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Data-driven insights: Improving remote team performance with time … – Data Science Central

The way we work has changed, with remote teams now a common part of the landscape. While remote work offers flexibility, it also brings challenges. Managing remote teams effectively is crucial to ensure productivity and collaboration.

In this article, well explore how using time tracking for remote teams can help manage employees performance better. Time-tracking tools provide insights into how work is done, helping organizations make informed decisions. Well see how analyzing time-tracking data reveals when teams are most productive and how tasks are managed. By understanding these patterns, organizations can enhance remote team performance and achieve better outcomes.

Time-tracking apps usually capture detailed information about tasks, projects, and activities, including start and end times, task descriptions, and breaks taken. They generate reports that display time allocation across different projects, clients, or categories, shedding light on where your efforts are concentrated. Furthermore, these apps often provide visual representations like charts and graphs, illustrating productivity trends, peak hours, and patterns of time distribution.

By analyzing this data, individuals and teams can gain valuable insights into how time is being allocated, identify bottlenecks, and streamline processes. This data-driven approach enables better time management and helps prioritize tasks effectively.

At the heart of effective time tracking for remote teams lies the practice of meticulously recording daily activities. From the moment a remote worker starts their day to when they sign off, every task, break, and project engagement is captured. This detailed chronicle not only offers a panoramic view of how time is spent but also highlights potential areas for optimization.

This approach offers transparency into each team members workflow. Managers gain insights into the types of tasks being executed, the time dedicated to each task, and potential areas where efforts might be misplaced.

Furthermore, tracking daily activities brings to light the ebbs and flows of each team members work patterns. This knowledge empowers remote teams to identify productivity trends, such as the times when individuals are most focused and effective.

Additionally, some time tracking tools offer customizable tagging systems, allowing you to categorize tasks based on their nature or complexity. For instance, users can label tasks as high priority, creative, or routine and later review their tracked time and note when they tackled specific types of tasks with the highest level of energy. This categorization helps you to identify peak productivity hours and the kinds of tasks that thrive during these periods.

Through time tracking, remote teams can pinpoint bottlenecks that hinder productivity. Whether its a recurring task that consumes excessive time or a specific step in a project workflow causing delays, these pain points become apparent. Armed with these insights, individuals and teams can pinpoint these time drains and take targeted actions to minimize them.

Moreover, time tracking data doesnt just show where time is being lost; it offers a deeper understanding of why its happening. Are there particular tasks that consistently take longer than expected? Are there patterns of multitasking that fragment concentration and efficiency? These insights allow for a more holistic analysis of work habits and the identification of underlying causes of time wastage. As a result, teams can implement strategies to address these specific issues.

In addition, many time-tracking tools for remote teams offer reports that show how time is allocated through different websites and apps. It offers a valuable window into your digital behavior, helping you gauge if you are spending excessive time on non-work-related websites. By analyzing these reports, team members can gather insights into whether their online activities align with their intended work goals. For example, if the reports show that you often spend a lot of time on social media or entertainment websites during work hours, its clear that you need to make changes to stay more focused.

By analyzing historical time data across various tasks and projects, teams can gain a clearer understanding of how long certain activities actually take to complete. This insight replaces guesswork with empirical evidence, enabling more accurate and realistic project timelines. As teams delve into the accumulated data, they can identify patterns in task durations, uncover potential bottlenecks, and factor in unforeseen variables that might affect future projects.

Furthermore, time-tracking data facilitates a proactive approach to managing project scope and client expectations. Armed with a comprehensive record of task durations and progress, project managers can provide clients with more transparent updates and realistic forecasts. Should any deviations from the initial project plan arise, the data serves as a valuable reference point to communicate adjustments and potential impacts. This not only fosters stronger client relationships built on trust but also enables teams to adapt swiftly, ensuring project goals remain achievable within the defined timeframe.

Time tracking data plays a great role in fostering a healthier work-life balance, especially in the context of remote work where boundaries between professional and personal life can blur. By providing a clear picture of how time is allocated throughout the day, you can identify when work goes into personal time or vice versa. For instance, if time tracking data reveals that work-related tasks often extend into evenings, you can adjust your work pattern to finish work a bit earlier.

Time tracking for remote teams also helps to reveal whether there are adequate breaks to rest and recharge, or if theres a tendency to overindulge in extended pauses. This information is crucial for sustaining a balanced work routine. If time tracking data shows prolonged periods without breaks, it may suggest incorporating short, regular breaks to prevent burnout and maintain focus. Conversely, excessive and frequent breaks might signal an opportunity to structure work periods more effectively. By analyzing the intervals between productive work sessions and short respites, individuals can fine-tune their approach to breaks, optimizing their productivity and well-being in the process.

By harnessing the power of data-driven insights, remote teams can unlock their true potential. From identifying peak productivity hours to enhancing work-life balance, time-tracking analytics pave the way for informed decisions, personalized strategies, and a more harmonious work environment.

