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AEGIS London reveals new Data Science and Analytics team to enhance underwriting – Reinsurance News

July 11, 2024 | Author: admin

Lloyds syndicate AEGIS London has established a new Data Science and Analytics team to enhance underwriting capabilities and data-driven initiatives, Led by Giuseppe DAngelo, Head of Data Analytics and Portfolio Underwriting.

The newly formed team reportedly comprises a group of Data Scientists, managed by AI and Data Science expert, Dan Hirlea, and Data Analysts, managed by data visualisation expert and qualified actuary, Balint Bone.

AEGIS London noted that these two bring a wealth of experience and knowledge to the firm, allowing them to extract valuable insights, identify trends, and provide data-driven recommendations for underwriting and portfolio management strategies.

Giuseppe DAngelo added, Data Science and Analytics is an established field within general insurance and in recent years has become a specialism in the London market within high-performing syndicates.

Skill sets are highly sought after so its great that we have been able to put together two strong teams under Dan and Balint.

Day to day, the teams will be retrieving, manipulating, and visualising AEGIS London data, as well as helping put the power of analytics directly into the hands of business users, educating and collaborating with data champions across the business.

AEGIS Londons CEO Alex Powell commented, Maximising the potential of data is one of my key strategic priorities. So, with this team of experts, we will turn our rich pool of data into the raw material for decisions, insights and product development.

Weve been through a major transformation of our finance and operational systems, which has created the platform for advanced data-based decision-making.

Over time, the team will collaborate with other teams within the business to help them better understand what data they have access to and what we can achieve with it.

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AEGIS London reveals new Data Science and Analytics team to enhance underwriting - Reinsurance News

UAMS Awarded $1.3M for High School-Focused Tech and Data Science Program – Arkansas Business

July 11, 2024 | Author: admin

The program will target underserved students in Northwest Arkansas. (Photo provided by UAMS)

UAMS has been awarded a $1.3 million grant from the National Institutes of Health (NIH) for its Arkansas Technology and Data Science in Health and Medicine (AR Tech-DaSH) program.

The five-year grant from the NIHs National Institute of Allergy and Infectious Diseases (NIAID) will support an outreach exposure program focused on technology and data science in health and medicine for high school students, teachers and the community, primarily in northwest Arkansas.

AR Tech-DaSH will incorporate imaging technologies and a data science curriculum focused on health and medicine into classroom outreach programs, a 10-day summer camp and community events.

The program will target underserved and underrepresented students in northwest Arkansas and will revolve around three major health concerns prevalent in the region: obesity and diabetes, cardiovascular and immunology and cancer.

The grant will fund seminars for ninth-grade classes at schools in both rural and urban districts in Northwest Arkansas. Students will experience using a variety of medical-related technologies, such as stethoscopes, ultrasound, infrared and CT imaging, as well as data science-focused activities.

The 10-day summer camp, which will be held once a year, aims to provide 25 students with an integrated exposure to medical-related skills, clinician-patient simulations, research and case-based discussions of primary health concerns. The camp also hopes to provide students with an exposure to exploratory data analysis, data transformation, data mining and machine learning using health or medicine-related datasets.

Students who attend the AR Tech-DaSH camp will be designated as STEM ambassadors and will design and implement outreach events in their local communities with input from community stakeholders.

Also part of the program are virtual outreach sessions, which will be provided to rural classrooms across the state. Virtual teacher training workshops plan to show teachers how to incorporate imaging and data science into their classroom curriculum.

The goal is to get students excited about STEM and data science careers so that the future workforce in these fields better reflects the diverse population in the U.S., Kevin Phelan, AR Tech-DaSH program director, said in a press release. Arkansas is a relatively poor, rural state with one of the lowest per capita income and education levels in the country. It faces the same challenges as other states in trying to prepare for the demands of a properly educated and diverse STEM workforce. Arkansas students desperately need early and repeated exposure to STEM and data science to be prepared not only for future careers but also to enable them to make data-driven decisions about lifestyle choices that affect their health.

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UAMS Awarded $1.3M for High School-Focused Tech and Data Science Program - Arkansas Business

Leveraging Python Pint Units Handler Package Part 2 | by Jose D. Hernandez-Betancur | Jul, 2024 – Towards Data Science

July 11, 2024 | Author: admin

Create your customized unit registry for physical quantities in Python Image generated by the author using OpenAIs DALL-E.

