Python is a general-purpose programming language which can be used for developing web and desktop applications. It is also used in the development of complex numeric and scientific applications. Due to its versatility, Python is one of the fastest growing programming languages today.
So why is Python so important for those who are looking for a career in data analysis today?
Let’s first take a quick look at what a data analyst does. This will make it easier to understand why Python is a necessary skill to learn. The underlying logic here is that you understand a job better so that you infer what tools you need to do the job.
So what does the work of a data analyst entail?
- Data Analysts interpret data and analyze results making use of statistical techniques and providing continuous reports. They develop and implement data analyses, data collection systems and other strategies that enhance statistical efficiency and quality. Data analysts also gather data from primary or secondary data sources and maintain databases.
- They identify, analyze, and interpret trends or patterns in complex data sets. The job also involves filtering and cleaning data by reviewing computer reports, printouts and performance indicators to locate and correct code problems.
- They conduct full lifecycle analyses and monitor performance and quality control plans to identify improvements.
These allow data analysts to work with management to improve business and contribute to revenue generation.
All the above work responsibilities and duties make it clear that data analysts contribute significantly to a business and all these require tools to handle huge amount of data easily. It is evident that the need of the hour is to handle massive amounts of information, clean the information in a manner that it is readily usable. This is where Python comes in – it is simple and is easy to use for performing repetitive tasks.
Relationship between Data Analysis and Data Science
You may very well question why we need to discuss Data Science here. Well, we need to explore the relationship between the two as Python also plays a significant role in data science. The two maybe separate fields but there’s also a significant overlap between the two.
One of the major differences between the two is that while a data analyst gathers meaningful insights from established data, a data scientist deals more with what-ifs. A data scientist’s job is to predict the future or what might be, where as a data analyst deals with everyday real time data to answer questions. From this it is apparent that there may be times that the two separate entities often have work which overlap. Hence, Python plays a significant role for both these two streams. Both these streams require knowledge of software engineering, good communication skills, mathematical acumen and a good understanding of algorithms.
However, possessing a strong business acumen is hugely beneficial for a data scientist which is not a strong requirement for a data analyst.
If we go on to discuss remuneration, an entry-level data analyst can expect an approximate annual pay of $60,000 while a data scientist can earn approximately $122,000 in the US and Canada.
Reasons why Python is a MUST for Data Analysis
It can be used for various purposes including script applications and developing websites.
Python is well known for its simplicity and readability. Thus, even for programmers who are at the beginning level can easily master this. The main advantage that Python offers programmers is that they can use fewer lines of code when compared to older languages. This saves valuable time.
As Python is open-source, it’s free and uses a community-based model for development. It can run on Windows as well as Linus environments. It can also be ported to multiple platforms easily.
Ready help at hand
The best part is if you run into any snag, help is readily at hand. Python has a large following and is widely used in academic and industrial sectors, hence, a lot of useful analytics libraries are available like Stack Overflow, mailing lists, and user-contributed code and documentation. Remember that this is a constantly growing source of free support material as more users keep contributing information on their user experience.
As discussed, Python is ideal for carrying out repetitive tasks and data manipulation leaving data analysts free to work on more interesting part of their work. Data science is one of today’s most in-demand functions and Python, an essential skill in any data scientist’s toolbox.
At Thinklogix, we are passionate about building new careers. Gain the in-demand skills you need to break into new careers in technology, information security or data science. Thinklogix offers a 20 hours program on Python for Data Science. You will master the ability to analyze and visualize data in meaningful ways using python to help solve complex business problems. Working with tools such as Jupyter Notebooks, NumPy, and Pandas, you will have the opportunity to analyze real-world datasets to identify patterns and relationships in data. Certificate of completion is provided by Thinklogix at the end of the course.