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Joe Depa named inaugural chief data and analytics officer at Emory – Emory News Center

ATLANTA Joe Depa, a global leader in data operations, analytics and artificial intelligence (AI), has been named Emory Universitys inaugural chief data and analytics officer. He began his new position on Sept. 11.

In this inaugural role, Depa will use the power of data to enhance health outcomes by ensuring better patient care and reducing clinician burnout, expand Emorys academic impact through groundbreaking research and education, and create an environment where the Emory community can thrive by focusing on efficiency and culture. Depas new position will support both the Emory University and Emory Healthcare data infrastructure.

Joes expertise and experience is a perfect fit for Emory at this time, as we seek to leverage the power of data and AI to enhance our capabilities in academic, administrative and research areas and improve patient outcomes, says John Ellis, PhD, interim chief information officer and senior vice provost for Emory University.Joe is also passionate about using data for good and is committed to our mission of improving the health of individuals and communities at home and throughout the world. We welcome Joe warmly as he beginsthis pivotal work.

Depa comes to Emory from Accenture, a Fortune 50 technology provider, where he served as the senior managing director and global lead for data and AI for the companys strategy and consulting business. There he managed their award-winning team of global professionals specializing in data science and AI strategy, and served on the global leadership committee. He focused on helping clients in health, life sciences and across industries to leverage data to develop new clinical data products, improve the patient and employee experience and reduce operating expenses.

As health care pivots to address patient access, workforce shortages and ballooning expenses, AI, machine learning and large language models have the potential to help, but only if guided by the right expertise, says Alistair Erskine, MD, chief information and digital officer for Emory Healthcare and vice president of digital health for Emory University.Joes experience in and out of health care, combined with his purpose-driven mission to alleviate human suffering, makes him the ideal inaugural leader for this critical role.

I am excited to join Emory in this new role to help enrich the patient, clinician and researcher experience through AI and data science, says Depa. This position supports a purpose-driven mission, using the power of data, to help advance positive changes in the lives of patients being cared for at Emory, in our daily work on our campuses and in our society.

Depa received a bachelors degree in industrial and systems engineering and a masters degree in analytics from Georgia Institute of Technology (Georgia Tech). Outside of work, he is a board member for Cure Childhood Cancer and founder and supporter of other organizations focused on research and advancing precision medicine for childhood cancer.

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IST researcher among recipients of $29 million fusion energy … – Pennsylvania State University

UNIVERSITY PARK, Pa. The U.S. Department of Energy awarded a $29 million grant to seven multi-institution teams across the country to explore applications of machine learning, artificial intelligence and data resources in fusion and plasma sciences. A Penn State faculty member is one of the 19 individual recipients recognized, with a share close to $400,000 to focus on the use of machine learning to help mitigate nuclear reactor disruptions.

Romit Maulik, assistant professor in the Penn State College of Information Sciences and Technology (IST) will collaborate with researchers from Los Alamos National Laboratory, the University of Florida and The University of Texas at Austin (UT Austin) over the next three years with this funding. The teams project is titled, DeepFusion Accelerator for Fusion Energy Sciences in Disruption Mitigation. The researchers will focus on using machine learning to better predict and prevent imminent failures in nuclear fusion reactors, which generate energy through the same process that powers the sun.

Artificial intelligence and scientific machine learning are transforming the way fusion and plasma research is conducted, said Jean Paul Allain, associate director for fusion energy sciences within the DOEs Office of Science, in a DOE press release. Allain is currently on leave from his role as the head of the Ken and Mary Alice Lindquist Department of Nuclear Engineering at Penn State. The U.S. is leveraging every tool in its pursuit of an aggressive program that will bring fusion energy to the grid on the most rapid timescale.

Before joining IST this year, Maulik who is also a Penn State Institute for Computational and Data Sciences co-hire had been collaborating on the Tokamak Disruption Mitigation project with Los Alamos National Laboratory to build machine learning algorithms to aid scientific discovery in nuclear fusion. He said this grant will support him as he takes a deeper dive into the machine learning side of things.

Nuclear fusion reactors are prone to catastrophic performance failures, Maulik said. This creates a safety hazard that prevents nuclear fusion from being commercialized or becoming a power source for the grid.

Maulik said one grand challenge is the inability to predict when a reactor will fail. Simulations provide insight, but they may be too slow and expensive to be used in real time to detect what might happen.

We want to use data science to accelerate these simulations dramatically, Maulik said. If we can rapidly predict an imminent failure, we can control the factors that affect our experiment so that this failure may be avoided.

Maulik said the project will develop machine learning models using previously run simulations as well as experimental data that is coming from actual reactor facilities.

Once were able to detect failures ahead of time, well be able to begin proposing mitigation strategies, he said.

