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Data Science vs. Machine Learning

Data Science vs. Machine Learning

by Aishwarya Gaikwad

If you are wondering how to use this work-from-home time to upskill with relevant certifications, go for a Data Science course. A Data Science Bootcamp takes care of the necessary training in the fundamentals and teaches you the skills for a future in Data Science. Indeed Hiring Lab found that technology jobs are witnessing a rapid recovery, and this is a great time to prepare for the slew of Data Science job postings sure to hit the job market in 2021 and 2022.

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The global Data Science market is growing at a tremendous pace. While an earlier report from Markets and Markets placed conservative estimates of 30% CAGR  of Data Science platform market during the forecast period of 2019 to 2024, a more recent estimate from the Mordor Intelligence’ Data Science Market Forecast Report expects a CAGR of 39.7 % during the forecast period, 2021-2026. 

What do these figures say? Between 2019 and 2020, the Data Science market grew phenomenally. The galloping CAGR projections indicate the growing use of data-intensive business strategies by enterprises.

Data Science is the future of the data-driven world. Be it education, life sciences, research, business, manufacturing, or other, the savvy IT professional must prepare for a future in Data Science.

What is Data Science  

Data Science is all about data discovery:  extracting, analyzing, visualizing, and managing data for insights. Data Science adopts a multidisciplinary approach to discover trends and patterns in the data. It uses mathematics, statistics and computer science, analytical techniques, and domain knowledge to uncover insights. It enables predictions, forecasting, and optimization strategies to monetize the insights for a competitive advantage.

The use of Data Science helps to figure out “what” happened and “how” it happened and to predict “what will” happen and “what to do” for desired outcomes to happen! Data Science begins by understanding the problem, gathering the required raw data and performing ETL (extract, transform, load) on the same, and testing models to design solutions.

Data Science involves the use of Machine Learning (ML) to model products for improved customer experiences. For instance, Recommendation Systems by movie booking apps provide personalized suggestions based on previous customer behavior. In the health sector, ML works with Data Science for the early detection of tumors. Thus, we see how ML complements Data Science to achieve superior outcomes: better customer experiences and operational efficiencies.

How does it differ from Machine Learning?

Data Science and Machine Learning (ML) are closely related. However, they have different functionalities and goals, and any IT person wanting to pursue a career in either of these fields must understand the difference between the two.

Data Science is a field of study that fine-tunes the process of drawing insights from raw data. It uses various tools, statistical models, and ML algorithms to prepare and crunch the data. Whereas ML is part of Artificial Intelligence (AI), the theory and training of computer systems to auto simulate human intelligence. ML trains machines to learn from past experiences on their own to improve performances and predict outcomes. It uses techniques of iterations and statistical methods to perform a given task without being explicitly programmed.

Data Science requires a good knowledge of the intersection of AI and ML for data-driven decision-making. ML calls for fine-tuning of models using algorithms for a competitive advantage. For instance, Data Science crunches customer data in an e-commerce company to understand user habits. It is ML, however, that generates cross-offers and intuitive product or pricing recommendations, for a longer customer lifecycle value.

What do they do?

Data Science uses various methods, algorithms, processes, and tools to extract insights, whereas Machine Learning uses supervised, unsupervised, and reinforced learning. In both, we learn from vast data sets, i.e. using supervised learning.

Data manipulation

Data Science understands the data and manipulates it to create models. It can work with any type of raw data, structure, or unstructured. ML works with prepared and structured data.

End goals

The goal of Data Science is to discover hidden patterns for decision-making and product development. ML models the data for making predictions and classifying the results. The end goal is for the machine to learn on its own from historical data and iterative behavior.

Applications

Data Science – customer insights, transport management, risk detection, route optimization in logistics, fast Internet allocation, election analysis, and so on.

Machine Learning – Chatbots, email spam filtering, product recommendations, online fraud detection, social media analysis, and so on.

Job Roles

The Data Science practitioner may be a Data Scientist, Data Analyst, Data Engineer, Data Science Generalist, Data Architect, or ML Engineer. An ML practitioner is usually an ML Engineer.  

Skills required

A Data Scientist must have skills related to Big Data and knowledge of multiple programming languages like Python, Java, C++, SQL, and Scala. Experience working with Big Data tools like Hadoop, Spark, Hive, and Pig, and software like R. Tableau and SAS are preferred qualifications. A deep understanding of mathematical and statistical concepts is necessary. Knowledge of ETL, statistical pattern recognition, predictive modeling, Machine Learning Algorithms, Data Mining, Database Programming, and Visualisation are other must-haves. 

An ML Engineer must have strong computer programming skills, including Python, Java, Scala, and SQL. Experience in R software, Docker, and NLP are preferred qualifications. It is necessary to have a deep understanding of computer science fundamentals, statistical concepts and probability. Knowledge of data modeling, cloud computing, and implementation of Machine Learning Algorithms are a must. 

Job responsibilities

Data Science practitioner – data handling, cleansing, classifying, mining, visualizing, and analyzing the trends and patterns in the data, besides deciding the data prototype. 

Machine Learning Engineer – managing the algorithms and mathematical concepts behind modeling and fine-tuning, manipulating computer systems to self-learn and perform given tasks 

Conclusion 

Now that you have understood the difference between Data Science and ML, and what a Data Science job role involves, get ready to pursue a Data Science course and watch your career take off. Both Data Science and ML require different skill sets, and opting for a Bootcamp specialized in Data Science training can help you stand out in the job market.

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