Artificial Intelligence (AI) and Data Science are two highly popular technology domains that have, and continue to, disrupt all spheres of our lives. They often overlap but are different in concept and their application. Aspirants who want to make a career in these domains need to understand the distinctions between the two domains and the various job roles in each.
In today’s digital world, data is constantly being generated at a colossal magnitude. What Data Science does is to help businesses derive meaningful insights from their massive data sets to make informed decisions, based on reliable analytics and improved forecasts. Aspirants must be proficient in collecting, cleaning, and refining data, crunching it to spot trends, patterns, and relationships within it, presenting data analytics and using the derived insights to solve business problems.
Data scientist: Works with large sets of structured and unstructured data to derive meaningful actionable insights.
Data engineer: Integrates, consolidates, and cleans the data collected from various sources and transforms it into a form that can be easily used by data scientists.
Statistician: Uses statistical methods and tools to collect, analyse, interpret and report conclusions about data.
AI is focused on creating applications and machines that are capable of emulating human-like intelligence, including carrying out cognitive activities with no or little programming efforts. Data is used here to train the algorithms that power AI models. Professionals are responsible for defining and testing AI models, identifying data sets to train the defined AI models and driving the required insights/analytics/forecasts for businesses.
Natural Language Processing Scientist: Designs, develops and enhancesextends NLP applications and products.
Machine Learning Engineer: Designs ML models, trains it using appropriate data-sets and produces predictive analytics and forecasts.
Deep Learning Engineer: Develops and applies ML and Deep Learning solutions, e.g. developing data pre-processing pipelines, modelling, training state of the art deep neural networks (CNNs/ RNNs/ LSTMs/ Transformers).
The skills common to both include proficiency in one or more of the programming languages like Python, C++, R, or C, a good understanding of high-school Maths, and knowledge of statistics and probability. However, each field requires certain unique skill sets:
Data Science aspirants must gain an understanding of data structures, storage and warehousing using tools such as MS Excel, traditional databases like MS SQL Server, Oracle, and others; and Big Data technologies like Hadoop and Hive. They must be proficient in Data Reporting and Visualisation using tools such as PowerBI, Tableau, SAS, MATLAB, ggplot2 and technologies like Apache Scala, Spark, and scripting languages such as VBScript, Bash, SQL.
Artificial Intelligence aspirants must gain expertise in Machine Learning models and algorithms, an understanding of tools and libraries such as PyTorch, TensorFlow, Kera, Scikit-Learn, NumPy, and platforms such as AWS, Fractal Analytics, Google Cloud Platform, H2O AI, MS Azure, SAS.
With increasing popularity and demand in these domains, the biggest names in the technology world such as AWS, Google, Microsoft, IBM, Oracle, RedHat, and others offer well-defined learning paths leading to industry-recognised certifications. The easiest and quickest way for graduates and professionals looking to make a career in Data Science or AI field would be to take advantage of the wide choice of training options available from these big players.
The writer is CEO, SpringPeople Software Pvt Ltd
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