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What Makes Python a Quick Pick for Data Analysis and Data Science?

  • Aileen Scott 

Python comes across as an object-oriented high-level programming language with dynamic semantics that allows rapid application development. It has become a general-purpose programming language for a number of reasons.  It is the ready pick for data science enthusiasts; who look forward to majoring in the field with the requisite essentials. Not just that, Python has been ranked No.1 (by TIOBE) in the wide array of programming languages doing the rounds worldwide.

Programming developers with core Python skills are a hot pick among recruiters from agencies ranging from government to financial institutions to tech giants. Today’s world is commanded by easy data tool access and natural propositions in the processes with tech advancements in the form of ace programming languages set rightly in place.

Making it a highly competitive ground; Python just fits in well raiding the entire landscape of the tech weeds smoothly. The moment you look forth to becoming a data science professional that could command major business decisions with accurate futuristic insights- Programming takes the main stage.

Although; it is not a prerequisite to possess an understanding of tech skills before you dive into the field, a basic understanding would go a long way. There are many credible data science certifications available across the board nowadays, to assist and equip you with the most efficient work prowess in the form of core skillsets and credential advancements. Making the most of these certifications would clearly mean a heightened data science career trajectory with amplified growth.

What is Data Analysis?

Data analysis simply means extracting useful information from data and making business decisions based on the process. It is a routine procedure to make decisions based on experience and reading the future through them. Although, the process involves several steps that lead to amplified growth such as inspecting, cleansing, transforming, and modeling data; to unravel valuable information, draw conclusions, and aid decision-making. Data mining is also used interchangeably with data analysis; although data analysis is an umbrella term. Business intelligence works great with data analysis as its rock. Experienced data analysts deploy innovative programming algorithms to assist in business development and processes wide and far. This is where clever data science skills come into play.

Role of Python in Data Analysis:

No doubt, programming tools, and techniques enable quick and targeted business decision-making. With Python and other programming languages being strewn in the process of business decision-making; the process gets faster at every step. Python exhibits multiple distinguishing features that enable programming at a supersonic speed and accurate results. The ways in which Python is applied include through:

  • Data Mining

An experienced data science professional would deploy libraries such as Scrapy and BeautifulSoup to build special programs that have access to structured data collection from the web; scraping and arranging data in the preferred format.

  • Data Processing

NumPy and Pandas are incredibly efficient at arranging big data sets and making math operations and vectorization on arrays easier.

  • Data Visualization

Matplotlib and Seaborn are the most popular Python data visualization libraries used for converting exhaustive lists of numbers into easy graphics, histograms, pie charts, etc.

High points of Python:

  • Ease of learning

Python focuses on being an easy-to-read language; that allows newcomers to be at peace with the simple syntax and offers reliable solutions.

  • Flexible

Its versatility allows flexibility for data models being systemized; developing ML algorithms, and web services; and completing a variety of tasks in possibly no time.

  • Well-supported

Python garners a huge fan base and is massively used in academic and industrial circles, allowing plenty of libraries available for analytics.

  • Massive libraries at access

Free and highly popular and easy-to-use libraries are a part of the Python gang base. This allows for an easy breathable space for constant expansion; while providing robust solutions.

  • Graphics and visualization

With a gazillion of visualization options at your disposal, Python is a must-have data science skill for becoming an ace data scientist. You can make data readily available for web-ready interactive plots and charts and graphics as well.

  • Built-in data analytics tools

These built-in data analytics tools can penetrate patterns, correlating information in extensive sets, and providing better insights in no time.

Final word:

Python is an internationally recognized programming language; rendering itself the most versatile credential on the portfolio to be put to a gazillion usage. The clever automation and easy accurate results bring home the best business insights for amplified growth. This is why Python has become a go-to credential for recruiters worldwide. This is exactly the time to upskill and arm yourself with the most trusted certification in data sciences, which is raging like wildfires in the Amazon. Be a profitable addition to your future recruiter today!