Let’s look at Wikipedia. company intelligence (BI) refers to the methods and tools that businesses employ to analyze company information. BI tools offer perspectives on corporate operations that are historical, present, and predictive. For this reason, all a business intelligence developer needs to do is locate the location of business data, “gather” the data, and “store” it there so that it can be used for additional analysis and reporting.

The biggest claim a BI developer can have is data integration. They have to be able to read from different sources of data including relational databases like SQL Server or Oracle, No-SQL databases like MongoDB, other services like Google Docs or Google Analytics data, or even data on social media like Twitter and Instagram.

A BI developer typically uses Microsoft SSIS (SQL Server Integration Services) or similar tools to integrate data from different sources into their data warehouse but the problem with this tool is that it is perfectly designed to read from relational databases and once you try to step a little further it either needs components written by ordinary people which often malfunction or does not support reading from the source you want at all.

It is when you as an experienced BI developer must know a way out of this problem because otherwise you cannot support the CLAIM. But don’t panic, there is always an easy learning and developing programming language to help you get away with that too, PYTHON.

Can Python be Used for Business Intelligence

Fortunately, python supports reading from almost whatever data source there is with the help of its useful and diverse packages yes I know that there are many analytics, data mining, machine learning, and data science packages to help with the modeling process but in this article, I wanted to be as simple as a BI developer trying to solve their ETL/ELT (Extract . Load, Transform) problems.

Companies want developers with experience in Python since it’s one of the most important tools in the field to bring their data insights—and beyond—up to par. They achieve this either by working with outsourcing companies to assist with their Python programming needs, or they go the traditional route and hire permanent in-house developers.

However, both employers and developers need to be aware of the most crucial Python skills that data science developers absolutely need to possess before they start focusing on hiring Python-focused candidates and before developers begin applying for these positions.

Python is one of the most widely used data science programming languages and is a very useful tool with many applications. Developers need to grasp Python as a language, as well as its frameworks, tools, and other related abilities, in order to excel in this industry. These abilities are the topic of several Python certifications.

• 1. Python fundamentals

A data scientist’s main work is to use data to extract actionable insights that help with various factors in a business, study, and so on. This process requires quite a bit of Python programming skills for each step. As such, data scientists must have a solid understanding of Python programming fundamentals to write the most efficient code for their job and to understand the codebases of other developers or teammates.

A few of the basic Python programming fundamentals that data scientists must master include:

• Data types. Python offers many built-in data types, including floats, integers, and strings. Devs must know the difference between each and when to use them.
• Operators. Python features special operating symbols that help devs perform specific operations on one or more commands. These operators include addition (+), subtraction (-), and multiplication (*).
• Variables. In Python, variables allow developers to store values in a program. They also create variables by assigning them a value using the equal sign (=).
• Lists. Lists are ordered collections of items, and they’re useful for storing data that requires accessing in a particular order. Or, devs use lists for storing multiple items of the same data type.
• Dictionaries. A dictionary in Python is a collection of key-value pairs. They’re useful in storing data that requires accessing with a unique key.
• Functions. A function is a code block that performs a specific task and isn’t reusable multiple times in one program. Defining and calling functions is a vital part of Python development.
• Control structures. These are code blocks that determine the execution of other code blocks. Common examples of control structures include if statements, for loops, and while loops.
• Modules and packages. A module is a file containing Python code, and a package is a collection of modules. Devs have to know how to import and use modules and packages, especially when creating larger and more complex Python programs.

• 2. Data manipulation and analysis

Data scientists spend a significant amount of time preparing and manipulating data to ensure it’s ready for analysis and modeling. Thus, it’s essential for them to possess the ability to work with Python to clean and prepare data, including different data types and sizes.

Read Also: Components of Business Intelligence

Proficiency in using Python for efficient analysis of datasets of varying types and sizes is crucial for a data scientist. Additionally, data scientists must know how to use PySpark for large dataset manipulation and employ libraries for different data types such as images, text, and audio when necessary.

• 3. Data visualization

Data visualization is an essential component of data science that helps facilitate exploration, comprehension, pattern identification, and effective communication of findings to diverse audiences. Data scientists need to have hands-on skills and a robust understanding of data visualization tools to use them effectively.

Among the numerous libraries and tools available in Python for data visualization, Matplotlib is a widely used library for creating static, animated, and interactive visualizations with an intuitive interface for generating statistical graphics. Seaborn, built on top of Matplotlib, provides a more polished interface for creating statistical graphics. Devs have many other options as well, including Plotly, Bokeh, Altair, and Vega.

• 4. Data storage and retrieval

Efficient data storage and retrieval skills are essential for data scientists who work with large amounts of data. Data scientists must know the various approaches for storing and retrieving data, depending on the nature of the data and their needs.

