As the world of data grows, so does the world of data science. To keep up with the data science world is a full-time job in itself. The market is ever-growing and tools are developing and dropping into the market causing chaos. And then you have the problem of learning these new tools, understanding their full potential, what can it replace or if its just another add-on.

Keeping up with all of this can be draining. This is why it is important to have the right tools in your data scientist toolbox to excel at what you do.

A good tool improves the way you work. A great tool improves the way you think.

If youre going to choose a programming language for data science - it will most probably be Python. Its a gold standard, with the largest data science user base. A lot of data science tools are written using Python and the community is the largest, fastest growing and most active. Youll be silly not to have this in your toolbox!

Courses to learn Python:

Maths and statistics. The elements of data science that make sure data science makes sense! They are the building blocks of machine learning algorithms. They help you understand a problem and allow you to use them to find a solution. From identifying patterns to outputting desired results from large complex data sets, data scientists can extract insights and reliably interpret results using maths and statistics.

Courses to learn Maths and Statistics:

As a data scientist, you should take pride in your findings and make them look pretty! But also remember that other stakeholders may not be highly technically inclined therefore visualisations are important to them. Its how they understand data science. Being able to visualise your insights in various ways will help you better communicate them without having to do much talking.

There are different libraries you can use such as Matplotlib or there are visualisation tools available such as Tableau - you just need to find which one works for you and your organisation.

Courses to learn data visualisation:

Structured Query Language, SQL for short is a programming language designed for managing data in a relational database. As a data scientist, you will be managing a lot of databases and SQL is your key to combing through the data. With SQL, you will be able to work with structured data stored in databases in which you can easily extract, manipulate, and analyse data. You may want to learn primarily Python or SQL, or you may want to be untouchable and learn both!

Courses to learn SQL:

As the data science, machine learning and artificial intelligence world becomes prominent in our day-to-day lives. It is also important that there are tools and software that developers can use to ensure the pipeline is accurate and effective from start to finish. Frameworks provide a flexible range of software components that help developers accelerate software development to production deployment.

When it comes to frameworks, there are a range of frameworks that are popular in the data science world, for example, TensorFlow, PyTorch, Pandas, Keras and more. As a data scientist, you must learn all of these frameworks as they could be beneficial to you at different times.

Courses to learn different Frameworks:

A data scientist's learning journey is endless. There will always be new tools and software entering the market. However, if you have the right tools in your toolbox, learning new skills will be a breeze.

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.

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5 Tools Every Data Scientist Needs in Their Toolbox in 2024 - KDnuggets

A career in data science involves using statistical, computational and analytical methods to extract insights from data. Data scientists regularly use programming languages like Python and R alongside machine-learning algorithms and data-visualisation software.

The need for data scientists has surged across various sectors, including finance, healthcare and technology, making it a highly sought after and lucrative profession. According to the U.S. Bureau of Labor Statistics, the average annual salary for data scientists in 2023 was $108,020, while demand for them is expected to increase by 35% in the next eight years much faster than average for all occupations.

SEE: What is Data Science? Benefits, Techniques and Use Cases

Online courses and certifications provide accessible pathways into the field, as many can fit around existing responsibilities like a day job. Such programs provide the expertise required for an individual to land their first data science role or just discover whether the career is for them. TechRepublic takes a look at the top six data science courses available in 2024 for learners with different goals and levels of experience.

SEE: How to Become a Data Scientist: A Cheat Sheet

The Data Science Professional Certificate from IBM, hosted on Coursera, offers a great starting point for those interested in learning about data science but dont fully understand what a career in it would entail. This course provides an overview of the tools, languages and libraries used daily by professional data scientists and puts them into practice through a number of exercises and projects. The final Capstone project also requires the student to create a GitHub account, encouraging them to familiarise themselves with the site and collaborate.

$49/38 per month after a seven-day free trial.

Six months at ten hours a week.

None.

