#Data Scientists

[datascienceunicorn]

HT @dataelixir

[procurement-insights]

Wen it comes to ProcureTech development and implementation what are the roles of Software and D&A Leaders?

[impact-newswire]

Muhamed’s Journey from Zindi to Ai Computer Vision

Muhamed Tuo As a young schoolboy growing up in Cote d’Ivoire, Muhamed was into gaming. After school, while some of his mates preferred to play football in the streets, he would race home to play Nintendo’s Super Mario Bros., overachieving his score each time. This game wasn’t just a pastime for him, as it sparked a deep curiosity and fascination with software development. “I’d always rush home,…

[vampyre-daisies]

Data science is a scam made up by Big Spreadsheet to sell more empty rectangles

[mitsde123]

The Future of Data Science: Trends to Watch in 2025 and Beyond

Data science continues to evolve rapidly, shaping industries worldwide. In 2025 and beyond, advancements like AI and ML integration will drive automation and innovation, while no-code platforms will make data analysis accessible to all. Sustainability-focused data models and edge computing will revolutionize sectors such as energy and healthcare. As privacy concerns grow, technologies like federated learning will enhance data security. The demand for skilled data scientists is soaring, with online programs offering flexible, industry-relevant learning options. Institutions like MITSDE equip learners with cutting-edge skills and career support, ensuring success in this transformative field.

READ MORE

[techenthuinsights]

Discover how healthcare data scientists are solving real-world medical challenges, improving patient care, and driving innovation through data-driven insights.

[cerebraix]

Interviewing data scientists requires a strategic approach to identify candidates with the right blend of technical skills, analytical thinking, and cultural fit.

[techinfotrends]

Learning data science is easy, getting hands-on expertise in Python programming nuances takes skills. It is highly advised when thinking of a career in the data science industry, it is critical to master Python and other programming languages to facilitate in-time efficient data-driven decision-making.

Python is the most-loved programming language among developers worldwide, TIOBE has ranked it at No.1 in the race for becoming the best programming language in the world. Being a powerful programming language, it lends clarity and concise explanations; that are widely deployed in web development, machine learning, and data science.

PYPL also seconds TIOBE is ranking Python as the top-notch programming language of today. This makes learning Python an inevitable task. It offers a beginner-friendly gateway, massive versatility, extensive libraries, and an active community to grow with. However, as the technology ramps up, Python faces the drawbacks of poor speed and memory management in some cases.

It is time you mastered most in-demand Python libraries such as NumPy, Scikit-Learn, Pandas, TensorFlow, and Matplotlib, among many others. data visualization and many other skills earned at the most trusted data science certifications can take you a long way ahead in earning sky-high data scientist salaries worldwide. Landing your dream data science job is never far away, with the best data science courses.

Bring zeal and ever-strengthening skills to be a lifelong learner to evolve with the enhancing times. Across different states in the USA, the UK, France, Germany, Australia, India, and other countries are brimming with a staggering demand for data scientists. Become a specialized professional and make a positive impact in the multitudinous growth of the global marketplace. This representation shall take you up, close, and personal with Python programming and convenient ways to conquer the nuances with the best facilitators around the world.

[catboybiologist]

By now, there's lots of people have heard about the internal CDC memos for all newly prepared manuscripts (like future scientific papers waiting to be published):

There's so much to comment on, and I'm seeing it all right now. What the state of science is. What this means for the queer community. All of that.

But fuck, I think I might genuinely start crying over this. As a transgender biologist, this feels like a brutally personal blow. I slowly accepted my gender alongside my biology education. The more misinformation that was spewed about "biological sex" by mainstream media, the more my professors, colleagues, and primary sources would casually drop information that proved they have no idea what they're talking about. I'm not an expert on sex determination, gender, or transgender biology specifically by any means. But my worldview has been crafted by my studies in genetics and molecular biology.

Engaging with this research helped me demystify transition. It helped me optimize my transition. It helped me explain how HRT and other steps of trans healthcare work to other people. And it helped me overcome my own internalized transphobia, and finally start transitioning, despite knowing I wanted to since my preteen years.

Who knows how enforceable internal guidelines like this will be. But its certainly going to scare a lot of researchers away from transgender healthcare and science in the coming years, and that breaks my heart.

There's a lot I can say here, but fuck. I just needed to vent for a moment. Fuck.

[kodehashtechnology]

Mastering Data Science: A Guide to Programming Languages

In the dynamic field of data science, mastering programming languages is essential for extracting insights from vast amounts of data. These languages serve as the foundation for data manipulation, analysis, and visualization, enabling data scientists to uncover valuable insights and make data-driven decisions. In this guide, we'll explore the key programming languages for data scientists, highlighting their features, use cases, and importance in mastering the art of data science.