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TUM Launches Munich Data Science Institute to Drive Collaboration … – The Munich Eye

The Technical University of Munich (TUM) is proud to announce the official inauguration of the Munich Data Science Institute (MDSI), a pivotal initiative within the framework of TUM AGENDA 2030. Supported by funding from the Excellence Initiative of the German government and federal states, the MDSI serves as a central hub for advancing the realms of data science, machine learning, and artificial intelligence (AI) at TUM, from foundational research to practical interdisciplinary applications. It also aims to provide training and education opportunities for master's students, researchers, and professionals in the field of data science.

In recent years, groundbreaking developments in machine learning, AI, natural language processing, and computer-based imaging have fundamentally reshaped society, the economy, and the landscape of scientific knowledge. With the aim of bolstering the foundational principles of modern data sciences, machine learning, and AI, and applying these insights to high-potential applications, TUM has established the Munich Data Science Institute (MDSI). As an integrative research institute, MDSI will harness the expertise of over 60 professors across various interdisciplinary domains.

The official launch event for the institute was held at the Galileo Building on the TUM Research Campus in Garching.

Bavaria's Minister of Science, Markus Blume, underscored the significance of data in his keynote address at the MDSI's opening. He stated, "Data is the treasure of our time. The Munich Data Science Institute is our key to the treasure chest and will open the door to innovation. In the MDSI, TUM is bringing together what must come together in the world of data science: business and science, fundamental research and applications. Because only through collaboration and a strong interdisciplinary network can we play a role in shaping the significant transformation of the digital age."

President Prof. Thomas F. Hofmann emphasized the importance of teamwork in the era of machine learning and AI, stating, "To effectively leverage the potential of the age of machine learning and AI, we need to see modern data science as a team sport. With the MDSI, we are delivering fresh impetus to data-based technology developments and integrating them into real-world applications. Machine learning and AI harbor enormous potential. From life sciences and medicine, material and design sciences to quantum science, astrophysics, and climate science - as well as the dynamics of societal, political, and economic systems - the MDSI will support pioneering data science experts in reshaping the boundaries of what is now feasible."

Stephan Gnnemann, Executive Director of the MDSI and Professor of Data Analytics and Machine Learning, outlined the institute's goals, saying, "At the MDSI, we want to study the foundations of modern data science. This relates to the areas of mathematics and informatics that deal with machine learning. But we also want to apply what we learn in specialized areas such as the development of new materials or in personalized medicine."

The MDSI also aims to disseminate research findings to the business world and society at large, facilitating the transfer of AI-based solutions to industry partners and startups in the data-related domain. Additionally, the MDSI will offer support to researchers grappling with the increasing demand for data-related tasks in their work and will serve as a network for interdisciplinary connections among AI experts.

The MDSI is a convergence point for TUM's strategic data-supported activities, ensuring synergy and reducing redundancies between different disciplines. As President Prof. Thomas F. Hofmann emphasized, "Purely quantitative growth by adding new disconnected activities, one after another, in the fields of data science and programs will not have the necessary impact to reach global player status."

Incorporating Various Initiatives and Facilities

The Munich Data Science Institute incorporates a range of initiatives and facilities under its umbrella:

The TUM Georg Nemetschek Institute - Artificial Intelligence for the Built World, an initiative supported by a generous 50 million euro donation from the Nemetschek Innovation Foundation in 2020. This initiative focuses on AI and machine learning applications throughout the entire life cycle of buildings, from planning and construction to sustainable management.

The AI Future Lab AI for Earth Observation (AI4EO), funded by the Federal Ministry of Research and led by Xiaoxiang Zhu, one of the five MDSI directors. AI4EO combines TUM's strengths in geodesy, earth observation, satellite technology, mathematics, AI, and ethics to develop reliable models related to global urbanization, food supply, and natural disaster management.

The establishment of the Center for Digital Medicine and Health, a new research building with federal and state funding, will be positioned within the medical campus of Klinikum rechts der Isar. Under the leadership of MDSI director Daniel Rckert, it will focus on the development of data-driven approaches and AI methods in medicine.

The Munich Center for Machine Learning (MCML), a collaboration between TUM and LMU, funded by the Federal Ministry of Education and Research and the HighTech Agenda Bayern as one of the National Centers of Excellence for AI Research. The TUM branch of the MCML is integrated into the MDSI infrastructure.

The Konrad Zuse School of Excellence in Reliable AI, coordinated by TUM and LMU, has received funding from the German Academic Exchange Service (DAAD) since 2022. The MDSI contains the business office of the Konrad Zuse School and is led by MDSI Executive Director Stephan Gnnemann.

These initiatives underscore TUM's commitment to advancing data science, machine learning, and AI, while fostering interdisciplinary collaboration to drive innovation and tackle complex challenges in the digital age.

