Category Archives: Data Science
Probably the Best Data Visualisation for Showing Many-to-Many Proportion In Python – Towards Data Science
How to draw a fancy chord chart with links using PyCirclize
In my previous article, I have introduced the Python library called PyCirclize. It can help us to generate very nice Circos Charts (or Chord Charts if you like) with very little effort. If you want to know how it can make the Data Visualisation well- Rounded, please don't miss out.
However, dont worry if you are only interested in the Chord Charts with Links. This article will make sure you understand how to draw this type of chart.
In this article, Ill introduce another type of Chord Chart that PyCirclize can do. That is a Chord Chart with links that will visualize proportional relationships between many-to-many entities very well, and so far is the best one among all the known typical diagram types.
Before we start, just make sure to use pip for installing the library as follows. Then, we are all good to go. Lets explore this fancy chart together!
As usual, lets start with something abstract but easy to follow. The purpose is to show you what the chart looks like and whats the basic way of plotting it. Let me put the full code and the diagram at the beginning.
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What Does it Take to Get into Data Engineering in 2024? – Towards Data Science
Career advice for aspiring data practitioners 14 min read
If you are reading this you were probably considering a career change lately. I am assuming that you want to learn somewhat close to software engineering and database design. It doesnt matter what your background is marketing, analytics or finance, you can do this! This story is to help you find the fastest way to enter the data space. Many years ago I did the same and never regretted since then. Technology space and especially data is full of wonders and perks. Not to mention remote working and massive benefit packages from the leading IT companies, it makes you capable of doing magic with files and numbers. In this story, Ill try to summarise a set of skills and possible projects which could be accomplished within two to three months timeframe. Imagine, just a few months of active learning and you are ready for your first job interview.
Any sufficiently advanced technology is indistinguishable from magic.
Indeed, why not Data Analytics or Data Science? I think the answer resides in the nature of this role as it combines the most difficult parts of these worlds. To become a data engineer you would need to learn Software engineering and database design, Machine Learning (ML) models, and understand data modelling and Business Intelligence (BI) development.
Data engineering is the fastest growing job according to DICE. They conducted research to demonstrate that there is a gap so be quick.
While Data Scientists have been considered to be the sexiest job in the market for a long time now it seems there is a certain lack of Data Engineers. I can see a massive demand in this area. This includes not only experienced and highly qualified engineers but also entry-level roles. Data engineering has been one of the fastest-growing careers in the UK over the last five years, ranking 13 on LinkedIns list of the most in-demand jobs in 2023 [1]. On
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What Does it Take to Get into Data Engineering in 2024? - Towards Data Science
Optimizing Pandas Code: The Impact of Operation Sequence – Towards Data Science
PYTHON PROGRAMMING Learn how to rearrange your code to achieve significant speed improvements. 9 min read
Pandas offer a fantastic framework to operate on dataframes. In data science, we work with small, big and sometimes very big dataframes. While analyzing small ones can be blazingly fast, even a single operation on a big dataframe can take noticeable time.
In this article I will show that often you can make this time shorter by something that costs practically nothing: the order of operations on a dataframe.
Imagine the following dataframe:
With a million rows and 25 columns, its big. Many operation on such a dataframe will be noticeable on current personal computers.
Imagine we want to filter the rows, in order to take those which follow the following condition: a < 50_000 and b > 3000 and select five columns: take_cols=['a', 'b', 'g', 'n', 'x']. We can do this in the following way:
In this code, we take the required columns first, and then we perform the filtering of rows. We can achieve the same in a different order of the operations, first performing the filtering and then selecting the columns:
We can achieve the very same result via chaining Pandas operations. The corresponding pipes of commands are as follows:
Since df is big, the four versions will probably differ in performance. Which will be the fastest and which will be the slowest?
Lets benchmark this operations. We will use the timeit module:
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Optimizing Pandas Code: The Impact of Operation Sequence - Towards Data Science
FGV EMAp holds graduation ceremony for Brazil’s first data science and AIcourse – Portal FGV
On March 1, 2024, Fundao Getulio Vargas School of Applied Mathematics (FGV EMAp) held the graduation ceremony for the first group of students to have completed its Data Science and Artificial Intelligence Course. The ceremony, held in FGVs main building in Rio de Janeiro, was attended by 38 undergraduate students, of whom 21 are studying applied mathematics, 13 are studying data science and four are doing a dual degree.
According to the schools dean, Csar Camacho, EMAps position in the job marketis extremely satisfactory and underscores the institutions quality. In the past, a degree in engineering, law or medicine was enough to ensure a promising career. However, societyhas become more complex and diverse in its demands,so as well as a degree, people need to invest in constant professional development in line with the sophisticated technological advances that are taking place. Our figures show that 100% of EMAp graduates are swiftly hired, including those who choose to do a masters or doctorate, he said.