Real-world systems, like the supply chain, often involve working with physical quantities, like mass and energy. You don't have to be a professional scientist or engineer to make an app that can scale and let users enter quantities in any unit without the app crashing. Python has a robust and constantly growing ecosystem that is full of alternatives that can be easily integrated and expanded for your application.

Within an earlier post, I talked about the Pint library, which makes working with physical quantities easy. For a more fun way to learn and put together the different parts of our programming puzzle, feel free to go back to the post .

The goal of this article is to provide more information about the Pint package so that we can create a way to store unit definitions that are made on the fly and keep them after the program ends.

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Leveraging Python Pint Units Handler Package Part 2 | by Jose D. Hernandez-Betancur | Jul, 2024 - Towards Data Science

An Off-Beat Approach to Train-Test-Validation Split Your Dataset | by Amarpreet Singh | Jul, 2024 – Towards Data Science

July 11, 2024 | Author: admin

Generated with Microsoft Designer

We all require to sample our population to perform statistical analysis and gain insights. When we do so, the aim is to ensure that our samples distribution closely matches that of the population.

For this, we have various methods: simple random sampling (where every member of the population has an equal chance of being selected), stratified sampling (which involves dividing the population into subgroups and sampling from each subgroup), cluster sampling (where the population is divided into clusters and entire clusters are randomly selected), systematic sampling (which involves selecting every nth member of the population), etc etc. Each method has its advantages and is chosen based on the specific needs and characteristics of the study.

In this article, we wont be focusing on sampling methods themselves per se, but rather on using these concepts to split the dataset used for machine learning approaches into Train-Test-Validation sets. These approaches work for all kinds of Tabular data. We will be working in Python here.

Below are some approaches that you already might know:

This approach uses random-sampling method. Example code:

This approach ensures that the splits maintain the same proportion of classes as the original dataset (with random sampling again of course), which is useful for imbalanced datasets. This approach will work when your target variable is not a continuous variable.

In K-Fold cross-validation, the dataset is split into k subsets (folds). The model is trained on k-1 folds and tested on the remaining fold. This process is repeated k times.

As the name suggests, this is a combination of Stratified sampling and K-fold cross-validation.

Full example usage:

Now, you can use these methods to split your dataset but they have the following limitations:

Now, suppose you have a small total number of observations in your dataset and its difficult to ensure similar distributions amongst your splits. In that case, you can combine clustering and random sampling (or stratified sampling).

Below is how I did it for my problem at hand:

In this method, first, we cluster our dataset and then use sampling methods on each cluster to obtain our data splits.

For example, using HDBSCAN:

You can also use other clustering methods according to your problem for eg. K-Means clustering:

Now you can also add levels of granularities (any categorical variable) to your dataset to get more refined clusters as follows:

Once you have obtained cluster labels from any clustering method, you can use random sampling or stratified sampling to select samples from each cluster.

We will select indices randomly and then use these indices to select our train-test-val sets as follows:

As per my use-case, it was useful to sort my target variable y and then select every 1st, 2nd, and 3rd indices for train, test, and validation set respectively (all mutually exclusive), a.k.a systematic random sampling as below:

The above-discussed approaches of combining clustering with different sampling methods are very useful when you have a small number of observations in your dataset as they ensure to maintain similar distributions amongst the Train, Test and Validation sets.

Thanks for reading, and I hope you find this article helpful!

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An Off-Beat Approach to Train-Test-Validation Split Your Dataset | by Amarpreet Singh | Jul, 2024 - Towards Data Science

Exploring Medusa and Multi-Token Prediction | by Matthew Gunton | Jul, 2024 – Towards Data Science

July 11, 2024 | Author: admin

Speculative Decoding was introduced as a way to speed up inferencing for an LLM. You see, LLMs are autoregressive, meaning we take the output token that we just predicted and use it to help predict the next token we want. Typically we are predicting one-token at a time (or one-token per forward pass of the neural network). However, because the attention pattern for the next token is very similar to the attention pattern from the previous one, we are repeating most of the same calculations and not gaining much new information.

Speculative decoding means that rather than doing one forward pass for one token, instead after one forward pass we try to find as many tokens as we can. In general there are three steps for this:

(1) Generate the candidates

(2) Process the candidates

(3) Accept certain candidates

Medusa is a type of speculative decoding, and so its steps map directly onto these. Medusa appends decoding heads to the final layer of the model as its implementation of (1). Tree attention is how it processes the candidates for (2). Finally, Medusa uses either rejection sampling or a typical acceptance scheme to accomplish (3). Lets go through each of these in detail.