Opening ceremony of the Munich Data Science Institute (MDSI) of the Technical University of Munich (TUM, on September 14th, 2023 in the Galileo Congress Center Garching; in the picture: TUM President

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NIH awards $50.3 million for multi-omics research on human … – National Institutes of Health (.gov)

News Release

Tuesday, September 12, 2023

New research consortium will develop innovative strategies for clinical studies involving ancestrally diverse populations.

The National Institutes of Health is establishing the Multi-Omics for Health and Disease Consortium, with approximately $11 million awarded in the consortiums first year of funding. The new consortium aims to advance the generation and analysis of multi-omic data for human health research.

Multi-omics refers to a research approach that incorporates several omics data types derived from different research areas such as genomics, epigenomics, transcriptomics, proteomics and metabolomics. Each of these data types reveals distinct information about different aspects of a biological system, and leveraging all these data types at once is becoming increasingly possible with advances in high-throughput technologies and data science.

The integration of multiple types of data from an individual participants biological sample can provide a more holistic view of the molecular factors and cellular processes involved in human health and disease, including untangling genetic and non-genetic factors in health and disease. Such an approach offers great promise in areas such as defining disease subtypes, identifying biomarkers and discovering drug targets.

Beyond gaining insights into individual diseases, the primary goal of this consortium is to develop scalable and generalizable multi-omics research strategies as well as methods to analyze these large and complex datasets, said Joannella Morales, Ph.D., a National Human Genome Research Institute (NHGRI) program director involved in leading the consortium. We expect these strategies will ultimately be adopted by other research groups, ensuring the consortiums work will have broad and long-lasting impacts for clinical research.

Approximately half of the awarded funds will support the work of six disease study sites, which will examine conditions such as fatty liver diseases, hepatocellular carcinoma, asthma, chronic kidney disease and preeclampsia, among others. The sites will enroll research participants, at least 75% of whom will be from ancestral backgrounds underrepresented in genomics research. The sites will also collect data on participants environments and social determinants of health to be used in conjunction with the multi-omics data. Combining the multi-omic and environmental data can offer an even more comprehensive view of the factors that contribute to disease risk and outcomes.

Specimens provided by participants will be processed at the omics production center, which will use high-throughput molecular assays to generate genomic, epigenomic, transcriptomic, proteomic and metabolomic data that will be analyzed to generate molecular profiles of disease and non-disease states. The data analysis and coordination center will then incorporate all of these data into large, organized datasets that will be made available to the scientific community for further studies.

Multi-omics studies are at the forefront of biomedical research and promise to advance our understanding of disease onset and progression, said Erin Ramos, Ph.D., M.P.H., deputy director of NHGRIs Division of Genomic Medicine. All while potentially providing important clues for treatment design and drug-discovery efforts. This new consortium is an important step in making those advances a reality.

Approximately $50.3 million will be awarded to the consortium over five years, pending the availability of funds. The award is funded jointly by NHGRI, the National Cancer Institute (NCI) and the National Institute of Environmental Health Sciences (NIEHS).

Multi-Omics for Health and Disease Consortium

Disease study sites and principal investigators

Omics production center and principal investigator

Data analysis and coordinating center and principal investigator

The National Human Genome Research Institute (NHGRI) is one of the 27 institutes and centers at the NIH, an agency of the Department of Health and Human Services. The NHGRI Division of Intramural Research develops and implements technology to understand, diagnose and treat genomic and genetic diseases. Additional information about NHGRI can be found at: https://www.genome.gov/.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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Podcast: Vanguard’s Ryan Swann on Big Data Strategies for Big … – InformationWeek

Investment management company Vanguard operates with some 20,000 employees, has more than 50 million investors, with more than $7 trillion dollars in assets under management.

Big money, big data, and a big responsibility to say the least. Financial institutions lean increasingly on data and technology to better navigate fluctuations of the market, which can see dramatic shifts as well as slow-burn trends. For instance, a few weeks ago LG AI Research and Qraft Technologies signed an agreement at the New York Stock Exchange to support efforts in AI applications and the creation of financial instruments. They are clearly not alone in the race to leverage AI and machine learning in conjunction with financial data.

With an operation of Vanguards size and scope, using data and analytics becomes a priority to help maximize investments. A combination of co-locating data and analytics teams to work with leaders while centralizing data is part of Vanguards approach to advising its clients on their investments and reducing risk.

Ryan Swann, Vanguards chief data analytics officer, shares some of the data strategies and structure employed by his team to identify ways to further assist customers who invest through Vanguard by identifying behaviors that might leave money on the table.

Listen to the full podcast here

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Modeling social media behaviors to combat misinformation – William & Mary

Not everyone you disagree with on social media is a bot, but various forms of social media manipulation are indeed used to spread false narratives, influence democratic processes and affect stock prices.

In 2019, theglobal costof bad actors on the internet was conservatively estimated at $78 billion. In the meantime, misinformation strategies have kept evolving: Detecting them has been so far a reactive affair, with malicious actors always being one step ahead.