Yuri Saporito, the coordinator of the Data Science and Artificial Intelligence Course and the class sponsor, revealed his gratitude for taking part in this moment alongside students who made a difference over the four years they were together. I couldnt have wished for a better first class.You were attentive, interested and very participative. This course was envisioned in 2018 during an internal FGV meeting, when I realized that a degree in data science and artificial intelligence would open our doors to students who hadnt previously considered FGV as an option. It was a huge effort, but we managed to implement the course in mid-2019 and our first class, you guys, started in March 2020, he said.
Tiago da Silva Henrique, a student on the data science course and a CDMC scholarship holder, was mentioned as an academic highlight.He said he was grateful for the institutional recognition for his performance during his degree. According to him, the graduation ceremony marked the beginning of a long professional journey. He said that the school was very intellectually demanding but offered a safe environment with well-defined objectives for students personal development. More difficult decisions and greater challenges are yet to be faced by me and my colleagues. My feeling, then, is one of proactivity, knowing that my next steps will possibly determine the subsequent years of my career, he said. He also emphasized the courses pioneering status, at the forefront of a fast-growing industry that will potentially benefit from modern technical training. The recent commercial rise of applications based on large language models, such as ChatGPT, reinforces this point, he concluded.
Twenty-five of the students came from the Center for the Development of Mathematics and Science (FGV CDMC) project. Created by FGV in 2017, this project aims to identify talentedyoungsters in the countrys government schools and offer them the possibility of taking FGVs undergraduate and graduate courses in Rio de Janeiro.
In recent years, this talent selection project has offered university scholarships to outstanding government school students and medal winners in national mathematics Olympiads. These students are invited to apply to take any of the undergraduate courses offered by FGV in Rio de Janeiro: Applied Mathematics, Data Science and Artificial Intelligence, Economics, Administration, Social Sciences, Law and Digital Communication.
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FGV EMAp holds graduation ceremony for Brazil's first data science and AIcourse - Portal FGV
Analytics and Data Science News for the Week of March 15: Updates from Quantexa, Alation, Matillion, and More – Solutions Review
Solutions Review Executive Editor Tim King curated this list of notable analytics and data science news for the week of March 15, 2024.
Keeping tabs on the most relevant analytics and data science news can be time-consuming. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.
Alation, Inc., the data intelligence company, announced its business lineage to provide business users with broad and rich visibility into datas journey as it flows and transforms through systems. This visibility increases trust in the data and accelerates time to insights. Business lineage is an extension of Alations end-to-end data lineage, providing an abstraction layer to filter views of data flows and visualize relationships across technical, system, and business metadata. This unified approach offers a complete view so that all users can confidently and effectively harness information to unlock the full potential of an organization.
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Quantexaa decision intelligence solution provider for the public and private sectorsused the backdrop of QuanCon24, its annual customer and partner conference, to reveal its Decision Intelligence Platform roadmap and provided an update on Q Assist, a generative artificial intelligence (AI) assistant that previewed in July last year. Quantexa also announced a partnership with Microsoft. Dan Higgins, Quantexas Chief Product Officer, was joined by Kate Rosenshine, Global Technology Director of Strategic Partnerships at Microsoft.
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Matillions Data Productivity Cloud is now available for Databricks, enabling users to access the power of Delta Lake within their data engineering. The Data Productivity Cloud with Databricks brings no-code data ingestion and transformation capabilities that are purpose-built for Databricks, enabling users to quickly build data pipelines at scale, which can be used in AI and analytics projects.
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In partnership with universities and colleges globally, data and AI leader SAS is helping students and educators prepare for an AI-driven economy. To recognize significant contributions to data analytics education, the SAS Educator Awards honor university educators who excel at integrating SAS analytic tools within their academic institutions. Winners are nominated and chosen based on their use of SAS and commitment to preparing early career talent.
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On March 27, Solutions Review will host a Solutions Spotlight webinar with Amplitude, a digital analytics and event-tracking platform. During the hour-long presentation, attendees will gain a deeper understanding of Amplitudes platform and demonstrate how companies can apply data insights into product-led growth workflows and use them in their ongoing marketing efforts. The webinar will also feature a Q&A section with Laura Schaffer, Vice President of Growth at Amplitude.
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On March 22, Solutions Review will host a Solutions Spotlight webinar with Alteryx, an analytics solutions provider. Join Director of Product Management Sarah Welch and Manager of Product Management David Cooperberg to learn how the Alteryx AI Platform for Enterprise Analytics offers integrated generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities. Register now to reserve your seat for the webinar, scheduled for 12:00 pm Eastern Time.