A decoding head takes the internal representation of the hidden state produced by a forward pass of the model and then creates the probabilities that correspond to different tokens in the vocabulary. In essence, it is converting the things the model has learned into probabilities that will determine what the next token is.

Medusa adjusts the architecture of a typical Transformer by appending multiple decoding heads to the last hidden layer of the model. By doing so, it can predict more than just one token given a forward pass. Each additional head that we add predicts one token further. So if you have 3 Medusa heads, you are predicting the first token from the forward pass, and then 3 more tokens after that with the Medusa heads. In the paper, the authors recommend using 5, as they saw this gave the best balance between speed-up and quality.

To accomplish this, the authors of the paper proposed the below decoder head for Medusa:

This equation gives us the probability of token t from the k-th head. We start off by using the weights weve found through training the Medusa head, W1, and multiplying them by our internal state for token t. We use the SiLU activation function to pass through only selective information(SiLU = x * sigmoid(x)). We add to this the internal state a second time as part of a skip connection, which allows the model to be more performant by not losing information during the linear activation of the SiLU. We then multiply the sum by the second set of weights weve trained for the head, W2, and run that product through a softmax to get our probability.

The first Medusa heads give the model probabilities they should consider based off the forward pass, but the subsequent Medusa heads need to figure out what token they should pick based off what the prior Medusa heads chose.

Naturally, the more options the earlier Medusa heads put forward (hyperparameter sk), the more options future heads need to consider. For example, when we consider just the top two candidates from head 1 (s1=2) and the top three from head 2 (s2=3), we wind up with 6 different situations we need to compute.

Due to this expansion, we would like to generate and verify these candidates as concurrently as possible.

The above matrix shows how we can run all of these calculations within the same batch via tree attention. Unlike typical causal self-attention, only the tokens from the same continuation are considered relevant for the attention pattern. As the matrix illustrates with this limited space, we can fit our candidates all into one batch and run attention on them concurrently.

The challenge here is that each prediction needs to consider only the candidate tokens that would be directly behind it. In other words, if we choose It from head 1, and we are evaluating which token should come next, we do not want to have the attention pattern for I being used for the tokens.

The authors avoid this kind of interference by using a mask to avoid passing data about irrelevant tokens into the attention calculation. By using this mask, they can be memory efficient while they calculate the attention pattern & then use that information in the decoding head to generate the subsequent token candidates.

While the above matrix shows us considering every prediction the same, if we have a probability for each prediction, we can treat these differently based on how likely they are to be the best choice. The below tree visualizes just that.

Exploring Medusa and Multi-Token Prediction | by Matthew Gunton | Jul, 2024 - Towards Data Science

Perception-Inspired Graph Convolution for Music Understanding Tasks | by Emmanouil Karystinaios | Jul, 2024 – Towards Data Science

July 11, 2024 | Author: admin

This article discusses MusGConv, a perception-inspired graph convolution block for symbolic musical applications 10 min read

In the field of Music Information Research (MIR), the challenge of understanding and processing musical scores has continuously been introduced to new methods and approaches. Most recently many graph-based techniques have been proposed as a way to target music understanding tasks such as voice separation, cadence detection, composer classification, and Roman numeral analysis.

This blog post covers one of my recent papers in which I introduced a new graph convolutional block, called MusGConv, designed specifically for processing music score data. MusGConv takes advantage of music perceptual principles to improve the efficiency and the performance of graph convolution in Graph Neural Networks applied to music understanding tasks.

Traditional approaches in MIR often rely on audio or symbolic representations of music. While audio captures the intensity of sound waves over time, symbolic representations like MIDI files or musical scores encode discrete musical events. Symbolic representations are particularly valuable as they provide higher-level information essential for tasks such as music analysis and generation.

However, existing techniques based on symbolic music representations often borrow from computer vision (CV) or natural language processing (NLP) methodologies. For instance, representing music as a pianoroll in a matrix format and treating it similarly to an image, or, representing music as a series of tokens and treating it with sequential models or transformers. These approaches, though effective, could fall short in fully capturing the complex, multi-dimensional nature of music, which includes hierarchical note relation and intricate pitch-temporal relationships. Some recent approaches have been proposed to model the musical score as a graph and apply Graph Neural Networks to solve various tasks.