Python:

Python stands out as one of the most popular programming languages for data scientists due to its simplicity, versatility, and robust ecosystem of libraries and frameworks. With libraries such as NumPy, Pandas, and Matplotlib, Python facilitates data manipulation, analysis, and visualization tasks with ease. Its intuitive syntax and readability make it accessible to beginners, while its scalability and performance ensure it remains a favorite among seasoned professionals. Python's extensive support for machine learning and deep learning libraries such as scikit-learn and TensorFlow further solidifies its position as a go-to language for data scientists.

R:

R is another widely used programming language in the field of data science, renowned for its powerful statistical analysis capabilities and extensive collection of packages. Developed specifically for statistical computing and graphics, R excels in data exploration, visualization, and modeling tasks. Its rich ecosystem of packages, including ggplot2, dplyr, and caret, provides data scientists with tools for data manipulation, visualization, and machine learning. R's interactive environment and built-in support for statistical analysis make it a preferred choice for researchers, statisticians, and analysts working with complex datasets.

SQL:

Structured Query Language (SQL) is indispensable for data scientists working with relational databases and large-scale datasets. SQL enables data scientists to query databases, manipulate data, and perform complex analyses with ease. Its declarative syntax and powerful querying capabilities allow for efficient data retrieval, aggregation, and transformation. Data scientists use SQL to extract insights from structured datasets, perform data cleaning and preprocessing tasks, and create reports and visualizations for stakeholders. While SQL is not a traditional programming language, its importance in data science cannot be overstated, particularly in industries where relational databases are prevalent.

Java:

Java is a versatile programming language widely used in enterprise applications, including data-intensive systems and big data processing frameworks. While not as popular among data scientists as Python or R, Java's scalability, performance, and robustness make it well-suited for building and deploying data-intensive applications. Java is commonly used in big data technologies such as Apache Hadoop and Apache Spark for distributed data processing and analysis. Data scientists proficient in Java can leverage its capabilities to build scalable data pipelines, implement distributed algorithms, and deploy machine learning models in production environments.

Julia:

Julia is a high-level programming language designed for numerical and scientific computing, with a focus on performance and ease of use. Julia combines the flexibility of dynamic languages like Python with the speed of statically compiled languages like C or Fortran, making it well-suited for computationally intensive tasks in data science. Julia's syntax is similar to MATLAB and Python, making it accessible to users familiar with these languages. Data scientists use Julia for tasks such as numerical simulations, optimization, and parallel computing, where performance and scalability are critical.

Best Practices for Mastering Programming Languages in Data Science:

Choose the right tool for the job: Understand the strengths and weaknesses of each programming language and select the most appropriate language for your specific data science tasks and requirements.

Practice regularly: Consistent practice is key to mastering programming languages. Work on real-world projects, participate in coding challenges, and collaborate with peers to improve your programming skills.

Explore libraries and frameworks: Familiarize yourself with popular libraries and frameworks in your chosen programming language, as they can significantly streamline your workflow and enhance your productivity.

Stay updated: Data science is a rapidly evolving field, with new tools, techniques, and libraries emerging regularly. Stay abreast of the latest developments and trends in programming languages and data science to remain competitive in the field.

Seek feedback: Solicit feedback from peers, mentors, and experts in the field to identify areas for improvement and refine your programming skills. Actively participate in online communities, forums, and meetups to connect with fellow data scientists and exchange knowledge and ideas.

Conclusion:

Mastering programming languages is essential for success in the field of data science, enabling data scientists to effectively manipulate, analyze, and visualize data to extract valuable insights and drive informed decision-making. Python, R, SQL, Java, and Julia are among the most commonly used programming languages in data science, each offering unique features and capabilities for different aspects of the data science workflow. By understanding the strengths and use cases of these languages and following best practices for mastering programming skills, aspiring data scientists can enhance their proficiency and excel in the field of data science.

[datascienceunicorn]

The Data Scientist Handbook 2024

HT @dataelixir

[procurement-insights]

Does Procurement Really Need Data Scientists?

Data Scientists + Procurement =?

Answer today’s question, then read these two Procurement Insights posts: Dangerous Supply Chain Myths (Part 7) | Procurement Insights (July 4th, 2007) Are you chasing solutions or solving problems? (Part 1 of 3) (October 23rd, 2023) Now, answer the question again? Did your answer change? Mine didn’t. 30 Weird Science?

[impact-newswire]

Zindi and GovTech Barbados Announce Landmark Partnership to Build National AI Ecosystem

Press Release – Bridgetown, Barbados – 31 March – Zindi, a global AI community platform, is proud to announce a year-long partnership with GovTech Barbados, part of the Ministry of Industry, Innovation, Science and Technology. This groundbreaking collaboration aims to establish and grow a vibrant AI ecosystem in Barbados, focused on nurturing talent, fostering innovation, and unlocking new job…

[clarkalston-blog]

Today, the world cannot deny the pivotal role programming languages play in leveraging high-end growth in diversified sectors of the global economy. From building games, embedded software, and medical technologies to storing information, retrieving and updating data, coding web applications, and whatnot! Programming prowess can be felt just everywhere.