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SoundCommerce, a retail data platform provider, announced a new partnership with Cordial, the marketing platform that powers billions of high-conversion email, SMS, and mobile app messages based on data. PacSun is the first consumer brand to take advantage of the new partnership, launching on Cordial with SoundCommerce data in less than 90 days. By leveraging SoundCommerces actionable data insights alongside Cordials personalized messaging solutions, retail brands can provide real-time, data-driven interactions that foster customer loyalty and maximize revenue opportunities.
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For consideration in future news roundups, send your announcements to the editor: tking@solutionsreview.com.
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How to Optimize Recommendation Results with Genetic Algorithm – Towards Data Science
Photo by Jakob Owens on Unsplash
Recommender systems have been applied in various industries nowadays, including e-commerce, marketing, video streaming, financial industries and so on. There are different types of algorithms out there, including collaborative filtering, content-based filtering, and reinforcement learning based recommender. However, sometimes the implementation of recommender algorithm is only a starting point there are always requirements to evaluate and further optimize the results based on business needs. In this post, we will be using a small subset of the classic dataset for recommendation study movielens dataset, to demonstrate how to use genetic algorithm to further optimize the recommendation results.
In terms of the recommendation algorithm, we will use the widely used collaborative filtering method ALS (Alternative Least Squares), which is provided by Spark MLlib. This approach is especially preferred when dealing with large datasets, although in our case study we are only using a small dataset for illustration purpose. The sample code of a basic ALS based recommender is as follows:
With just a few lines of code, we have a simple movie recommender model established. The next question is, how do we evaluate the performance of the recommender?
The answer for this question really depends on how to frame the problem, as well as the business context behind this model. For instance, if we are just building the recommender for learning purpose, then we can simply evaluate recommender output
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How to Optimize Recommendation Results with Genetic Algorithm - Towards Data Science
Alteryx’s Steve Harris Explains How AI Is Changing Data Analytics – GovCon Wire
At the end of 2023,Steve Harris became thepresident and general manager of the newly established public sector business unit at Alteryx, a data science and analytics tools provider in the GovCon market. Executive Mosaic recently sat down with Harris, a six-timeWash100 Award winner, to learn more about how artificial intelligence is shaping the data analytics industry and how Alteryx is embracing the technology.
Harris previously served as chief revenue officer for Ellucian, and prior to that role, he spent more than two decades at Dell Technologies. Read below for Harris full Executive Spotlight interview.
Tell me about the current state of the artificial intelligence market. Where are you seeing new opportunities in AI, and where do you think the market is heading?
Theres a tremendous amount of opportunity, and with that can come confusion. Theres a lot of curiosity and potential as well as risk. I like to compare it to the very early days of cloud, where the bad actors were scaling faster than cyber protection capabilities. Its a huge emerging market. Theres a massive fragmentation of players.
What are some of the biggest opportunity areas you see with generative AI? How is Alteryx approaching that?
AI is when technology simulates the actions of a person, and its based on machine learning. Generative AI can actually produce content and emulate the way that a human would create content. That can create opportunity in a number of ways, and particularly, I see worthwhile uses in analytics.
Given that generative AI is only as good or is just as bad as the data that its applied to, Alteryx is in a terrific position with our entire AI Platform for Enterprise Analytics, where we help people understand incredibly complex data sets in a number of ways.
At Alteryx, we believe that analytics can empower all employees to make faster, more insightful and more confident decisions regardless of technical skill level. Organizations can drive smarter, faster decisions and automate analytics to improve revenue performance, manage costs and mitigate risks across their organizations.
Alteryx AiDIN is the AI engine that powers the Alteryx AI platform, bringing enterprise-grade machine learning and generative AI for faster time to value, streamlined innovation, improved operations and enhanced governance.
Were a leader in AI for analytics. Our platform is a leader in no-code, easy to use technology that allows users across the organization to turn data into insights. The generative AI that we build into our platform is incredibly useful because its very hard for people to understand their data without studying it for a long time. Thats where generative AI comes in and does that data study almost instantly. Its applied to a quality data set, peoples own data and the third-party data that they choose to bring in as part of the reference. Generative AI has huge potential and has an equal amount of risk today.
What are some of the key challenges agencies face as they try to use their data for decision advantage or to better understand their organizations?
There are many silos of data. Alteryx is here to say that you dont have to embark on any significant or large-scale data management projects in order to get data. We bring the analytics to the data any kind of data, any place. The data never leaves your environment; we only take the data set from each source of data that is part of that query, and we make it extremely easy and understandable for a layman to get to an accurate, clean data set.
Then, because we are a no-code platform, we really attack that other major issue, which is making accessible technology available so that the people who are closest to the data have the power to transform that data, create business intelligence and bring data to decisions. These are the keys to the kingdom.