The fundamental idea of GNN-based approaches to musical scores is to model a musical score as a graph where notes are the vertices and edges are built from the temporal relations between the notes. To create a graph from a musical score we can consider four types of edges (see Figure below for a visualization of the graph on the score):

A GNN can treat the graph created from the notes and these four types of relations.

MusGConv is designed to leverage music score graphs and enhance them by incorporating principles of music perception into the graph convolution process. It focuses on two fundamental dimensions of music: pitch and rhythm, considering both their relative and absolute representations.

Absolute representations refer to features that can be attributed to each note individually such as the notes pitch or spelling, its duration or any other feature. On the other hand, relative features are computed between pairs of notes, such as the music interval between two notes, their onset difference, i.e. the time on which they occur, etc.

The importance and coexistence of the relative and absolute representations can be understood from a transpositional perspective in music. Imagine the same music content transposed. Then, the intervalic relations between notes stay the same but the pitch of each note is altered.

To fully understand the inner workings of the MusGConv convolution block it is important to first explain the principles of Message Passing.

In the context of GNNs, message passing is a process where vertices within a graph exchange information with their neighbors to update their own representations. This exchange allows each node to gather contextual information from the graph, which is then used to for predictive tasks.

The message passing process is defined by the following steps:

MusGConv alters the standard message passing process mainly by incorporating both absolute features as node features and relative musical features as edge features. This design is tailored to fit the nature of musical data.

The MusGConv convolution is defined by the following steps:

By designing the message passing mechanism in this way, MusGConv attempts to preserve the relative perceptual properties of music (such as intervals and rhythms), leading to more meaningful representations of musical data.

Should edge features are absent or deliberately not provided then MusGConv computes the edge features between two nodes as the absolute difference between their node features. The version of MusGConv with the edges features is named MusGConv(+EF) in the experiments.

To demonstrate the potential of MusGConv I discuss below the tasks and the experiments conducted in the paper. All models independent of the task are designed with the pipeline shown in the figure below. When MusGConv is employed the GNN blocks are replaced by MusGConv blocks.

I decided to apply MusGConv to four tasks: voice separation, composer classification, Roman numeral analysis, and cadence detection. Each one of these tasks presents a different taxonomy from a graph learning perspective. Voice separation is a link prediction task, composer classification is a global classification task, cadence detection is a node classification task, and Roman numeral analysis can be viewed as a subgraph classification task. Therefore we are exploring the suitability of MusGConv not only from a musical analysis perspective but through out the spectrum of graph deep learning task taxonomy.

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Perception-Inspired Graph Convolution for Music Understanding Tasks | by Emmanouil Karystinaios | Jul, 2024 - Towards Data Science

Catching online scammers: our model combines data and behavioural science to map the psychological games cybercriminals play – The Conversation Canada

July 11, 2024 | Author: admin

When fictions most famous detective, Sherlock Holmes, needed to solve a crime, he turned to his sharp observational skills and deep understanding of human nature. He used this combination more than once when facing off against his arch-nemesis, Dr James Moriarty, a villain adept at exploiting human weaknesses for his gain.

This classic battle mirrors todays ongoing fight against cybercrime. Like Moriarty, cybercriminals use cunning strategies to exploit their victims psychological vulnerabilities. They send deceptive emails or messages that appear to be from trusted sources such as banks, employers, or friends. These messages often contain urgent requests or alarming information to provoke an immediate response.

For example, a phishing email might claim there has been suspicious activity on a victims bank account and prompt them to click on a link to verify their account details. Once the victim clicks the link and enters their information, the attackers capture their credentials for malicious use. Or individuals are manipulated into divulging confidential information to compromise their own or a companys security.

Holmes had to outsmart Moriarty by understanding and anticipating his moves. Modern cybersecurity teams and users must stay vigilant and proactive to outmanoeuvre cybercriminals who continuously refine their deceptive tactics.

Read more: Deepfakes in South Africa: protecting your image online is the key to fighting them

What if those trying to prevent cybercrime could harness Holmess skills? Could those skills complement existing, more data-driven ways of identifying potential threats? I am a professor of information systems whose research focuses on, among other things, integrating data science and behavioural science through a sociotechnical lens to investigate the deceptive tactics used by cybercriminals.

Recently, I worked with Shiven Naidoo, a Masters student in data science, to understand how behavioural science and data science could join forces to combat cybercrime.