AlexanderNwala, a William & Mary assistant professor of data science, aims to address these forms of abuse proactively. With colleagues at theIndiana University Observatory on Social Media, he has recently publishedan Open Access paperinEPJ Data Scienceto introduce BLOC, a universal language framework for modeling social media behaviors.

The main idea behind this framework is not to target a specific behavior, but instead provide a language that can describe behaviors, said Nwala.

Automated bots mimicking human actions have become more sophisticated over time. Inauthentic coordinated behavior represents another common deception, manifested through actions that may not look suspicious at the individual account level, but are actually part of a strategy involving multiple accounts.

However, not all automated or coordinated behavior is necessarily malicious. BLOC does not classify good or bad activities but gives researchers a language to describe social media behaviorsbased on which potentially malicious actions can be more easily identified.

A user-friendly tool to investigate suspicious account behavior is in the works at William & Mary. Ian MacDonald 25, technical director of the W&M undergraduate-ledDisinfoLab, is building a BLOC-based website that would be accessed by researchers, journalists and the general public.

The process, Nwala explained, starts with sampling posts from a given social media account within a specific timeframe and encoding information using specific alphabets.

BLOC, which stands for Behavioral Languages for Online Characterization, relies on action and content alphabets to represent user behavior in a way that can be easily adapted to different social media platforms.

For instance, a string like TpR indicates a sequence of four user actions: specifically, a published post, a reply to a non-friend and then to themselves and a repost of a friends message.

Using the content alphabet, the same set of actions can be characterized as (t)(EEH)(UM)(m) if the users posts respectively contain text, two images and a hashtag, a link and a mention to a friend and a mention of a non-friend.

The BLOC strings obtained are then tokenized into words which could represent different behaviors. Once we have these words, we build what we call vectors, mathematical representations of these words, said Nwala. So well have various BLOC words and then the number of times a user expressed the word or behavior.

Once vectors are obtained, data is run through a machine learning algorithm trained to identify patterns distinguishing between different classes of users (e.g., machines and humans).

Human and bot-like behaviors are at the opposite ends of a spectrum: In between, there are cyborg-like accounts oscillating between these two.

We create models which capture machine and human behavior, and then we find out whether unknown accounts are closer to humans, or to machines, said Nwala.

Using the BLOC framework does not merely facilitate bot detection, equaling or outperforming current detection methods; it also allows the identification of similarities between human-led accounts. Nwala pointed out that BLOC had also been applied to detect coordinated inauthentic accounts engaging in information operations from countries that attempted to influence elections in the U.S. and the West.

Similarity is a very useful metric, he said. If two accounts are doing almost the same thing, you can investigate their behaviors using BLOC to see if perhaps theyre controlled by the same person and then investigate their behavior further.

BLOC is so far unique in addressing different forms of manipulation and is well-poised to outlive platform changes that can make popular detection tools obsolete.

Also, if a new form of behavior arises that we want to study, we dont need to start from scratch, said Nwala. We can just use BLOC to study that behavior and possibly detect it.

As Nwala points out to students in his class onWeb Science the science of decentralized information structures studying web tools and technologies needs to take into account social, cultural and psychological dimensions.

As we interact with technologies, all of these forces come together, he said.

Nwala suggested potential future applications of BLOC in areas such as mental health, as the framework supports the study of behavioral shifts in social media actions.

Research work on social media, however, has been recentlylimitedby therestrictionsimposed by social media platforms on application programming interfaces.

Research like this was only possible because of the availability of APIs to collect large amounts of data, said Nwala. Manipulators will be able to afford whatever it takes to continue their behaviors, but researchers on the other side wont.

According to Nwala, such limitations do not only affect researchers, but also the society at large as these studies help raise awareness of social media manipulation and contribute to effective policymaking.

Just as theres been this steady shout about how the slow decline of local news media affects the democratic process, I think this rises up to that level, he said. The ability of good faith researchers to collect and analyze social media data at a large scale is a public good that needs not to be restricted.

Editors note:Dataanddemocracyare two of four cornerstone initiatives in W&Ms Vision 2026 strategic plan. Visit theVision 2026 websiteto learn more.

Antonella Di Marzio, Senior Research Writer

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PyCharm vs. Spyder: Choosing the Right Python IDE – Unite.AI

Python is immensely popular among developers and data scientists due to its simplicity, versatility, and robustness, making it one of the most used programming languages in 2023. With around 147,000 packages, the Python ecosystem continues to evolve with better tools, plugins, and community support.

When we talk about Python development, Integrated Development Environments (IDEs) take center stage, allowing developers to enhance their coding experience. Two popular IDEs for Python development are PyCharm and Spyder. This article briefly compares Python vs. Spyder to help developers make an informed choice.

Before comparing PyCharm vs. Spyder to determine the best IDE for Python development, its essential to understand what these tools entail.

