Data features are evolving every day. There are new data management and compliance standards arriving with new technologies and skills to master. In this dynamic and fast-changing disruptive world of data science, learning about some basics in Python Data Science could set your talent apart.
One such skill that you can learn in Python Data Science Course is – Data Exploration. Data Exploration is the first and the most difficult to manage step in the entire process of data analysis and visualization. Top Business Intelligence and Analytics teams hire only a handful of specialists in Python data exploration.
What is the Exact Role of Python Data Exploration Engineer?
The primary role of the Python data engineers for data exploration process is to summarize the entire workflow involved in Data Analysis – starting from measuring the size, type, accuracy, variety, variance, initial patterns and establishing relationships with every data attribute. The most advanced workflows use standalone programs built on Python, R and similar data science programming languages.
Open Source Programming Languages
According to a recent survey on top programming languages that data scientists prefer to work with and hire for include Python, R, PERL, SQL, and so on. While R is the most sophisticated and advanced compared to others, its Python Data Science stability and libraries that make it best to make quick increments and iterations, in shortest programming cycles.
Other languages that Data scientists combine with the likes of R and Python include Java (Java Virtual Machine / Java vm), Julia, Scala, MATLAB, and Google’s TensorFlow.
Depending on your maturity in the coding world, and previous experiences with Python, these open source community languages can enable you to manage numerous GPUs and Quantum Computing demands.
Why Learn Data Exploration with Python?
Learning data science with Python skills makes it easier or anyone to understand Data fabric, especially data exploration cycles. Because, with Python, programmers can basically target all their future data exploration/ searches, excluding irrelevant data points and search paths to remove duplication and redundancy that often hamper data analysis.
More importantly, these coding skills can help analytics teams to correlate various diverse types and build a familiarity with the existing information (historical, and predictive) to make better diagnosis and decision-making in the most complex process of data analysis.
Commitment to AI and Machine Learning Era
Staying true and relevant to the current wave of AI and ML in ITOps and DevOps force most companies to automate their processes to machines and human intelligence. Given the fact that only 2 out of 10 IT professionals are willing to invest learning and training with in the current technology stacks of IT, DevOps, Cloud Computing and Data Analysis, it’s crunch time in the talent market for top 500 Big Data and BI companies.
For those who have mastered Data Exploration in data analysis using Python and a combination of other programming languages, greener pastures await you in the growing Big Data markets of the world.