Our study found that, just as Holmess analytical genius and his sidekick Dr John Watsons practical approach were complementary, behavioural scientists and data scientists can collaborate to make cybercrime detection and prevention models more effective.

Data science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

When its powerful algorithms are applied to complex and large datasets they can identify patterns that indicate potential cyber threats. Predictive analysis helps cybersecurity teams anticipate and prevent large-scale attacks. This is done through, for instance, detecting anomalies in sentence structure to spot scams.

However, relying solely on data science often overlooks the human factors that drive cybercriminal behaviour.

The behavioural sciences study human behaviour. They consider the principles that influence decision-making and compliance. We drew extensively from US psychologist Robert Cialdinis social influence model in our study.

This model has been applied in cybersecurity studies to explain how cybercriminals exploit psychological tendencies.

Read more: Five things South Africa must do to combat cybercrime

For example, cybercriminals exploit humans tendency to be obedient to authority by impersonating trusted figures to spread disinformation. They also exploit urgency and scarcity principles to prompt hasty actions. Social proof the tendency to follow the actions of those similar to us is another tool, used to manipulate users into complying with fraudulent requests. For instance, cybercriminals might create fake reviews or testimonials, prompting users to fall for a scam.

We adapted the social influence model to detect cybercriminal tactics in scam datasets by combining behavioural and data science. Scam datasets consist of unstructured data, which includes complex text data such as phishing emails and fake social media posts. Our data consisted of known scams such as phishing and other malicious activities. It came from FraudWatch Internationals Cyber Intelligence Datafeed, which collects information on cybercrime incidents.

Its tough to draw insights from unstructured data. Models cant easily discern between meaningful data points and those that are irrelevant or misleading (we call it noisy data). Data scientists rely on feature engineering to cut through the noise. This process identifies and labels meaningful data points using knowledge from other fields.

We used domain knowledge from behavioural science to engineer and label meaningful features in unstructured scam data. Scams were labelled based on how they used Cialdinis social influence principles, transforming raw text data into meaningful features. For example, a phishing email might use the principle of urgency by saying your account will be locked in 24 hours if you do not respond!. The raw text is transformed into a meaningful feature labelled urgency, which can be analysed for patterns. Then we used machine learning to analyse and visualise the labelled dataset.

The results showed that certain social influence principles such as liking and authority were frequently used together in scams. We also found that phishing scams often employed a mix of several principles. This made them more sophisticated and harder to detect.

The results gave us valuable insights into how often different types of social influence principles (such as urgency, trust, familiarity) are exploited by cybercriminals, as well as where more than one type is used at a time. Analysing unstructured text data like phishing emails and fake social media posts allowed us to identify patterns that indicated manipulative tactics.

Overall, our work yielded high quality insights from complex scam datasets.

Its important to mention that our dataset was not exhaustive. However, we believe our results are invaluable for mining insights from complex cybercrime data. This kind of analysis can be used by cybersecurity professionals, data scientists, cybersecurity firms and organisations involved in cybersecurity research. It can help improve automated detection systems and inform targeted training.

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Catching online scammers: our model combines data and behavioural science to map the psychological games cybercriminals play - The Conversation Canada

A Weekend AI Project: Object Detection with YOLO on PC and Raspberry Pi | by Dmitrii Eliuseev | Jul, 2024 – Towards Data Science

July 11, 2024 | Author: admin

Running the Latest YOLO v10 Model on Different Hardware YOLO Objects Detection, Image by author

Computer vision can be an important part of ML apps of different scales, from $20,000 Tesla Bots or self-driving cars to smart doorbells and vacuum cleaners. It is also a challenging task because, compared to a cloud infrastructure, on real edge devices, the hardware specs are often much more constrained.

YOLO (You Only Look Once) is a popular object detection library; its first version was made in 2015. YOLO is particularly interesting for embedded devices because it can run almost anywhere; there are not only Python but also C++ (ONNX and OpenVINO) and Rust versions available. A year ago, I tested YOLO v8 on a Raspberry Pi 4. Nowadays, many things have changed a new Raspberry Pi 5 became available, and a newer YOLO v10 was released. So I expect a new model on new hardware to work faster and more precisely.

The code presented in this article is cross-platform, so readers who dont have a Raspberry Pi can run it on a Windows, Linux, or OS X computer as well.

Without further ado, lets see how it works!

