Business Intelligence (BI) Developer with Pandas Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Business Intelligence (BI) developer with Pandas with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.
The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.
Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.
Trending Business Intelligence (BI) tech & tools in 2024
Business Intelligence (BI)
Business intelligence (BI) is the term used for analysis by SQL specialists, typically yielding status reports for the business. Data analytics grew from BI, partly because the need for reporting and analysis became more frequent and dynamic, but also because most company data now resides in the cloud – in a data warehouse and on a customer data platform (CDP) – and tools to administer these systems became easy to use by people other than SQL specialists, such as data analysts. Understanding the differences between data analytics and business intelligence is essential to operating a profitable business that deploys data in the 21st-century way.
Using both BI and data analytics should help you to better understand the day-to-day execution of your business, and improve your decision-making process.
What is business intelligence and new trends?
At its most basic, business intelligence is defined as the collection, storage, and analysis of input received from different operations in an organisation. Although the entire purpose of BI is to track the overall direction and movements of an organisation, as well as providing and suggesting more informed decisions from data, it does so by producing reports for managers that would help them in their decisions. For instance, these reports can give insights on what’s going on inside the business, but can also be solely about external aspects surrounding the business, for example, in creating an analysis of a market in which they have a desire of venturing into.
What tends to happen with BI is to provide explanations of why the business is in the state it is – as well as presenting some perspective on how operations have grown over time. BI uses facts from recorded business data to help interpret the past, which means that company officials can move ahead with a better grasp of the company’s journey and where it is heading. Business intelligence is often also required to ‘play out’ various scenarios to assist with business planning. For example: ‘What will happen to signups if we raise our prices?
In day-to-day business operations, a system that would produce such reports was a traditional system of what was then known as ‘business intelligence’. And because stakeholders would require such reports on a regular basis – every month, or every quarter – producing the same report over and over again was a tedious task for the so-called business intelligence analysts. Today’s Business Intelligence, however, relies largely on automated regular reports, which are often generated by in-house data analytics, so that in the modern sense data analytics is an integral part of business intelligence.
Behind Business Intelligence (BI)
Approach is a set of technologies which are helping companies to collect and analyze data from business operations, and following actionable insight, they are using such insight to make sustainable business decisions. With the ever-growing amounts of data, it can be highly beneficial for the procurement stream to acquire some kind of understanding in business intelligence tools in order to start forming its current strategy and future strategic decisions. Through this write up, I’m offering to cover the essence behind the term, along with some further explanation with examples to provide. I am also trying to cover the related and relevant topics, and most importantly I will try to answer any possible questions you may continue to have with regards to business intelligence.
The definition of Business Intelligence
Often confused with business analytics, business intelligence (BI) is an umbrella term for the processes, methods, and software that collects both internal and external data, structured and unstructured, and processes them for further analysis. Users are then able to draw conclusions from the data by means of reports, dashboards, and data visualization.
Formerly the preserve of data analysts, business intelligence software is spreading and becoming accessible to wider circles. Businesses are becoming truly ‘data driven’. The accelerating spread of the large-data revolution gives businesses everywhere a chance to squeeze the full potential of digital transformation, via enhanced operational advantages.
However, Business Intelligence (and related notions such as machine learning, artificial intelligence…) not only aims at best optimizing the processes or at increasing the performances of the entity, it also helps to guide, speed up and to improve the decisions made by the company and based on real-time actual metrics.
These applications are now referred to as essential tools for companies to get an overview of the business, to discover market trends and patterns, to track sales and financial performance, to set up key performance indicator monitoring, to boost performance and many other things. In other words, this data, if used well, is one of the main resources for gaining competitive advantages.
How does Business Intelligence work?
Business Intelligence is based on four stages which are: Data Collection , Data Storage , Data Distribution and Use.
- Collection: Initially, ETL (Extract, Transform, and Load) tools are used to collect, format, cleanse, and combine all the data, regardless of the source or form of appearance. This raw data comes from various sources, including company information system (ERP[2]), its customer relationship management (CRM) tool, marketing analysis, call center, etc.
- Storage: Once aggregated, this data is then stored and centralized in a database, whether hosted on a server or in the cloud. This is called a data warehouse or a data mart.
- Distribution: The principle here is to distribute to the company’s internal partners everything that is created in the decision support platform. There are many new varieties of BI emerging, which use all of the characteristics of web 2.0 and therefore allow access to information used for decision-making to an even broader audience.
- Use: Various tools are used depending on the needs. For example, for multidimensional data analysis, there are OLAP (Online Analytical Processing) tools, for correlation search there are data mining tools, for performance communication there are reporting tools, for performance management there are dashboards and so on.