DataCamp is another well-regarded provider of data-related courses, and one of its highest rated is titled Associate Data Scientist in Python. It sets itself apart with its unique hands-on coding exercises, one of which involves manipulating and visualising data on Netflix movies. Language-wise, this course exclusively uses Python, but introduces learners to multiple libraries including pandas, Seaborn, Matplotlib and scikit-learn. Knowledge of Python is not required for this course, as the necessary skills are taught along the way.

$13/11 a month for full access.

Nine weeks at ten hours a week.

None.

While many data science courses are taught with Python due to its popularity and simplicity, R Programming A-Z on Udemy is aimed at learners looking to get to grips with R and RStudio. R is a powerful language used frequently in data science for handling complex data sets. This course assumes no prior knowledge and starts with the very basics of R programming, including variables and for() loops, before looking at matrices, vectors and more advanced data manipulation. Large projects that help cement learning use real-world financial and sports data.

$109.99/69.99.

10.5 hours of lectures + exercises.

None.

Applied Data Science Specialization, another course by IBM, fast tracks data science beginners towards skills with real-life applications. Python skills for data analysis and visualisation are taught assuming no prior knowledge of the language and are then put into practice in the interactive labs and projects. These cover the extraction and graphing of financial data, creation of regression models to predict housing prices and visualisation of data treemaps and line plots on Python dashboards. By the end of the course, participants should have solidified their practical Python skills to the extent that they can confidently explore more advanced topics like big data, AI and deep learning.

$49/38 per month after a seven day free trial.

Two months at ten hours a week.

None.

As the title suggests, this course from DeepLearning.ai has a particular focus on mathematics for data scientists. Mathematics underpins the profession and is essential for understanding algorithms, cleaning data, drawing insights, visualisation, evaluating models and more. The course covers the fundamental mathematical toolkit of machine learning, including calculus, linear algebra, statistics and probability. Learners say it provides a good entry point into the theory of data science and the lab exercises are practical.

$49/38 per month after a seven-day free trial.

Six weeks at ten hours a week.

A high school level of mathematics and a basic knowledge of Python is recommended.

The Statistics and Data Science with Python course presented by the Massachusetts Institute of Technology is by far the most comprehensive course featured on this list. The so-called MicroMasters takes learners over a year to complete and prepares them for their first career in data science. It provides a graduate-level introduction to concepts such as statistical inference and linear models, as well as practical experience building machine learning algorithms. It is designed to fit around a day job or university study while not compromising on the level of content.

$1,350/1,186.

One year and two months at ten hours a week.

University-level calculus and comfort with mathematical reasoning and Python programming are recommended.

The key difference between data analysis and data science is that the former primarily looks to interpret existing data, while the latter involves creating new ways of doing so.

Data analysis focuses on examining datasets to identify trends, draw conclusions and support business decisions. It involves cleaning, transforming and modelling data to extract useful information, often using tools like Excel and SQL. It is performed by data analysts who are typically hired into a wide range of industries, including marketing firms, government agencies, healthcare providers, financial institutions and more.

Data science, on the other hand, integrates data analysis with advanced machine learning algorithms, predictive modelling and big data technologies. Data scientists often develop new tools and methods to handle complex problems and derive insights from large-scale datasets. Skills required for this include proficiency in programming languages such as Python and R, as well as a deeper understanding of statistical methods and machine learning.

SEE: 10 Signs You May Not Be Cut Out for a Data Scientist Job

Data science remains in high demand in 2024. The IDC estimates that the amount of data worldwide will reach 291 zettabytes by 2027, and as growth continues, more data professionals will be needed to manipulate and interpret it. Furthermore, many of the key industries within which data scientists work are expanding, such as AI, machine learning and the Internet of Things, while others provide core services such as healthcare, energy, finance and logistics. Salaries also reflect this high demand as, according to Glassdoor, the average base pay of a data scientist in the U.S. is $113,000.