PyCharm is a product by JetBrains that offers a feature-rich integrated development environment for Python. The IDE has two editions PyCharm Community and PyCharm Professional. The former is a free, open-source version, while the latter is a paid version for full-stack development. Both versions support several features, including code completion, code analysis, debugging tools, and integration with various version control systems. The professional edition further includes frameworks for web development and data science.

Spyder, or Scientific Python Development Environment, is an open-source IDE primarily focusing on data science and scientific computing in Python. Its part of the Anaconda distribution, a popular package manager and distribution platform for Python. Spyder provides comprehensive tools for advanced data analysis, visualization, and scientific development. It features automatic code completion, code analysis, and vertical/horizontal screen splits with a multi-language editor pane that developers can use for creating and modifying source files. Moreover, developers can extend Spyders functionality with powerful plugins.

Several similarities and differences exist between these two IDEs. Below, we compare them against various dimensions, including code editing and navigation features, debugging capability, support for integrated tools, customizability, performance, usability, community support, and pricing.

Both PyCharm and Spyder offer powerful code editing and navigation features, making it easy for developers to write and understand code across files. While Spyder provides similar code completion and navigation ability, it is less robust than PyCharm's code editing features, which offer context-based recommendations for faster development. For instance, developers get code completion suggestions (sorted by priority) based on other developers' work in a similar scenario.

PyCharm leads this category with its advanced code analysis and completion capabilities.

PyCharms professional version has a Javascript-based debugger that supports various debugging modes, including remote debugging. It also provides a visual debugger with breakpoints, variable inspection, and step-by-step execution.

Spyder includes a PDB debugger. PDB is a source debugging library for Python that lets developers set conditional breakpoints and inspect stack frames. Its variable explorer is particularly helpful for checking variable states at several breakpoints.

While Spyders debugging capabilities are robust, PyCharms visual debugger is better as it helps in more complex debugging scenarios.

PyCharm has extensive integration with third-party tools and services. For instance, it has built-in support for version control systems like Git, SVN, Perforce, etc. The professional edition supports web development frameworks, such as Django, Flask, Angular, etc., making it an excellent choice for full-stack development.

Spyder, primarily a data science and scientific computing utility, comes with numerous libraries and tools, such as NumPy, SciPy, Matplotlib, and Jupyter Notebooks. Also, it shares all libraries that come with the Anaconda distribution. However, Spyder only supports Git for version control.

Overall, PyCharm overtakes Spyder in this category since the former offers integration with diverse tools through plugins.

PyCharm offers a high level of visual customization, allowing developers to tailor the IDE according to their workflow and preferences. They can change font type and color, code style, configure keyboard shortcuts, etc.

Spyder is relatively less customizable compared to PyCharm. The most a user can do is change the user interfaces (UIs) theme using a few options among light and dark styles.

Again, PyCharm takes the win in the customization category.

While performance can vary depending on the size and complexity of the projects, Spyder is relatively faster than PyCharm. Since PyCharm has many plugins installed by default, it consumes more system resources than Spyder.

As such, Spyder's lightweight architecture can make it a better choice for data scientists who work on large datasets and complex data analysis.

Spyder is the clear winner in the performance category.

PyCharm has many customization options for its user interface (UI). Developers benefit from an intuitive navigation system with a clean layout. However, its extensive feature set means it has a steep learning curve, especially for beginners.

In contrast, Spyder's interface is much more straightforward. Like R, it has a variable navigation pane, a console, a plot visualization section, and a code editor, all on a single screen. The simplified view is best for data scientists who want a holistic view of model results with diagnostic charts and data frames. Also, Spyder's integration with Jupyter Notebooks makes data exploration and visualization easier for those new to data science.

Overall, Spyder is ideal for beginners, while PyCharm is more suited to experienced Python developers.

PyCharm has a free and paid version. The free community version is suitable for individual developers and teams working on a small scale. The paid version, the Professional Edition, comes in two variants for organizations and individuals. The organization version costs US 24.90 monthly, while the individual one costs USD 9.90 monthly.

In contrast, Spyder is open-source and entirely free to use. It comes as part of the Anaconda distribution, which is also open-source and free.

In terms of cost, Sypder is a clear winner. However, in Python development, it is up to the practitioners and organizations to choose based on their business requirements.

Both PyCharm and Spyder have active communities that provide extensive support to users. PyCharm benefits from JetBrains' strong reputation and rich experience in building Python development tools. As such, developers can utilize its large user community and get help from a dedicated support team. They also have access to many tutorials, help guides, and plugins.

Spyder leverages the Anaconda community for user support. With an active data science community, Spyder benefits from the frequent contributions of data scientists who provide help through forums and online resources, data science tutorials, frameworks, and computation libraries.

Again, it is up to the practitioners and organizations to choose a community that aligns with their task or business requirements.

Choosing between PyCharm and Spyder can be challenging. Its helpful to consider some of their use cases so practitioners can decide which IDE is better for their task.