And on another note, this whole conversation is about a journey toward data literacy. Everybody likes to talk about the most exciting or interesting part of the journey big model AI, generative AI, disparate data sets, merging data but its all part of the journey towards data literacy for not only the staff and administrators of our agencies but also our citizenry.
U.S. citizens need to know when something they read online was produced by generative AI, because that could impact how much they trust what theyre seeing. If generative AI is producing the content on a government website, I no longer trust it because I have the data literacy to know that that generative AI could be taking information from sources that I dont consider authoritative. Data literacy is a really important overarching topic.
Which other emerging technologies do you anticipate will have the greatest impact on the federal landscape in the next few years?
The truth is that the problems havent changed dramatically over the last three to five years. Were still talking about JADC2, multi-domain command and control, cyber, cloud. Organizations are still concerned about where to put their data and their workloads. And were still talking about the lack of analytics and bringing data to decisions. I think the most disruptive technology is going to be the IT modernization of the legacy technology that exists across the spectrum of the federal government. We have really mature technologies that are highly addressable today that just are not being brought to bear.
The disruptor will be enabling the transition from outdated legacy systems to robust and contemporary technology solutions. Positioned as the premier AI platform for enterprise analytics, we boast a proud 27-year legacy. Our platform is trusted by 49 percent of the worlds top 2,000 companies, along with numerous government agencies globally. This underscores the vast potential for growth and innovation ahead. Our platform exemplifies the shift from technical debt and antiquated technologies to embracing and expanding modern technological capabilities that can have a compelling, positive impact on people and organizations.
I think the most disruptive technology will be the least proprietary technology. When you think about some of the market leaders being more rapidly adopted in the federal space, those technologies tend to be more black box in nature less of a software company and more of proprietary technology with many services, not only to implement but also to maintain. Thats the definition of legacy technology. If youre stepping into a legacy IT model as a way to modernize, I think theres a lot of danger there.
I do think that some of the big AI models and machine learning that is able to happen, assisted or unassisted, is going to have a tremendous impact on some of the biggest problems. Big model AI is going to help make a huge difference, taking those huge solutions and applying them to hundreds of thousands of small problems and decisions made every day.
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Alteryx's Steve Harris Explains How AI Is Changing Data Analytics - GovCon Wire
Supporting and valuing women in tech: Five questions with Assistant Professor of Data Science Kristen Gore – willamette.edu
With experience in the private sector and research interests in a variety of fields from meteorology to engineering, Assistant Professor of Data Science Kristen Gore brings a valuable perspective to Willamettes School of Computing & Information Sciences. During her career, she has drawn on the inspiration of engineers and scholars throughout history who have paved the way for women in technology.
Gore recently presented about the impact women have had in the technology sector at WU TechDay, a day-long event on Willamettes Salem campus. We reached out to learn more about gender inequities in technology fields and what can be done to address them.
1. You have a distinct professional path to data science. How did your journey through multiple fields and through the private sector inform your perspective?
Gore: Ive always known that I was interested in a lot of different fields (aerospace engineering, meteorology, education statistics, etc.), so I needed a degree that would give me a skillset I could use in a variety of applications. Thats why adding statistics as a second major was one of my best decisions. It opened my world to many different possibilities. Its one of the few majors that lets you switch from meteorology to biostatistics to semiconductor physics to business analytics. I never feel pigeonholed because of it.
As for my experience in the private sector, working for a global tech company like Hewlett-Packard shaped how I approach problems because as a consultant, I knew that the final answer was never solely determined by the mathematical answer. The macro-level business interests, budget and resource constraints, staffing limitations, schedules, project scope, and business priorities all had to be considered in my recommendations. It framed my viewpoint that the implementation of data science methods has to be rooted in practicality.
2. Are there any women in the history of technology that particularly inspire you?
Gore: Absolutely too many to name, but here are my favorites. First, my NASA Fab 5: Valerie Thomas, Melba Roy Mouton, Katherine Johnson, Mary Jackson, and Dorothy Vaughan. Advancements in aerospace engineering have led to a lot of advancements in the fields of reliability and statistical engineering, which are my fields of research , so Im thankful for the contributions these women have made to not only the field of aerospace engineering but in the paths they paved for other underrepresented minorities in STEM.
Im also a huge fan of Cathy ONeil and Joy Buolamwini for the contributions theyve made in the field of AI ethics. Anyone reading this right now should buy their books immediately.
3. What does the data say about the state of women and gender minorities in technology fields today?
Gore: We have a lot to do to increase representation of women and gender minorities in STEM. In almost all subfields of STEM (except life sciences), the number of women lags behind that of men. This trend also persists in college major declaration statistics.
