Why Python is crucial for building a career in data science
As the strong emphasis on establishing a digital world becomes stronger, data is becoming the most valuable commodity. It is considered the cornerstone of how we interact, make choices, and click a button on social media, and so on. Every second we spend in the digital domain, we generate invaluable data that drives algorithms, making our stay productive yet entertaining. This has led to the growing emphasis on mining, analysing and interpreting data to further businesses to undertake digital transformations, ensuring relevancy and profitability for all.
However, while data is widely regarded as the most important commodity around, a lack of comprehension has resulted in today’s youth becoming unaware of how to align their careers with it. For example, it is data scientists who accumulate, analyse and interpret significant amounts of data through diverse skills; to create actionable strategies that supplement business growth. This is a role vastly different from data analysts, who operate in the same domain. Many are of the opinion that the two are similar roles, and as a result, often end up learning programming languages such as C++, which are good to have skills but not crucial. On the other hand, Python is considered the bare minimum to build a career as a data scientist. The roles of these programming languages are vastly different when the business domain is concerned, which is why we will discuss why Python is crucial for becoming a Data Scientist below.
Rise of Python in recent years
As per prominent industry reports, 54% of programmers use Python in some capacity globally. Coupled with this, almost 60% of the global programming community has adopted Python as their preferred programming language in the last year alone. This demand and popularity is driven by the growing emphasis on Artificial Intelligence (AI) and Data Science, the foundation for this enhanced priority among the programmers’ community.
Furthermore, present job data in the Indian IT industry suggests that over 1 lakh Data Scientist roles are vacant at the moment, presenting a unique opportunity for youngsters to learn Python as a first preference. Apart from its versatility and simplicity, Python is the cornerstone for data cleaning, wrangling, manipulation, reports, AI, Machine Learning (ML), developing neural networks, Natural Language Processing (NLP) and Big Data. The scope of work this programming language provides remains immense, offering a future-proof aspect to all programmers.
Global Community support
Python beckons operational diversity and its numerous aspects have been critical factors for gaining the support and popularity of the global tech community. It is easy to learn, has a hyperactive and collaborative community, is flexible across disciplines, and incentivizes data science and analysis by reducing complexity and reliability. Additionally, Python offers modernistic efficiency that raises the operational bar, ease of usage for IoT technology, custom automation, and AI & ML support, aligning it with the interests of the global developers’ community. Its standard library has extensive offerings, while the availability of third-party packages for everything else makes Python the most preferable programming language for all. Unlike other languages, it does not require complex boilerplates to create basic programs, reducing time and effort.
Aspects of data handling
Analysing data is one of the most significant aspects of Data Science, and the Pandas library offered by Python is the preferred choice among programmers to interact and analyse it in diverse formats.
The role of Python is especially helpful when handling tabular data like spreadsheets that are done through data exploration, cleaning and preprocessing. The latter part of this operation is also spearheaded by Python through bifurcating duplications, handling missing values and general transformation. Through the Pandas library, Data Scientists are empowered to pivot, merge or transform data at will.
Additionally, Python’s NumPy library undertakes tasks like handling arrays, and basic mathematical solutions like addition, multiplication and others. This is done in tandem with Pandas, which selects, filters and aggregates data manipulation for efficient output. It is also particularly important to generate detailed statistical reports — a task that is handled by the efficient tools of the SciPy library that help to test hypotheses, regression and clusters. On the other hand, the Matplotlib library helps by visualising crucial data through charts, bars and scatter plots among others.
Python also offers the Seaborn library that is used to create efficient statistical graphics like facet grids and heat maps, which have layered variables. By obtaining help from the Plotly library, scatter plots and bar charts are created, along with sharable digital graphics irrespective of recipients. In addition, Python is particularly helpful in handling Big Data, a critical part of operations in Data Science. By offering its wide range of packages, Python empowers programmers to optimise work on real-time analytics, Apache Spark, stream processing and Hadoop. The Pandas, Scipy and Pyspark libraries are particularly useful in this initiative.
An underrated aspect of handling data remains providing Data Security. As we continue to generate useful yet highly private numerical, spatial or qualitative data, it is Python that is used extensively throughout the world to come up with safety measures.
From encrypting and decrypting to authentication, this programming language acts as the primary line of defence in the digital domain to keep malicious cyberattacks in check. However, Python’s extensive offerings are also helpful in developing AI, ML, Automation, Deep Learning, NLP, Web Development and a plethora of real-world applications that highlight its general significance in building a prosperous career for aspiring learners.
(The author is Founder & CEO of GUVI Geek Networks)