Data QA Developer with NumPy Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Data QA developer with NumPy 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 Data QA tech & tools in 2024
Data QA
What is Data Quality
A data quality analyst maintains an organisation’s data so that they can have confidence in the accuracy, completeness, consistency, trustworthiness, and availability of their data. DQA teams are in charge of conducting audits, defining the data quality standards, spotting outliers, and fixing the flaws, and play a key role at all stages in the data lifecycle. Without DQA work, strategic plans will fail, operations will go awry, customers will leave, and organisations will face substantial financial losses, as well as a lack of customer trust and potential legal repercussions due to poor-quality data.
This is a job that has changed as much as the hidden infrastructure that transforms data into insight and then powers the apps that we all use. I mean, it’s changed a lot.
Data Correctness/Validation
This is the largest stream of all the tasks. When we talk about data correctness, we should be asking: what does correctness mean to you, for this dataset? Because it would be different for every dataset and every organisation. The commonsense interpretation is that it must be what your end user (or business) wants from the dataset. Or what would be an expected result of the dataset.
We can obtain this just by asking questions, or else reading through the list of requirements. Here are some of the tests we might run, in this stream:
Finding Duplicates — nobody wants this in their data.
– Your data contains unique/distinct values in that column/field. Will the returned value be a unique/distinct value in that column/field?
– Any value that can be found in your data is returned.
Data with KPIs – If data has any columns we can sum, min or max on it’s called a key performance indicator. So basically any models which are mostly numeric/int column. eg: Budget, Revenue, Sales etc. If there is data comparison between two datasets then below tests applies:
– Comparing counts between two datasets — get the difference in count
– Compare the unique/distinct values and counts for columns – find out which values are not present in either of the datasets.
– Compare the KPIs between two datasets and get the percentage difference between them.
– Replace missing values – missing in any one of the datasets with primary or composite primary key. This can be done in a data source that does not have primary key too.
– Perform the metrics by segment for the individual column value — that can help you determine what might be going wrong if the count of values in the Zoopla-side doesn’t match the count on the Rightmove-side or if some of the values are missing.
Data Freshness
This is an easy set. How do we know if the data is fresh?
An obvious indication here is to check if your dataset has a date column, in which case, you just check the max date. Another one is, when the data was pulled into a particular table, all of this can be converted into a very simple automated checks, which we might talk about in a later blog entry.
Data Completeness
This could be an intermediate step in addition to data correctness, but how do we know to get there if the space of answers is complete?
To do this test, check if any column has all values null in it perhaps that’s okay, but most of the time it’s bad news.
Another test would be one-valuedness: whether everywhere on the column all values are the same, probably in some cases that would be a fine result, but probably in other cases that would be something we’d rather look into.
What are Data Quality Tools and How are They Used?
Data quality tools are used to improve, or sometimes automate, many processes required to ensure that data stays fit for analytics, data science, and machine learning. For example, such tools enable teams to evaluate their existing data pipelines, identify bottlenecks in quality, and even automate many remediation steps. Examples of activities relating to guaranteeing data quality include data profiling, data lineage, and data cleansing. Data cleansing, data profiling, measurement, and visualization tools can be used by teams to ‘understand the shape and values of the data assets that have been acquired – and how they are being collected’. These tools will call outliers and mixed formats. In the data analytics pipeline, data profiling acts as a quality control gate. And each of these are data management chores.
Where is NumPy used?
Space Invaders: Astronomical Array Antics
- Astronomers gaze into the abyss with NumPy crunching cosmic number candies, mapping the universe one pixel party at a time.
Wall Street Wizards
- Financial forecasters ride the stock wave, NumPy's mathematical surfboard doing gnarly calculus curls and portfolio pirouettes.
Robot Choreographers
- In secretive robot dance-offs, NumPy scripts the slickest servo moves, teaching tin men to tango with tensor elegance.
Quantum Realm Rendezvous
- Quantum computing courtiers entangle spooky particles at a distance, with NumPy sending love letters in matrix form.
NumPy Alternatives
SciPy
SciPy builds on NumPy providing a large number of higher-level functions that operate on numpy arrays and are useful for different types of scientific and engineering applications.
# Example: Solving a system of equations with SciPy
import numpy as np
from scipy.linalg import solve
a = np.array([[3,1], [1,2]])
b = np.array([9,8])
x = solve(a, b)
print(x) # Output: array([2., 3.])
- Pros:
- Integrates with NumPy, using it as the basis for operation.
- Rich collection of algorithms for optimization, integration, interpolation, eigenvalue problems, algebra, and more.
- Covers a wide range of domains such as signal processing, statistics, etc.
- Cons:
- Heavier to load due to more comprehensive features.
- Not as efficient for tasks requiring intense computation.
- More complex API compared to NumPy's simplicity.
TensorFlow
TensorFlow is an open-source software library for high performance numerical computation used extensively for deep learning tasks.
# Example: Using TensorFlow to create a constant
import tensorflow as tf
a = tf.constant([2, 3])
b = tf.constant([3, 4])
c = tf.add(a, b)
print(c) # Output: tf.Tensor([5 7], shape=(2,), dtype=int32)
- Pros:
- Highly optimized for large-scale machine learning.
- Strong GPU support for parallel computations.
- Comprehensive ecosystem with tools for model building, serving, etc.
- Cons:
- Steep learning curve for new users.