For someone who may have never heard about the Raspberry Pi, lets make a short

Online Data Science Training Programs Market size is set to grow by USD 6.54 billion from 2024-2028, Increasing job prospects boost the market,…

July 11, 2024 | Author: admin

NEW YORK, July 9, 2024 /PRNewswire/ -- The global online data science training programs market size is estimated to grow by USD 6.54 billion from 2024 to 2028, according to Technavio, with a CAGR of 34.73% during the forecast period. This growth is driven by increasing job prospects and a trend towards microlearning and gamification. However, the advent of open-source learning materials poses a challenge. Key market players include 2U Inc., Coursera Inc., DataCamp Inc., Harvard University, Intellipaat Software Solutions Pvt. Ltd., Simplilearn, Udacity Inc., and upGrad Education Pvt. Ltd.

Get a detailed analysis on regions, market segments, customer landscape, and companies-View the snapshot of this report

Online Data Science Training Programs Market Scope

Report Coverage

Details

Base year

2023

Historic period

2018 - 2022

Forecast period

2024-2028

Growth momentum & CAGR

Accelerate at a CAGR of 34.73%

Market growth 2024-2028

USD 6542.6 million

Market structure

Fragmented

YoY growth 2022-2023 (%)

25.9

Regional analysis

North America, APAC, Europe, South America, and Middle East and Africa

Performing market contribution

APAC at 47%

Key countries

US, China, Canada, India, and Germany

Key companies profiled

2U Inc., Alison, AnalytixLabs, Coursera Inc., DataCamp Inc., Dataquest Labs Inc., Great Lakes E-Learning Services Pvt. Ltd., Harvard University, Henry Harvin Education Inc., Intellipaat Software Solutions Pvt. Ltd., InventaTeq, Kaplan Inc., Manipal Academy of Higher Education, NIIT Ltd., NYC Data Science Academy, Simplilearn, Udacity Inc., Udemy Inc., and upGrad Education Pvt. Ltd.

Market Driver

The global online data science training programs market is witnessing a rise in the popularity of microlearning. Microlearning delivers content in short, easily digestible formats, including videos and infographics, which can be accessed on-demand. Corporations are increasingly adopting microlearning due to its compatibility with mobile devices and its ability to provide just-in-time learning. Vendors like DataCamp offer mobile versions with gamified, interactive microlearning lessons, enabling efficient knowledge acquisition and addressing learning gaps. This trend towards microlearning and gamification is projected to fuel market expansion during the forecast period.

Online Data Science training programs have seen a significant surge in popularity due to the technological advancement in online education. These programs cover essential topics like Statistics, Math, Data management, Data visualization, Statistical programming, Machine learning, and more. Students and working professionals can now access high-quality Data Science education from anywhere in the world, thanks to live streaming and recorded sessions. Online Statistics courses are particularly beneficial for data science beginners, helping them grasp fundamental statistical concepts. Education technology companies offer flexible and convenient solutions, including digital-curriculum, tutoring platforms, and collaboration opportunities. Certifications and Masters programs add industry recognition for career advancement. Remote work trends and the increasing demand for skilled data scientists make online training programs an attractive option for organizations and decision-makers. Mid-Range Data Scientists can also benefit from these platforms, addressing technical issues and providing access to the latest industry knowledge. Online learning platforms offer a cost-effective alternative to college education, with the added benefits of access to textbooks, communication apps, and artificial intelligence tools. Overall, online Data Science training programs provide flexibility, convenience, and global accessibility, making them an essential part of the Data Science education landscape.

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MarketChallenges

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Segment Overview

This online data science training programs market report extensively covers market segmentation by

1.1Professional degree courses- The professional degree course segment is a significant player in the global online data science training market. It offers intensive and detailed training programs, designed to arm professionals with essential data science skills and knowledge. The curriculum covers essential data science areas like statistical analysis, machine learning, data visualization, and data engineering. Industry experts and academic professionals deliver these courses, ensuring top-notch education. Students engage in live lectures, webinars, and group discussions, fostering a collaborative learning environment. Practical assignments and projects enable learners to apply their knowledge in real-world scenarios, enhancing their proficiency in data science techniques and tools. Upon completion, students receive industry-relevant certifications, increasing their competitiveness in the job market. These certifications validate their acquired knowledge and skills, providing a significant edge in pursuing data science careers. The professional degree course segment's active and comprehensive approach to education is expected to fuel the growth of the global online data science training programs market.