4. What is causing this disparity?
Gore: This is a difficult question to answer because there are mechanisms at every stage of the pipelinefrom K-12, to college, to the workforcethat are contributing to the underrepresentation. Working backward chronologically, women in the workforce are often subjected to unfavorable workplace environments, which can range from persistent microaggressions to inflexible working arrangements.
In college, women in non-STEM degree programs outnumber the women in STEM degree programs, and by this stage, many of their minds have been made up in terms of not pursuing a STEM career/major. And this is often due to key elements of their K-12 experience elements which can be self-feeding if not addressed early in students development.
When students are free to explore, make mistakes, and learn in the absence of outside influences, they can learn in a reduced-pressure environment and discover their love for different subjects including STEM. I cant emphasize how important this growth mindset is. Several studies have shown that girls interest in STEM dramatically decreases around 7th grade, and these sentiments can remain with them throughout their high school years. Its crucial that girls and underrepresented gender minorities have positive reinforcement early in their K-12 journey.
5. What are some ways we can address this gap?
Gore: There are things we can do at each stage of the pipeline. In K-12, we can encourage educators to foster a positive learning environment and limit biasing behaviors that might reduce girls confidence in their ability to succeed in STEM. Also, encouraging girls to participate in programs like Hour of Code, Black Girls Code, Science Olympiads, and Future City can expose them to ways STEM can be really fun. I think programs like Willamette Academy are excellent in exposing students to the myriad of possibilities that exist in STEM and non-STEM fields.
At the college level, we can conduct regular program assessments to make sure that our curricula are serving all demographics of students. Were doing this in the School of Computing & Information Sciences and are partnering with other departments to do the same.
In the workforce, we can ensure that fair compensation and promotional practices are being upheld. These analyses are anything but trivial, but theyre essential in making sure women are being fairly treated in the workplace. I have personally benefited from sponsorship programs in which upper-level managers play an active role in creating opportunities for promising talent, and I would absolutely advocate for similar programs at other companies.
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Data Science Syllabus and Subjects: Here’s What you Should Know Before Opting for a Course – Simplilearn
Data science is often considered the twenty-first century's most lucrative career pathway, pivotal to organizations' operations and service delivery worldwide. With the demand for data scientists soaring globally, educational institutions strive to cater to this need.
Key Takeaways:
A Data Science program equips learners with the ability to manipulate structured and unstructured data through various tools, algorithms, and software, emphasizing the development of critical Data Science skills. It is essential for participants to understand the data science course outline, which includes the acquisition of these skills, before choosing an educational establishment.
The foundational subjects of any data science curriculum or degree encompass Statistics, Programming, Machine Learning, Artificial Intelligence, Mathematics, and Data Mining, regardless of the course's delivery mode.
While the data science syllabus remains consistent across various degrees, the projects and elective components may vary. For instance, the B.Tech., Data Science syllabus, compared to the B.Sc., in Data Science, also includes practical labs, projects, and thesis work. Similarly, the M.Sc., in Data Science emphasizes research-oriented studies, including specialized training and research initiatives.
Students pursuing data science gain comprehensive insights into handling diverse data types and statistical analysis. The curriculum is structured to ensure students acquire a deep understanding of various strategies, skills, techniques, and tools essential for managing business data effectively. The courses offer focused education and training in areas such as statistics, programming, algorithms, and analytics. Through this training, students develop the skills necessary to uncover solutions and contribute to impactful decision-making. These students become adept at navigating different data science roles and are thoroughly equipped to be recruited by leading companies.
The best data science programs are designed to equip students with robust skills and knowledge, preparing them for the dynamic field of data science. These programs cover a comprehensive curriculum that spans technical subjects, theoretical foundations, and practical applications. Heres a detailed look at some of the core subjects that are crucial for any top-tier data science program:
Understanding statistics and probability is fundamental to data science. This subject covers descriptive statistics, inferential statistics, probability distributions, hypothesis testing, and statistical modeling. Mastery in statistics allows data scientists to analyze data effectively, make predictions, and infer insights from data.
An essential skill for data scientists. Python and R are the most common languages due to their simplicity and the powerful libraries they offer for data analysis (like Pandas, NumPy, Matplotlib, Seaborn in Python, and ggplot2, and dplyr in R). A good data science program will cover programming fundamentals, data structures and algorithms, and software engineering principles.
It teaches computers to learn from data and make decisions or predictions. Core topics include supervised learning (regression and classification), unsupervised learning (clustering, dimensionality reduction), neural networks, deep learning, reinforcement learning, and the practical application of these algorithms.
Data mining extracts valuable information from large datasets. This subject covers data preprocessing, data cleaning, data exploration, and the use of algorithms to discover patterns and insights. Data wrangling focuses on transforming and mapping data from its raw form into a more suitable format for analysis.