- Overhead for simple, non deep learning-related operations.
- Less intuitive syntax for non-machine learning tasks.
Pandas
Pandas is an open-source data analysis and manipulation tool built on top of the Python programming language, offering data structures and operations for manipulating numerical tables and time series.
# Example: Using Pandas to read a CSV file
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head()) # Outputs the first 5 lines of the CSV file
- Pros:
- Robust tool for data manipulation and cleaning.
- High-level data structures such as dataframes are easy to use.
- Comprehensive I/O capabilities for different file formats.
- Cons:
- Can be less memory efficient than numpy for large datasets.
- Performance issues with very large datasets or complex operations.
- Less suitable for multi-dimensional numerical arrays or matrix ops.
Quick Facts about NumPy
The Birth of NumPy: A Tale of Arrays and Efficiency
Picture this – the year is 2005, tunes from Coldplay's "X&Y" album are filling the air, and enter Travis Oliphant, the quant who just can't deal with slow numeric computations. NumPy is born! This clever chap took the best parts of its predecessors, Numeric and Numarray, and fused them into one super-array package. Now, scientists don’t have to write for loops that crawl at a snail's pace; they can use NumPy's n-dimensional arrays to make their calculations zoom.
NumPy's Groundbreaking Bits: Slicing, Dicing, and Broadcasting
Ever tried to add a two-dimensional matrix to, say, a single row of numbers without NumPy? Sounds like you're about to undertake a Herculean task, right? NumPy comes to the rescue with its broadcasting abilities. It's like giving a megaphone to that single row, enabling it to be heard across the vast rows and columns of the two-dimensional matrix without throwing a tantrum or even breaking a sweat!
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6]])
row = np.array([1, 1, 1])
summed = matrix + row # Boom! Broadcasting in action.
The Version Evolution: A NumPy Timeline
NumPy hasn't just flatlined since its creation – oh no, it’s been on a roller coaster of improvements! Picture it sprouting new features like a banana plant in time-lapse. With each major version, from 1.0 in 2006 to the sleek 1.20 in 2021, NumPy has buffed up its core with more speed and muscle. Its evolving API is like a Transformer, reshaping itself to tackle ever larger computational conundrums. NumPy 1.0? Basic. NumPy now? A beast!
What is the difference between Junior, Middle, Senior and Expert NumPy developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior NumPy Developer | 0-2 | $50,000 - $70,000 |
|
| Middle NumPy Developer | 2-5 | $70,000 - $95,000 |
|
| Senior NumPy Developer | 5-10 | $95,000 - $120,000 |
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| Expert/Team Lead NumPy Developer | 10+ | $120,000 - $160,000 |
|
Top 10 NumPy Related Tech
Python
Python, the great serpent of programming languages, slithers into the first spot with ease. It's the primary language for NumPy development, being as cuddly for beginners as it is powerful for the pros. Whether you're slicing data or dicing algorithms, Python makes sure you don't get bitten by complexity.
SciPy
SciPy stands atop NumPy like a cherry on an ice cream sundae — it’s the essential topping. This library provides additional functionality to NumPy’s numerical computations by adding a treasure chest of algorithms for minimization, regression, Fourier-transformation, and more. Think of it as the swiss-army knife for math wizards.
Pandas
To handle data like a pro pandas-wrestler, Pandas is your go-to. This library will have you performing data manipulation with the finesse of a chef slicing sushi. It's built on NumPy, offering DataFrame objects that make data look prettier than a peacock.
Matplotlib
If numbers are a snooze-fest, then let Matplotlib wake you up with some splashes of color. This plotting library turns your data into visually appealing charts that can convey your point better than a Shakespearean soliloquy. It’s NumPy’s BFF for putting numbers into perspective.
IPython/Jupyter
When working with NumPy, the IPython shell and Jupyter notebooks are like the command center on the starship Enterprise. They’re not just for typing code; they let you mix live code, equations, visualizations, and narrative text all in one place. It's like having your own science lab inside your computer.
# An example of using IPython magic with NumPy
%timeit np.dot(np.random.rand(1000), np.random.rand(1000))Scikit-learn
Ever dreamed of teaching your computer to predict the stock market or identify cats in photos? Scikit-learn turns those dreams into reality by riding on top of NumPy for all your machine learning endeavors. It's like Pokémon training for data — gotta fit 'em all!
TensorFlow/PyTorch
For those who seek the bleeding edge of machine learning and deep learning, TensorFlow and PyTorch light the way. Powered by NumPy, they've got more layers than an onion (and they’re sure to make the competition cry). These frameworks are for anyone eager to teach computers to think.
Dask
When NumPy starts sweating under massive datasets, Dask swoops in like a superhero. It scales your NumPy workflows to tackle the Hulk-sized computations with grace, using parallel processing that’s as impressive as a circus balancing act.
Numba
If you've ever wished your Python code could run at the speed of a cheetah strapped to a rocket, say hello to Numba. This just-in-time compiler takes your slow Python loops and gives them a nitrous boost, all with minimal effort, turning NumPy array operations into speed demons.
GitHub/Version Control
Last but not least, what’s a developer without their trusty version control? GitHub hugs your code repositories while whispering sweet nothings into their .git directories. It safeguards your NumPy masterpieces and lets you collaborate with other coders as seamlessly as a chorus line.