation – This holds high importance in the entire data processing journey as data visualization is pivotal for depicting data in an appealing and understandable format. These include graphs, charts, infographics, etc., which are a few options to represent a large amount of data. Tableau, ggplot, and Power BI are a few tools with an intuitive and interactive interface for visualizing data effectively.

• Business Acumen and Strategy – With all the technical knowledge aside, there is a massive requirement to understand the modalities of the business side of the company as well. This is because business problems are complicated and a data scientist needs to conduct analysis, to build infrastructure for maneuvering data according to the scale of the business problem and impact.

Amongst all these pillars, there are a few interpersonal skill sets like effective communication, the art of storytelling, diversified collaboration, and a steep learning curve that makes a person pursuing data science complete.

The Sweet Spot -

The following is one of the most revolutionary and defining Venn Diagrams to demarcate and eradicate the confusion between the spheres of Machine Learning, Data Science, and Artificial Intelligence.

Artificial Intelligence – AI has the utmost superiority in this particular domain of the tech industry. The sole purpose of AI is to instill human behavior in a computer. This is where human intelligence is fed to the machines, making them more capable of performing tasks and decision-making.

The application of AI dates back to the 1950s when Alan Turing empowered a machine to crack the famous Nazi enigma code. Moreover, he devised the Turing test which determines the intelligence level of a computer that to a human.

The reason why AI has captured the world so quickly is that big facets like machine learning and deep learning, are subsets of AI which enable the formulation of a robust problem-solving mechanism. AI algorithms are extensively used in these domains to enhance predictions based on raw input data. Artificial Intelligence could be further classified based on two main categories –

Capability-Based –

• Weak AI – This is an existent type of AI in the current world that can perform some tasks with intelligence. It is specifically trained for an individual purpose and thus is restricted for expansion.

• General AI – No system exists under this category in the present world. The purpose behind general AI is to perform a task with human efficiency and smartness.

• Strong AI – It is a hypothetical classification that aims to surpass human intelligence to perform any particular task with better cognitive abilities.

Functionality Based –

• Reactive Machines – These machines are very straightforward and react as per the current situation as they do not store past instances to predict the future, depicting the simplest form of AI.

• Limited Memory – Unlike reactive machines, limited memory machines have the capability temporarily store past instances and data for a limited period only.

• Theory of Mind – This type of classification can behave like human entities. They can comprehend the human mind socially by understanding emotions and thought processes.

• Self-Awareness – Another hypothetical concept that aims to outplay the human mind by instilling its sentiments and consciousness. The future beholds extremely smart machines with unimaginable applications.

There are innumerable applications of artificial intelligence in the modern world. From automated stock trading options to tap into the speech analytics and recognition industry during the pandemic, times have proven that artificial intelligence can leverage any opportunity and situation by delivering tremendous impact and leaving its success story behind by creating ample future opportunities.

• Machine Learning – It is a field that involves predicting outcomes based on the raw data fed into the system. ML deals with statistical models that assist in defining a decision model under the influence of predictive analytics. Moreover, ML is a subset of AI and plays an extremely crucial role in data modeling in the domain of data science.

ML is often used to bring out the best possible business outcomes. Moreover, data science becomes necessary as clean data is a basic requirement for algorithms to train and test a particular model. Thus, ML is not a subset of data science but is an integral component for building effective algorithms. ML comprises supervised, unsupervised, and reinforcement learning which could be found in our previous blogs.

Moreover, deep learning is another subset of machine learning that revolves around formulating algorithms based on artificial neural networks based on the brain's functioning. By using this concept, there have been revolutionary advancements in the domain of AI and ML. Deep learning is highly scalable and has more enhanced performances with the increase in data as compared to older learning algorithms.

Data science, ML, and AI are extremely closely related, but they are not identical. Each domain has its own set of technical ground rules, concepts, and applications. A balance of all three creates the most holistic and competent data scientist because of the extreme versatility and flexibility to work across diverse projects with different demands. Data science in totality is one of the most lucrative roles for the current and the upcoming gigs in the tech industry.

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