Business Intelligence technology to support procurement
But by giving procurement departments access to new Business Intelligence tools, they should be able to produce summary data that is accurate and relevant regarding both their corporate expenditure and their supplier base – such as actual and forecast turnover, contact and dispute histories, negotiated prices, the organization of contracts, and so on.
They can imagine and mine it quickly, and then communicate it in a digestible, understandable form to all, as well as use it as an input to inform business decisions as part of their sourcing strategy – to get better outcomes.
BI functionality allows them to give supplier performance benchmarks, score tenders, select suppliers according to multiple selection criteria in the application of Lean Procurement, etc.
In addition to this decision support, buyers also enjoy operational efficiency gains: procurement departments are notorious for lagging in terms of digitalization, and despite the benefits they could bring, buyers still spend almost three-quarters of their time on purely transactional or operational activities[2]. In this sense, such a solution makes total sense.
To take one example, the Itochu Corporation, a Japanese global trading company, says it has cut the time needed to produce its monthly reports by 92 per cent using BI tools[3]. That is a figure that any buyer today should sit up and take notice of.
Ultimately, such software makes communication between procurement departments and the wider company easier and more effective; armed with data and figures, they can work in tandem with other divisions, particularly finance, and also try to define their strategic footprint within the organization.
Resistance to BI
But such technology is not easy to develop. Two formidable challenges stand in the way.
- Complexity of use: At the beginning, the use of Business Intelligence implies profiles with technical skills, analysts, architects, or even developers specialized in BI. Nevertheless, the solutions in the market today are increasingly aimed at all staff in an organization, at the managerial and operational personnel. Easy both to use and interpret, they are now tuned so that the management tools can be tailored. The business user is beginning to see the rise of ‘self-service BI’.
- Quality, reliability, and usefulness of data: Second, the quality, relevance, and value of the data can themselves become a barrier, for instance, if the supplier selection process is not managed in a centralized way or not validated by procurement departments. It is thus essential that the collection be prepared and the databases organized before posing any queries.
Data is the 21st century gold, ie one of the most strategic resources for a company. No surprise then that, in addition to the logical quality, the era of Big Data is quickly turning into the era of Smart Data. In fact, towards a real Purchasing Intelligence approach. Business Intelligence programs can go even further by integrating predictive analytics, data, or text mining tools, etc., and thanks to BI capabilities, it’s up to the procurement function to aim for a Purchasing Intelligence approach in order to optimize the performance of the company.
Where is Pandas used?
Data Wrangling in Chef's Kitchen
- Like a master chef slicing and dicing ingredients, Pandas chops and stirs data into gourmet insights for data analysts.
Time-Traveling Financial Wizards
- Pandas hops aboard the DeLorean, transforming historic stock prices into predictive alchemy, enchanting the wallets of investors.
Science Labs Get Schooled
- In the petri dish of scientific research, Pandas is the bacteria that ferments raw numbers into the fine wine of knowledge.
Marketing Prophets and Their Crystal Balls
- Marketing gurus gaze into the Pandas-powered crystal ball to foretell customer desires, crafting campaigns that resonate like a catchy jingle.
Pandas Alternatives
Apache Arrow
A cross-language development platform for in-memory data, providing optimized data interchange for systems.
Example: Reading a CSV file into an Arrow Table.
import pyarrow.csv as pv
table = pv.read_csv('example.csv')
Pros:
- Language-agnostic data format
- Fast in-memory processing
- Seamless integration with other data tools
Cons:
- Less mature ecosystem compared to Pandas
- Smaller community support
- Learning curve for new users
Dask
Parallel computing library that scales Python analytics enabling efficiency in large datasets processing.
Example: Using Dask DataFrame to perform operations similar to Pandas.
import dask.dataframe as dd
df = dd.read_csv('large_dataset.csv')
df = df[df.column > 0]
result = df.compute()
Pros:
- Handles larger-than-memory datasets
- Parallel and distributed computing capabilities
- Compatible with Pandas API
Cons:
- Can be cumbersome for small datasets
- Requires understanding of parallelism
- Overhead for task scheduling
Polars
A Rust-powered DataFrame library with eager and lazy computations aimed at high performance & memory efficiency.
Example: Basic data manipulation in Polars.
import polars as pl
df = pl.read_csv('data.csv')
df = df.filter(pl.col("column") > 10)
df = df.select(["column", "other_column"])
Pros:
- Blazing-fast performance
- Memory-efficient
- Both eager and lazy execution
Cons:
- Still developing API
- Less extensive documentation
- Smaller user and contributor base
Quick Facts about Pandas
The Birth of a Data Giant
Imagine a world swamped with data but starving for tools to digest it. In steps Pandas, a Python powerhouse created by one Wes McKinney. The year was 2008, and McKinney, in a quest to analyze financial data, gives birth to this game-changer, becoming the data wrangler's knight in shining armor.