In Python, there are multiple ways to store and retrieve data. Common approaches include flat files, CSV files, JSON files, relational databases, NoSQL databases, and cloud storage services. Relational databases are powerful systems that store structured data and can be queried using SQL. Cloud storage services such as Amazon S3, Google Cloud Storage, and Microsoft Azure Storage provide scalable options for storing large amounts of data in the cloud. Python provides libraries such as boto3 and google-cloud-storage for accessing these services.

• 5. pandas

The pandas package is a crucial tool for data scientists and analysts working in Python. It is an open-source Python library that enables the handling of tabular data by exploring, cleaning, and processing it. Pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive. pandas is one of the essential libraries for any data science workflow, allowing for data processing, wrangling, and munging.

• 6. NumPy

NumPy is a Python library that enables the handling of large-dimension arrays through mathematical functions. It offers a variety of methods for array manipulation, metrics, and linear algebra. NumPy stands for Numerical Python and allows for the vectorization of mathematical operations on NumPy arrays, enhancing performance and speeding up execution. The library makes working with large multidimensional arrays and matrices effortless, allowing for efficient data analysis and manipulation.

• 7. Artificial intelligence and machine learning

Data scientists of any kind require a good grasp of artificial intelligence and machine learning. Algorithms in machine learning aim to create systems capable of learning from data patterns automatically. Mastery of Python is absolutely vital in working with machine learning algorithms effectively as it’s the language of choice for data science.

• 8. Deep learning

Deep learning is a crucial component of data science that involves using artificial neural networks to extract higher-level features from data through multiple layers of processing. Python plays a vital role in this field, as it offers a wide range of powerful libraries and tools, such as TensorFlow and PyTorch that allow developers to build and train deep learning models effectively.

• 9. Web frameworks

Developers looking to successfully create and deploy web apps while taking advantage of their Python know-how must have a solid understanding of web frameworks. The most popular frameworks used by Python developers are Flask and Django. Django is a high-level web framework that prioritizes clean, rapid, and pragmatic design while offering many libraries to assist with the creation of high-quality web apps without building everything from scratch.

Flask is the opposite of Django in that it’s a micro-framework that doesn’t rely on any particular tools or libraries. It doesn’t include a database extraction layer, form validation, or any other common functions provided by third-party libraries.

However, it’s considered a template engine with its own modules and libraries. This allows developers to create web apps without needing to write low-level code. Both of these frameworks are highly versatile and allow developers to create useful web apps with Python. By leveraging the tools and libraries within these frameworks, devs focus on writing high-quality code without getting bogged down in lower-level details.

• 10. Front-end technologies

To successfully develop web apps to help with data science endeavors, Python developers must have a solid understanding of front-end technologies. This requires three primary front-end markup languages: CSS, JavaScript, and HTML. Python can generate all three markup languages through compilers, parsers, and transpilers.

Python devs must hone their skills in these front-end technologies in order to fully utilize their Python knowledge helps dev build the basic structure of a web page, CSS helps style layouts and content, and JavaScript adds interactivity and dynamic behavior to web pages. By developing skills in all three, Python devs ensure that their apps and data science projects are not only functional but also visually appealing.

What Skills Are Needed to Be a Business Intelligence Developer?

Here are the steps you need to take to become a Business Analyst.

• Step 1: Plan to get into a business analyst profile

First of all you need to consider the fact that it takes a bit of time to become a business analyst or to change any career you are working as. Working in an X profile, now you have to move into a business analyst profile, so you need to have some time. You need to plan it well, so that you can devote five to six hours every week to learning certain things, especially in the initial period of your journey.

Therefore, your first step is to plan for getting into a business analyst role by making up your mind.

• Step 2: Choose a course to learn business analysis skills with hands-on training

Choosing a course to learn business analysis skills with hands-on training is crucial for gaining practical experience in the field. Look for courses that provide real-world scenarios and case studies to work on, as well as opportunities to collaborate with other learners.

• Step 3: Prepare for ECBA Certification or others

Preparing for ECBA Certification or other certifications is essential for demonstrating your knowledge and competency in business analysis. Make sure to research the requirements and exam format, and consider joining study groups or taking practice exams to boost your confidence.

• Step 4: Get your resume dressed up

Getting your resume dressed up is crucial for showcasing your skills and experience in the best possible light. Make sure to highlight your relevant education, certifications, and work experience, and use keywords from the job description to catch the recruiter’s attention.

• Step 5: Prepare for the interview

Preparing for the interview is key to making a great impression on potential employers. Practice answering common interview questions and be ready to discuss your strengths, weaknesses, and relevant experience. Make sure to research the company and the position to show your interest and enthusiasm.

• Step 6: Upload your resume

Uploading your resume is usually part of the application process for job openings. Make sure to follow the instructions carefully and double-check for any typos or errors before submitting. Customize your resume to match the specific job requirements to increase your chances of being selected for an interview.

• Step 7: Give interviews

Giving interviews is your chance to shine and show why you are the best candidate for the job. Dress professionally, arrive on time, and be prepared to discuss your skills and experience in detail. Remember to listen actively, ask thoughtful questions, and follow up with a thank-you note afterwards.