Opinions on online data science courses vary within the industry. For some, there are enough free resources available through platforms like YouTube to render paid courses unnecessary. They may also argue that there is no substitute for hands-on experience, and that even beginners should learn the necessary skills by downloading an open-source dataset and attempting to manipulate it themselves.

However, the key to learning anything new is persistence, and it can be difficult to remain motivated without a defined learning programme to follow, coursemates to connect with or a course fee at risk of going to waste. For individuals with a tendency to start projects but not finish them, an initial investment in a structured course may provide the motivation they need. Many paid courses also give direct access to qualified instructors who can provide tailored help that would otherwise not be available.

Ultimately, there are certainly opportunities to break into data science without taking any type of online course. However, if structured learning provides the skills or motivation you desire, then the investment may be worth it.

When assessing online courses, TechRepublic examined the reliability and popularity of the provider, the depth and variety of topics offered, the practicality of the information, the cost and the duration. The courses and certification programs vary considerably, so be sure to choose the option that is right for your goals and learning style.

Continued here:

The 7 Best Data Science Courses That are Worth Taking in 2024 - TechRepublic

14 min read

The stellar performance of large language models (LLMs) such as ChatGPT has shocked the world. The breakthrough was made by the invention of the Transformer architecture, which is surprisingly simple and scalable. It is still built of deep learning neural networks. The main addition is the so-called attention mechanism that contextualizes each word token. Moreover, its unprecedented parallelisms endow LLMs with massive scalability and, therefore, impressive accuracy after training over billions of parameters.

The simplicity that the Transformer architecture has demonstrated is, in fact, comparable to the Turing machine. The difference is that the Turing machine controls what the machine can do at each step. The Transformer, however, is like a magic black box, learning from massive input data through parameter optimizations. Researchers and scientists are still intensely interested in discovering its potential and any theoretical implications for studying the human mind.

In this article, we will first discuss the four main features of the Transformer architecture: word embedding, attention mechanism, single-word prediction, and generalization capabilities such as multi-modal extension and transferred learning. The intention is to focus on why the architecture is so effective instead of how to build it (for which readers can find many

Original post:

What Does the Transformer Architecture Tell Us? | by Stephanie Shen | Jul, 2024 - Towards Data Science

One of the biggest challenges for drug developers is understanding how to make their drugs work as promised in the lab. And this is where clinical trials come in. The clinical trial process allows developers to test their drugs with human subjects in controlled environments to determine their efficacy and safety.

Indeed, the clinical trial process is crucial for drug development as it can significantly impact patients lives. However, clinical trials can be slow and expensive, with many factors that can affect the outcomes.But fortunately, using data science and adopting advanced technology, such as automating clinical trials with Formedix ryze software, are powerful ways to help drug developers understand how well their drugs work in real-world conditions.

Additionally, platforms like Evidation.com play a crucial role by utilizing real-world research to measure health in everyday life, which helps researchers understand the effectiveness of treatments outside controlled environments. This approach provides valuable insights and generates compelling evidence, accelerating decision-making in clinical trials. To learn more about the benefits of these platforms, read more here.

Furthermore, data scientists have helped doctors determine which patients would benefit most from specific treatments based on their genetic makeup, symptoms, past medical history, and family history. This process allows doctors to provide the best care possible for their patients while also ensuring they do not miss any patients who may need additional care or attention.

That said, read on to learn more about how data science is changing clinical trials.

In the medical field, many clinical studies rely on patients willing to participate in a clinical trial. It can be challenging, as many patients dont want to be involved in studies. One way to ensure that patients participate in clinical trials is by using data science techniques designed to optimize patient recruitment and retention.

The medical field can improve patient recruitment with the help of data scientists who understand how to use machine-learning techniques for this purpose. Notably, these machine learning techniques include identifying high-value targets, developing strategies for reaching them, and evaluating their results once they have been implemented. Data scientists can use these insights to improve future recruitment strategies.