PyCharm is ideal for full-stack developers as the IDE features several web and mobile app development tools and supports end-to-end testing. Its best for working on large-scale projects requiring extensive collaboration across several domains.

Spyder, in contrast, is suitable for data scientists, researchers, and statisticians. Its lightweight architecture allows users to perform exploratory data analysis and run simple ML models for experimentation. Instructors can use this IDE to teach students the art of data storytelling and empower them to train machine learning models efficiently.

The choice between PyCharm and Spyder ultimately depends on user needs, as both IDEs offer robust features for specific use cases.

PyCharm is best for experienced professionals who can benefit from its advanced web development tools, making it an excellent choice for building web and mobile apps. Users wishing to learn data science or work on related projects should go for Spyder.

To read more interesting technology-related content, navigate through Unite.ais extensive catalog of insightful resources to amplify your knowledge.

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360DigiTMG: Your Path to Success in Data Science and AI Careers – mid-day.com

360DigiTMG

360DigiTMG, a globally recognized leader in IT training, has introduced a groundbreaking Professional Data Science and AI with Placement Guarantee Program. The goal of this extensive program is to provide students with the knowledge and abilities they need to succeed in the fast-paced fields of data science and artificial intelligence, opening the door to lucrative careers in these emerging fields.

360DigiTMG is leading the way globally in offering expert training in many different fields. The institute, which was founded in 2013, is well-known in India and other countries. 360DigiTMG significantly transforms the careers of its students by continuously offering them an exceptional learning experience. It has branches in Malaysia, the United States, East Asia, Australia, the United Kingdom, the Netherlands, and the Middle East in addition to its main office in India.

Data Science and AI: A Trending Career

The combination of data science and AI has transformed businesses. Automation, output, cost reduction, and creativity are now common uses of AI. PWC predicts that AI will affect 50% of human jobs in five years, increasing demand for AI experts. AI job openings have increased by 119% in the past three years, doubling the demand for AI skills.

A growing emphasis is being placed on creating ethical and impartial AI technologies amid discussions about how AI will affect employment. Leading tech firms have created ethical committees to monitor AI's effects on society, including Microsoft and Google. Data silos have been dismantled by data management platforms, allowing businesses to gain insightful data. AI is also making it possible to customize services, especially in the finance industry, where analytics are essential for retaining customers.

The public cloud market is predicted to be dominated by AI platforms in the upcoming years as Google, AWS, and Microsoft expand their AI cloud offerings. More businesses will use real-time analytics to make data-driven decisions by spotting hidden patterns. Other fields experiencing rapid development include IoT applications, Patent Analytics, market sizing tools, and Earning Transcripts analysis.

360DigiTMG's Data Science & AI Professional Course with Placement Guarantee

Leading professionals in India have received training from 360DigiTMG. Through its outstanding training programs created to meet industry needs, the institute is dedicated to transforming careers. The curriculum of each certification program at 360DigiTMG is carefully crafted to reflect the most recent market trends.

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Each certification program's curriculum has been painstakingly created to be in line with current business trends. To provide students with practical experience, 360DigiTMG collaborates with renowned businesses like Panasonic India Innovation Center and Innodatatics, which is accredited by UTM, Malaysia. For concept review, students have access to a Learning Management System (LMS), and the institute provides 100% job assistance to help students land jobs at prestigious companies.

Key Features of the Professional Course in Data Science & AI with Placement Guarantee

Who Should Sign Up For The Program

Since its establishment, 360DigiTMG has helped countless people transform their careers by providing top-notch training opportunities.

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For additional information about 360DigiTMG and its Professional Data Science and AI with Placement Guarantee Program, please visit https://360digitmg.com/.

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Why Some Top Runners Prefer to Train Without a GPS Watch – The New York Times

As a decorated college runner at Notre Dame and then at the University of Tennessee, Dylan Jacobs dabbled with a device that many of his teammates considered indispensable.

But on those rare occasions when Jacobs succumbed to peer pressure and slapped a GPS watch around his wrist, he almost immediately remembered why he had resisted the temptation in the first place.

The runs just felt so much longer, said Jacobs, 23, a three-time N.C.A.A. champion who recently turned pro. That was one of my main problems with it. I wasnt enjoying myself or looking around. Instead, I was kind of looking at the watch every quarter-mile to see how much longer I had left.

GPS watches popular brands are Garmin, Suunto and Coros come equipped with satellite technology and heart rate monitors to produce a buffet of functions. Want to know how far and how fast youve run, or how many milliliters of sweat you dumped in Central Park last weekend? How about your average stride length? Your cadence? The list goes on.

For many, GPS watches are a remarkably useful training tool. But there are other runners, including world-class runners like Jacobs, who have a hard time understanding the fuss. To them, a smorgasbord of data is more hindrance than help. And get this: Some runners dont wear watches at all.