Knowledge of databases is crucial for managing data. This includes understanding relational databases (SQL), NoSQL databases, and big data technologies such as Hadoop, Spark, and cloud storage solutions. These tools help efficiently store, retrieve, and process large volumes of data.
It is the graphical representation of information and data. It uses visual elements like charts, graphs, and maps to see and understand data trends, outliers, and patterns. Tools like Tableau and Power BI and programming libraries such as Matplotlib and ggplot2 are commonly taught.
With great power comes great responsibility. Data science programs also address the ethical considerations and legalities surrounding data privacy, data protection, and bias in machine learning models. Understanding these aspects is critical to ensuring that data science practices are ethical and respectful of user privacy.
Many programs offer courses tailored to specific industries like healthcare, finance, or marketing. These courses focus on applying data science techniques to solve industry-specific problems, offering students insights into how data science can drive decision-making in various fields.
Top programs often allow students to specialize in areas of interest through electives. These might include advanced machine learning, artificial intelligence, natural language processing, computer vision, or robotics.
Hands-on projects and capstone courses are integral to applying what students have learned in real-world scenarios. They provide practical experience in problem-solving, data analysis, and model development, preparing students for the challenges they will face in their careers.
Regardless of whether you opt for an online course, a traditional classroom setting, or a full-time university degree, the data science course outline remains consistent. While the specific projects undertaken in each course may vary, every data science curriculum must encompass the fundamental concepts of data science, which are listed below:
Data Visualization
Machine Learning
Deep Learning
Data Mining
Programming Languages
Statistics
Cloud Computing
EDA
Artificial Intelligence
Big Data
Data Structures
NLP
Business Intelligence
Data Engineering
Data Warehousing
DB Management
Liner Algebra
Linear Regression
Spatial Sciences
Statistical Interference
Probability
It involves representing data in a graphical or visual format to communicate information clearly and efficiently. It employs tools like charts, graphs, and maps to help users understand trends, outliers, and patterns in data.
It is a subset of AI enabling systems to learn and improve from experience. It includes algorithms for classification, regression, clustering, and recommendation systems.
Deep Learning employs multi-layered neural networks to sift through vast data sets. It plays a crucial role in powering image and speech recognition, natural language processing, and developing self-driving cars, by enabling complex, pattern-based data analysis.
It involves extracting valuable information from large datasets to identify patterns, correlations, and trends. It combines machine learning, statistics, and database systems to turn data into actionable knowledge.
Key programming languages in data science include Python, and R. Python is favored for its simplicity and rich ecosystem of data libraries (e.g., Pandas and NumPy), while R specializes in statistical analysis and graphical models.
Statistics is fundamental in data science for analyzing data and making predictions. It covers descriptive statistics, inferential statistics, hypothesis testing, and various statistical models and methods.
Cloud Computing provides scalable resources for storing, processing, and analyzing large datasets in data science. Services like AWS, Google Cloud, and Azure offer platforms for deploying machine learning models and big data processing.
EDA is the process of analyzing datasets to summarize their main characteristics, often using visual methods. It's a critical step before formal modeling commences, helping to uncover patterns, anomalies, and relationships.
AI encompasses techniques that enable machines to mimic human intelligence, including reasoning, learning, and problem-solving. It's a broader field that includes machine learning, deep learning, and other algorithms.
Big Data encompasses extensive datasets that are computationally analyzed to uncover patterns, trends, and connections. It is distinguished by its large volume, rapid flow, and diverse types, posing challenges to conventional data processing tools due to its complexity and scale.
Data Structures organize and store data to be accessed and modified efficiently. Important structures include arrays, lists, trees, and graphs, which are fundamental in optimizing algorithms in data science.
NLP is the intersection of computer science, AI, and linguistics, focused on facilitating computers to understand, interpret, and generate human language.
BI involves analyzing business data to provide actionable insights. It includes data aggregation, analysis, and visualization tools to support decision-making processes.
Data Engineering focuses on the practical application of data collection and pipeline architecture. It involves preparing 'big data' for analytical or operational uses.
A business's electronic storage system designed for holding vast amounts of data, aimed at facilitating query and analysis rather than processing transactions. It serves as a unified database that consolidates data from various sources, ensuring the data is integrated and organized for comprehensive analysis.
Database Management encompasses the processes and technologies involved in managing, storing, and retrieving data from databases. It ensures data integrity, security, and availability.
Linear algebra, a fundamental field of mathematics, focuses on the study of vector spaces and the linear transformations connecting them. It serves as a critical foundation for the algorithms that underpin machine learning and data science, enabling the handling and analysis of data in these advanced fields.
This statistical technique analyzes the connection between a primary variable and one or several predictors, aiming to forecast future outcomes or make predictions.
Spatial Sciences study the phenomena related to the position, distance, and area of objects on Earth's surface. It's used in mapping, geographic information systems (GIS), and spatial analysis.