This strengthens the base for learning the best and most in-demand programming languages for data scientists. Especially when discussing the most esteemed programming languages, Python unquestionably stands at the pinnacle of recognition. The US Bureau of Labor Statistics expects a 25% growth rate for software developers, quality assurance analysts, testers, and many other meaty industry roles over the next decade.

This is a clear sign of earning the best programming languages to garner high-intent career growth with organizational success as well. Making the right move now could level you up to a higher pedestal of strategic career milestones.

From being the most popular data science programming language to the growing-in-demand programming languages; the list goes endless with the ever-new addition of the languages. Whether you are aiming at query essentials, powerful data processing; versatility; or blazing-fast speed of efficient numerical computing; a plethora of programming languages are at your disposal today. Explore the best skills and top programming languages of all time with the most nuanced data science certification providers around the world.

The time is right to take the plunge and give it your all and earn a staggering annual package with the best programming languages for data scientists today. Make the move now!

[mitsde123]

Data Science Job Market : Current Trends and Future Opportunities

The data science job market is thriving, driven by the explosive growth of data and the increasing reliance on data-driven decision-making across industries. As organizations continue to recognize the value of data, the demand for data scientists has surged, creating a wealth of opportunities for professionals in this field.

[onlinetrainingcourse]

7 Mistakes to Avoid in Your Next Data Science Project

Data has become an indispensable asset in our increasingly interconnected and digital world. Data science is a dynamic field that harnesses the immense potential of data to extract knowledge, make predictions, and drive innovations. On these days, pursuing a data science degree online is an excellent choice for those looking to enter the field of data analysis or advance their existing careers in this domain. However, even experienced data scientists can make mistakes that hinder the success of their projects. This article will explore seven common mistakes to avoid in your next data science project, ensuring that you achieve the best possible results.

One of the most critical phases of any data science project is defining the problem you aim to solve. Failing to define the problem clearly can lead to wasted time and resources. Before collecting python and data science, ensure you have a well-defined problem statement. Understanding the problem's scope, objectives, and desired outcomes is essential for a successful project. 

2. Not Choosing the Right Tools Considering the Problem at Hand

It is crucial to choose the right algorithm for a certain task. Before deciding whether an algorithm or method is best, a data scientist should carefully consider the intricacies and requirements of the problem, much like an expert craftsman selecting the perfect tool for a certain task. This procedure thoroughly evaluates the problem's nature, goals, and particular difficulties. Data scientists must take a diverse approach to problem understanding to ensure a solid and efficient solution. 

3. Neglecting Model Optimization in the Face of Changing Data Dynamics

Every machine learning model is built around the crucial idea of optimization. The model's hyperparameters, which are effectively the levers and gears that control its performance, must be adjusted during this process. To succeed in data science, one must accept and welcome change as inevitable. Successful data scientists are able to adapt, improve, and retrain the model in response to new data, as opposed to unsuccessful ones. 

4. Applying Uniform Functions to a Range of Problems

Whether it be textual data, audio files, pictures, numbers, or complex time-series data, every sort of data has its own distinct qualities and complexities. For data scientist beginners, applying the same functions, tools, and approaches to various data-related problems is a common error. This tendency results from theoretical knowledge but frequently fails when it comes to practical, real-world applications. 

One of the most critical phases of any data science project is defining the problem you aim to solve. Failing to define the problem clearly can lead to wasted time and resources. Before collecting python and data science, ensure you have a well-defined problem statement.

5. Not Considering All Necessary Datasets in Model Development

A key mistake when starting a data science project is failing to include all possibly relevant datasets. A skilled data scientist should be aware of several datasets connected to a problem and diligently try to draw connections between them. Sometimes, data is purposefully spread over multiple databases to improve data structure and accessibility. The data scientist is responsible for making connections and building an accurate model while doing so.

6. Overlooking Exploratory Data Analysis (EDA)  While visualizing and summarizing, separating valuable information from chaotic and overwhelming data might be difficult when it is raw. In data analysis, skipping the EDA process might have significant repercussions. Without a thorough understanding of the data, jumping right into model might lead to less reliable, accurate, and insightful models. 

7. Focusing Solely on Model Complexity

For particular sorts of issues, simpler models, such as linear regression or decision trees, are frequently easier to understand and more accurate. Data scientists can make a serious error by diving headfirst into model complexity without understanding the problem's fundamental requirements. The attraction of complexity can result in the construction of time- and resource-consuming models that may need to meet the goals of the issue.

Summing up

Data science initiatives have a huge potential to drive organizational innovation and growth, but success depends on avoiding typical pitfalls. Learning data analysis through an online training program equips you with the knowledge and skills to extract meaningful insights from data. Avoiding these seven frequent errors can considerably boost the possibility that your next data science project will be successful.