The Name Game
Don't let the cute animal association fool you! "PAN" from "Panel Data" and "DAS" from "Data Analysis" form the muscular acronym Pandas. It does not munch bamboo but devours colossal datasets with an insatiable appetite, and just like real pandas, it became an endangered species - almost extinct development-wise in 2015, but survived and thrived!
From Struggle to Supersonic Speeds
Forget the tortoise; this hare’s got nitro boosters! With Pandas 1.0.0 unleashed in January 2020, data manipulation turned supersonic, boasting stability and new features. One such feature, "Nullable Integer Data Type", is like finding a unicorn in a haystack. Here, watch it turn "N/A"s into zeros without breaking a sweat:
import pandas as pd
# Creating a DataFrame with missing values
df = pd.DataFrame({'col1': [1, 2, None]})
# Converting column to nullable integer type
df['col1'] = df['col1'].astype('Int64')
# Surprise! N/A turns into a soft zero, no fireworks.
print(df)
So there you have it, loyal subjects of Datatopia, a brief chronicle of Pandas—your gallant guardian against data disarray. Farewell, and may your dataframes never falter!
What is the difference between Junior, Middle, Senior and Expert Pandas developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior | 0-2 | $50,000 - $70,000 |
|
| Middle | 2-5 | $70,000 - $95,000 |
|
| Senior | 5+ | $95,000 - $120,000 |
|
| Expert/Team Lead | 8+ | $120,000 - $150,000+ |
|
Top 10 Pandas Related Tech
Python
This is the bread and butter, the cheese to your macaroni. Without Python, your Pandas are just bamboo-less bears. Python is the language that breathes life into Pandas, and it's as essential as coffee on a Monday morning. You've got to know your Python like the back of your hand, or, perhaps, like your favorite mug.
NumPy
It's like the Robin to your Batman. NumPy is the powerhouse for numerical computing in Python, and Pandas sits on its shoulders to reach the data manipulation cookies on the high shelf. Know NumPy to crunch numbers like you're munching on cereal.
import numpy as np
np_array = np.array([1, 2, 3, 4])
Jupyter Notebook
Think of it as your spellbook where magic happens. Jupyter Notebooks are where data storytelling unfolds, combining live code with narrative text. It's where your data analysis becomes as interactive as a game of "Whack-a-Mole."
%matplotlib inline
import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
SQLAlchemy
This is your toolkit for database wizardry. SQLAlchemy lets you speak to databases in their native language, SQL, but through the comfort of Python scripts. It's like being able to converse with both animals and plants in an enchanted forest.
from sqlalchemy import create_engine
engine = create_engine('sqlite:///example.db')
Matplotlib/Seaborn
You'll be painting with data like Picasso with these. Matplotlib lets you craft fine traditional visualizations, while Seaborn spices them up with modern flavor. It's your art gallery of graphs and charts.
import matplotlib.pyplot as plt
import seaborn as sns
data = sns.load_dataset("iris")
sns.pairplot(data)
plt.show()
Excel/CSV Handling
A data analyst without Excel skills is like a sushi chef who can't handle fish. Pandas love files in these formats like pandas love bamboo. Get ready to import, export, and twirl these files like a data ballerina. Bonus points if you can make Excel puns.
df.to_csv('dataframe.csv', index=False)
df_read = pd.read_csv('dataframe.csv')
Dask
If Pandas is an artist, Dask is the fancy new brush set. It scales your data science toolkit to the next level, allowing you to work with larger-than-memory datasets on your humble laptop, pretending it's the Hulk when it's really just Bruce Banner.
import dask.dataframe as dd
dask_df = dd.read_csv('large_dataset.csv')
Git
Ah, the time traveler's tool. Git lets you manage versions of your data projects like you manage your playlists, with the added bonus of easy collaboration. Commit, push, pull, and branch out your code like a wild data gardener.
Scikit-Learn
When you're ready to take that Pandas DataFrame and start making predictions, Scikit-Learn is your go-to. It's like turning your spreadsheet into a crystal ball, forecasting the mystical trends of the future.
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
APIs Integration
Roll out the red carpet for APIs, the dignitaries that grace your data sets with fresh external info. Get comfortable pulling your weight with APIs, because when you do, you'll be the belle of the data ball, able to fetch data like Cinderella's fairy godmother.