It draws conclusions about a population from a sample. It includes techniques like estimating population parameters, hypothesis testing, and confidence intervals.
It is the branch of mathematics that calculates the likelihood of a given event's occurrence, which is fundamental in statistical analysis and modeling uncertainties in data science.
The Bachelor of Technology (B.Tech) in Data Science and Engineering offered by the Indian Institutes of Technology (IITs) is a comprehensive program designed to equip students with the fundamentals and advanced knowledge required in the field of data science and engineering. Here's an overview of what students can expect from a B.Tech in Data Science and Engineering curriculum at an IIT:
The Bachelor of Science (BSc) in Data Science is an undergraduate program designed to equip students with the foundational knowledge and practical skills required in the field of data science. Below is an outline of a typical BSc Data Science program curriculum:
It is a significant component of the BSc Data Science program, usually completed in the final year. This project allows students to apply their accumulated knowledge and skills to solve a real-world data science problem, often in collaboration with industry partners or academic research teams.
The B.Tech in Data Science is an undergraduate program designed to equip students with the foundational knowledge and skills necessary to enter the field of data science and analytics. Here's an overview of the typical curriculum structure:
The MSc Data Science program curriculum is designed to help students comprehensively understand data science, analytics, and advanced computational methods. Below is a generalized year-wise breakdown of the curriculum for an MSc Data Science program.
Simplilearn is the worlds #1 online learning platform that offers diverse professional certification courses, including a comprehensive Data Science Program. This program is designed to cater to beginners and professionals looking to advance their careers in data science.
The prerequisites for a data science course generally include a mix of educational background, technical skills, and analytical acumen. A strong grasp of mathematics, particularly in statistics, probability, and linear algebra, is essential at the foundational level. This mathematical foundation supports understanding algorithms and statistical methods used in data analysis.
Additionally, proficiency in programming languages, notably Python or R, is crucial since these languages are the primary tools used for data manipulation, analysis, and modeling in the field. Familiarity with database management, including SQL, helps manage and query large datasets effectively.
Some courses might also require knowledge of machine learning concepts and tools, though introductory courses may cover these as part of the curriculum. Prior exposure to computer science concepts, especially data structures and algorithms, can be beneficial.
For more advanced courses, an understanding of big data technologies, cloud computing platforms, and experience with data visualization tools might be expected. Academic qualifications can range from a bachelor's degree in a related field or even fields where analytical skills are emphasized.
Yes, coding is a fundamental skill in the field of data science. It is the backbone for analyzing data, building models, and developing algorithms. Data scientists often rely on programming languages such as Python and R, which are equipped with libraries and frameworks.
Coding enables data scientists to manipulate large datasets, extract insights, visualize data trends, and implement machine learning algorithms efficiently. Furthermore, coding skills are essential for automating tasks, cleaning data, and creating reproducible research.
Simplilearn offers a range of courses and learning paths for individuals interested in data science, with Python being a central focus in many of these educational offerings. The Data Science Masters course equips learners with the necessary skills to enter the field of data science and analytics. This course typically covers fundamental to advanced concepts of Python programming, along with its application in data science, including libraries such as NumPy, pandas, Matplotlib, and Scikit-learn.
Simplilearn's Data Science courses provide a comprehensive understanding of key data science concepts, tools, and techniques. With industry-recognized certification, hands-on projects, and expert-led training, our courses help learners gain the skills needed to succeed in the data-driven world. Upgrade your career with Simplilearn today!
For those looking to take a significant step forward in their data science journey, the Data Scientist Masters program offered by Simplilearn stands out as a premier choice. This program goes beyond the standard curriculum to offer hands-on experience with real-world projects, ensuring that learners understand the theoretical aspects and gain practical skills.
Yes, pursuing an education in Data Science is a viable job path. The demand for data scientists continues to grow across industries due to the increasing importance of big data and analytics in decision-making processes, offering high earning potential and job security.
A Data Scientist analyzes large data sets to derive actionable insights and inform decision-making. They use statistical analysis, machine learning, and data visualization techniques to interpret complex data and communicate findings to stakeholders.
The time to become a professional data scientist varies, typically ranging from a few months to several years, depending on the individual's background, learning pace, and the depth of knowledge and skills they aim to acquire.
Yes, you can study Data Science online. Numerous platforms and institutions offer online courses, certifications, and degree programs in Data Science, catering to beginners and advanced learners and providing flexibility to study at your own pace.
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Uncovering the EU AI Act. The EU has moved to regulate machine | by Stephanie Kirmer | Mar, 2024 – Towards Data Science
The EU has moved to regulate machine learning. What does this new law mean for data scientists? Photo by Hansjrg Keller on Unsplash
The EU AI Act just passed the European Parliament. You might think, Im not in the EU, whatever, but trust me, this is actually more important to data scientists and individuals around the world than you might think. The EU AI Act is a major move to regulate and manage the use of certain machine learning models in the EU or that affect EU citizens, and it contains some strict rules and serious penalties for violation.