Retention rates are also crucial in ensuring that patients remain engaged in studies. Data scientists may use machine learning techniques to identify factors that lead to high retention rates among participants and then use this information to improve retention rates over time.

Advancements in patient recruitment software powered by AI are further enhancing these efforts, enabling a more efficient and precise approach to identify eligible participants. This technology utilizes vast datasets to optimize patient recruitment strategies, ensuring trials are populated quickly with suitable candidates, thereby reducing timelines and costs.

Clinical trials are vulnerable to poor study design, poor data collection practices, and misleading results. But thats where data science comes in. Its transforming clinical trials by strengthening risk-based monitoring. Researchers can achieve it by identifying and analyzing the relationship between clinical trial data and the variables in the clinical trial environment.

The main goal of this strategy is to improve clinical trials by preventing adverse events during the process. In addition, it helps to identify any potential issues that may arise during the process and make sure theyre resolved before proceeding with further research.

Accordingly, the first step in implementing this strategy is data collection from various sources, including patient input surveys and feedback forms, as well as other objective measures such as lab test results or physician observations. Then these data sets are analyzed using statistical methods such as regression analysis or nonparametric statistics.

Then, researchers can get viewable, easy-to-understand reports that show all the information they need on each patient and how they fared while participating in the trial.

Clinical trials are a crucial part of drug development. Apart from helping researchers test the safety and effectiveness of new drugs, they also provide data that can inform future research, which is essential for companies working on different kinds of medicines.

However, there has been a long-standing problem with the predictability of clinical trials. Researchers have long struggled to determine how successful their clinical trials will be. And they often cant predict this until after patients have been enrolled in the study. The uncertainty about the value of a test can make it difficult for pharmaceutical companies to invest in drug research.

Accordingly, data science has helped address this issue by providing evidence about how well clinical trials are likely to perform in practice. The ability to use machine learning algorithms to predict how successful a clinical trial is will be based on its design. It allows pharmaceutical companies to make more informed decisions about whether or not to invest resources into them.

Data science is revolutionizing clinical trials by helping identify ideal locations for clinical trials. Clinical trials are usually conducted at multiple sites around the country, which increases their cost and slows down the process of getting new drugs to market.

Using machine learning techniques, data scientists can identify which sites are most suitable for conducting clinical trials based on factors such as proximity and other resources that must be made available. They can then use this information to help create a list of potential sites that meet all criteria needed for conducting a trial effectively.

With that, companies no longer have to spend money on traveling costs or rent out unused buildings. They can conduct clinical trials in one location instead of spreading them out over multiple locations.

Conclusion

The role of data science in clinical trials will be essential to watch in the coming years. As pharmaceutical companies try to streamline this expensive, time-consuming process, finding ways to use data science more effectively will be critical. Itll lead to more drugs being approved by the FDA and help drive down the costs associated with clinical trials.

See the article here:

How Data Science Is Transforming The Clinical Trial Process - TechiExpert.com

The surprising generalisation beyond overfitting 8 min read

As someone who spent considerable time with various computer science topics, where mathematical abstractions can sometimes be very dry and abstract, I find the practical, hands-on nature of data science to be a breath of fresh air. It never fails to amaze me how even the simplest ideas can lead to fascinating results.

This article faces one of these surprising revelations I just stumbled upon.

Ill never forget how the implementation of my Bachelors thesis went. While it was not about machine learning, it had a formative effect on me, and Ill manage to constantly remind myself of it when tinkering with neural networks. It was an intense time because the thesis was about an analytical model of sound propagation that aimed to run in the browser, thus having very limited performance leading to long running simulations. It constantly failed to complete after running for many hours. But the worst experience was interpreting wrongly configured simulations with confusing results, which often made me think the whole model was nonsense.

The same happens from time to time when I actually train neural networks myself. It can be exhausting to

Link:

I Used to Hate Overfitting, But Now Im Grokking It | by Laurin Heilmeyer | Jul, 2024 - Towards Data Science