In an era where automation and technology are leading the way, learning Python is imperative for aspirants wanting to make waves in data science. Python is a computer programming language used in a range of applications, from websites to maps, to make data delectable and accessible for non-specialists.
A recent survey conducted by Hackerrank (research organization) stated that Python is a leader in FinTech because of its easy-to-understand and access ecosystem. Furthermore, the survey also revealed that Python’s easy syntax system and clutter-free coding system make it easy for non-technical people to use.
Moreover, Python is also good in picking up frameworks like Apache Spark, others, which eases collection, categorizing, and cleaning data, making the work of data scientists and data analysts easier. So, here are some more reasons why learning Python in data science and data analysis is a crucial prerequisite.
Why Is It Important to Learn Python in Data Science and Data Analytics?
As Python is not created for a specific need, it does not have a fixed template or specific API and is suitable for all kinds of applications. As a result, Python is used in an array of industries for different functions, from website development to app development and more. Interestingly, YouTube also shifted from PHP to Phyton in 2007 because of its flexibility.
2. Relatively Easy-to-learn and Use
Python’s simple syntax and closer-to-spoken human language functionality make it relatively easier to learn and use Python for data science. Moreover, Python has a low learning curve, making it an ideal choice for first-timers.
3. Free and Widespread
Python is an open-source coding language that is accessible and free to everyone. Furthermore, it is designed for all computer systems. From Windows to Linux, all can implement and use the coding language. Meanwhile, some other coding languages are costly and are limited to a certain operating system.
4. Career Prospects
It is imperative to learn at least one programming language to operate in data science and data analytics. Learning Python in data science and data analytics is widely preferred by people because it’s easy, free, and accessible. Moreover, if you do not learn Python, you would miss out on many valuable opportunities in your career.
Every company expects its staff to be efficient and highly productive in their work. According to a 2018 poll conducted by KDnuggets, 66% of data scientists used Python over other programming languages to increase efficiency. Hence, to excel in data science and analytics, you need to learn and be well-versed with Python.
Until now, we looked at the importance of Python in data science and data analytics. Henceforth, we will discuss what data science is, how to make a successful career in data science. Read on.
What is Data Analytics and Data Science?
Big data plays an important role in this time and age. Therefore, many companies are investing in collecting, analyzing, cleaning, and maintaining data to evaluate the business risk and solve business-related problems beforehand.
The upsurge of Big Data has resulted in the opening up of streams like data analysis and data science that help in managing and analyzing data. The easiest programming language used in data analytics and data science is Python. Here’ the exact meaning of data analytics and data science –
Data Analytics is the process of cleaning, changing, and processing raw data. It also includes evaluating data to extract relevant and important information from them to apply to business to attain higher profits and sales. The data collected is converted into charts, images, tables, and graphs to gain insight into a business problem.
Meanwhile, data science studies data to find unseen patterns, derive meaningful information, make right business decisions, and alleviate business risk. Although data analysis and data science might seem like similar business functions, they are fundamentally different. Continue reading to know the difference between data analysis and data science.
Data Analytics vs. Data Science: Know the Difference
|Data Analytics only consists of statistics, mathematics, and statistical analysis.
|It is the umbrella under which data analytics, computer science, machine learning, and artificial intelligence exist.
|D is a branch of data science that focuses on analyzing extracted data and converting them into graph, bar, Venn diagram to make business decisions.
|It focuses on finding correlation between data.
|It aims at finding solutions to business problems.
|This seeks to find unique questions to drive innovation.
|Data Analysts must be proficient in data mining, data modeling, data warehousing, statistics, among others.
|Data Scientists must be well-versed with Mathematics, Computer Science, Machine Learning, among others.
A career in Data Science and Data Analytics
Unlike traditional jobs, you need to keep upskilling and updating yourself to become a good data scientist and data analyst. Therefore, building a career in Data Science is a little complex and competitive. However, it is rewarding at the same time – you would be drawing a good paycheck and will have no fear of replacement from automation.
So, if you wish to develop a career in data science and data analytics, you can take up online courses that will prepare you for the job. Emeritus offers various short-term and executive courses that will skyrocket your career as a data scientist and data analyst. The curriculum of our data science courses includes learning Python and many other things. We have partnered with famous institutes like the Indian Institute of Management (IIM) Calcutta, Indian School of Business (ISB), and others to offer high-impact executive education programmes.