This law has a lot of discussion about risk, and this means risk to the health, safety, and fundamental rights of EU citizens. Its not just the risk of some kind of theoretical AI apocalypse, its about the day to day risk that real peoples lives are made worse in some way by the model youre building or the product youre selling. If youre familiar with many debates about AI ethics today, this should sound familiar. Embedded discrimination and violation of peoples rights, as well as harm to peoples health and safety, are serious issues facing the current crop of AI products and companies, and this law is the EUs first effort to protect people.
Regular readers know that I always want AI to be well defined, and am annoyed when its too vague. In this case, the Act defines AI as follows:
A machine-based system designed to operate with varying levels of autonomy that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments.
So, what does this really mean? My interpretation is that machine learning models that produce outputs that are used to influence the world (especially peoples physical or digital conditions) fall under this definition. It doesnt have to adapt live or retrain automatically, although if it does thats covered.
But if youre building ML models that are used to do things like
These will all be covered by this law, if your model effects anyone who is a citizen of the EU and thats just to name a few examples.
All AI is not the same, however, and the law acknowledges that. Certain applications of AI are going to be banned entirely, and others subjected to much higher scrutiny and transparency requirements.
These kinds of systems are now called Unacceptable Risk AI Systems and are simply not allowed. This part of the law is going into effect first, six months from now.
This means, for example, you cant build (or be forced to submit to) a screening that is meant to determine whether youre happy enough to get a retail job. Facial recognition is being restricted to only select, targeted, specific situations. (Clearview AI is definitely an example of that.) Predictive policing, something I worked on in academia early in my career and now very much regret, is out.
The biometric categorization point refers to models that group people using risky or sensitive traits like political, religious, philosophical beliefs, sexual orientation, race, and so on. Using AI to try and label people according to these categories is understandably banned under the law.
This list, on the other hand, covers systems that are not banned, but highly scrutinized. There are specific rules and regulations that will cover all these systems, which are described below.
This is excluding those specific use cases described above. So, emotion-recognition systems might be allowed, but not in the workplace or in education. AI in medical devices and in vehicles are called out as having serious risks or potential risks for health and safety, rightly so, and need to be pursued only with great care.
The other two categories that remain are Low Risk AI Systems and General Purpose AI Models. General Purpose models are things like GPT-4, or Claude, or Gemini systems that have very broad use cases and are usually employed within other downstream products. So, GPT-4 by itself isnt in a high risk or banned category, but the ways you can embed them for use is limited by the other rules described here. You cant use GPT-4 for predictive policing, but GPT-4 can be used for low risk cases.
So, lets say youre working on a high risk AI application, and you want to follow all the rules and get approval to do it. How to begin?
For High Risk AI Systems, youre going to be responsible for the following:
Another thing the law makes note of is that if youre working on building a high risk AI solution, you need to have a way to test it to ensure youre following the guidelines, so there are allowances for testing on regular people once you get informed consent. Those of us from the social sciences will find this pretty familiar its a lot like getting institutional review board approval to run a study.
The law has a staggered implementation:
Note: The law does not cover purely personal, non-professional activities, unless they fall into the prohibited types listed earlier, so your tiny open source side project isnt likely to be a risk.
So, what happens if your company fails to follow the law, and an EU citizen is affected? There are explicit penalties in the law.
If you do one of the prohibited forms of AI described above:
Other violation not included in the prohibited set:
Lying to authorities about any of these things:
Note: For small and medium size businesses, including startups, then the fine is whichever of the numbers is lower, not higher.
If youre building models and products using AI under the definition in the Act, you should first and foremost familiarize yourself with the law and what its requiring. Even if you arent affecting EU citizens today, this is likely to have a major impact on the field and you should be aware of it.
Then, watch out for potential violations in your own business or organization. You have some time to find and remedy issues, but the banned forms of AI take effect first. In large businesses, youre likely going to have a legal team, but dont assume they are going to take care of all this for you. You are the expert on machine learning, and so youre a very important part of how the business can detect and avoid violations. You can use the Compliance Checker tool on the EU AI Act website to help you.
There are many forms of AI in use today at businesses and organizations that are not allowed under this new law. I mentioned Clearview AI above, as well as predictive policing. Emotional testing is also a very real thing that people are subjected to during job interview processes (I invite you to google emotional testing for jobs and see the onslaught of companies offering to sell this service), as well as high volume facial or other biometric collection. Its going to be extremely interesting and important for all of us to follow this and see how enforcement goes, once the law takes full effect.
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