I like to focus more on the feel of everything and not worry too much about the time, Jacobs said.

Heather MacLean, an Olympic 1,500-meter runner, recalled a period of her life when she enjoyed the utility of a GPS watch. As a student at the University of Massachusetts, she grew to understand the value of sleep and more important, that she was not getting enough of it while working in a neuroscience laboratory. So she began using a Garmin Forerunner to monitor her rest and adjust her schedule.

Later, as a first-year pro with Team New Balance Boston, MacLean tried to be consistent about wearing a GPS watch but was hampered by a couple of issues. First, she was always forgetting to charge it.

I would just let it die all the time, and Im super lazy with that kind of stuff, she said.

Second, MacLean realized her watch was draining the fun from her runs. It was especially apparent to her during a low-key stretch when she was simply trying to build fitness.

I hated that every run I went on, I felt like I had to check my pace and my distance and whatever else, she said. So I just decided that I was going to lay off it for a while and switch to a regular watch.

She never went back. MacLean, 28, who now wears an Armitron Dragonfly that she said she picked up for $10 at Walmart, acknowledged that there were certain workouts when a GPS watch would come in handy, like when she did a tempo run by herself. (Tempo runs are faster than easy jogs, and frequently run at a prescribed pace.) But Mark Coogan, her coach, has long prioritized effort over pace, and MacLean logs her training in minutes rather than in miles.

I know Im at the elite level now, so not everything is going to be joyful, MacLean said. But when there are things that bring me a lot of joy, Im going to invest in them. And one of those things is the ability to avoid focusing on my pace during my runs.

Without the pressure of feeling as if she needs to account for every mile or, perish the thought, post her workouts for public inspection on Strava, the exercise-tracking platform MacLean has also gotten better about listening to her body. She has no qualms about bailing on an extra workout if she is feeling beat.

And Ill tell Mark that Im going for a walk instead, MacLean said. And hes like, OK!

Sam Prakel was a high school standout in Versailles, Ohio, when the assistant coach of his cross-country team introduced him to the magic of GPS watches. Prakel invested in one. It was a mistake from the start.

I just started running too fast on all my runs, Prakel said, and it became harder to recover from them because I was so focused on my pace. I learned pretty quickly that it wasnt good for me.

Prakel opted instead for a Timex Ironman, which he wore through his freshman year at the University of Oregon. When the band snapped in his sophomore year, he ordered another. Prakel, 28, has worn the same no-frills watch ever since through his time at Oregon, where he was a five-time all-American, and in more recent years as a pro miler for Adidas. He has never needed to change its battery.

The reigning U.S. indoor champion in the mens 1,500 and 3,000 meters, Prakel has a system that works for him, which is a throwback in a sense. What did any runners do before the advent of GPS watches? They estimated. In Prakels case, a 65-minute run is roughly equivalent to 10 miles and a half-hour jog is good for four miles. He does not need to be precise.

As long as I do the same things every week and keep it consistent, thats all that matters, he said, adding: I feel like Im in a better place when I dont have all that data to worry about.

For some runners, aesthetics also matter. Luke Houser, a junior at the University of Washington who won an N.C.A.A. championship in the mens indoor mile last winter, wears a vintage-inspired Casio with a digital display and a gold metal band. His teammates simply refer to it as the gold Casio.

I just think it looks cool, he said. Ive never been interested in cadence or heart rate, which I dont think is ever that accurate anyway. All you need to know is how you feel and the time. That does the job.

Kieran Lumb, who recently broke his own Canadian record in the mens 3,000 meters, is well aware that he is the type of person who is susceptible to the sweet lure of data.

At the University of British Columbia, Lumb majored in electrical engineering. Later, while running at Washington, he earned a masters degree in information systems. And for the longest time, no one who knew him was surprised that he maintained an Excel spreadsheet to catalog his sleep, workouts and something he called rated perceived fatigue.

Just trying to do a little bit of data science on myself, he said.

The twist is that Lumb, 25, who now runs professionally for the athletics apparel brand On, has not worn a GPS watch since he was a competitive cross-country skier growing up in Canada. He made the switch as a college freshman to a Casio calculator watch that didnt even have a proper lap function for track workouts.

So Id just have to remember all my splits, he said, and it was awesome.

Lumb noted that because many runners are naturally competitive, they can become obsessed with numbers. And the business of making it to the top of the heap as an elite runner can be especially taxing.

As a result, Lumbs coach, Andy Powell, tries to keep things as simple as possible. For Lumb, that has meant ditching his Excel folder in favor of Powells old-school approach: weekly workout sheets that his runners fill out and file in three-ring binders.

Theres something nice about slowing down and writing it by hand that I find almost endearing, Lumb said. Its taken a while for me to be less neurotic, but its liberating.

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Why Some Top Runners Prefer to Train Without a GPS Watch - The New York Times

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