For more information on market segmentation with geographical analysis including forecast (2024-2028) and historic data (2017-2021) - Download a Sample Report

Research Analysis

Online Data Science Training Programs have gained significant popularity in recent years due to the increasing demand for skilled data scientists and the convenience of remote learning. These programs cover various aspects of data science, including Statistics, Math, Data Management, Data Visualization, Statistical Programming, Machine Learning, and more. Students and professionals can learn fundamental statistical concepts, big data technologies, and advanced machine learning algorithms from the comfort of their homes. Online training programs offer Industry recognition through certifications and even Masters degrees, making it an attractive option for career advancement. With remote work trends on the rise, online learning platforms provide Global accessibility, making data science education accessible to anyone with an internet connection. Organizations and decision-makers benefit from having a skilled workforce, and online training programs offer an efficient and cost-effective solution. Online statistics courses are available for data science beginners, and advanced programs cater to professionals looking to expand their skillset. Overall, online data science training programs offer a flexible, convenient, and accessible way to learn data science and advance your career.

Market Research Overview

Online Data Science Training Programs: Unleashing the Power of Statistics, Math, and Machine Learning Online Data Science training programs have gained significant traction in recent times, offering Students and Working Professionals an opportunity to master this high-demand field from the comfort of their homes or workplaces. These programs cover essential topics such as Statistics, Math, Data Management, Data Visualization, Statistical Programming, Machine Learning, and more. Online Education technology companies have been at the forefront of this technological advancement, providing live streaming and recorded sessions, digital-curriculum, tutoring platforms, and collaboration opportunities with industry partners. These platforms offer Mid-Range to advanced courses, catering to Data Scientists, decision-makers, and data science beginners. Online learning platforms provide Flexibility, Convenience, and Global Accessibility, making it an attractive alternative to traditional College education. Industry recognition and career advancement opportunities are significant benefits, as skilled data scientists are in high demand by organizations. Customized training programs, hands-on projects, and real-world applications ensure learners gain practical experience and upskilling/reskilling opportunities. Online certification exams, designed with integrity and security, provide learners with Industry recognition. However, concerns regarding Quality, Industry relevance, and accreditation are valid. Informed decisions should be made based on the platform's reputation, industry partnerships, and the ability to offer practical experience and hands-on training in the online format. Digital technology and Mobile technology have revolutionized data science education, with automation and communication apps streamlining the learning process. Despite these advancements, technical issues may arise, necessitating effective assessments, evaluations, and customer support. In conclusion, Online Data Science Training Programs offer a cost-effective, flexible, and convenient alternative to traditional education methods, providing learners with the skills and knowledge required to excel in this dynamic field.

Table of Contents:

1 Executive Summary 2 Market Landscape 3 Market Sizing 4 Historic Market Size 5 Five Forces Analysis 6 Market Segmentation

7Customer Landscape 8 Geographic Landscape 9 Drivers, Challenges, and Trends 10 Company Landscape 11 Company Analysis 12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research Jesse Maida Media & Marketing Executive US: +1 844 364 1100 UK: +44 203 893 3200 Email:[emailprotected] Website:www.technavio.com/

SOURCE Technavio

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Online Data Science Training Programs Market size is set to grow by USD 6.54 billion from 2024-2028, Increasing job prospects boost the market,...

Master This Data Science Skill and You Will Land a Job In Big Tech Part I | by Khouloud El Alami | Jul, 2024 – Towards Data Science

July 11, 2024 | Author: admin

11 min read

Are you a data scientist dreaming of landing a job in Big Tech but youre not sure what skills you need to get there?

Well, Ive got a secret weapon that could be just what you need to land your dream job in top tech companies.

A few months ago, I wrote this article about all the essential skills you need to get hired by the best tech firms, and today, were going to focus on one of those crucial skills: Experimentation.

Experimentation is a statistical approach that helps us isolate and evaluate the impact of product changes launching features, UX updates, and all!

But why is experimentation so important for standing out among other data scientists?

Its simple. The biggest tech companies are all about creating great products, and experimentation is a vital tool in achieving that.

If you can become an expert in experimentation, youll have a significant advantage over other candidates because most job seekers overlook this skill and dont know how to develop it.

Excerpt from:

Master This Data Science Skill and You Will Land a Job In Big Tech Part I | by Khouloud El Alami | Jul, 2024 - Towards Data Science

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Master This Data Science Skill and You Will Land a Job In Big Tech Part I | by Khouloud El Alami | Jul, 2024 – Towards Data Science

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