Atharv Gyan

Data Science, Data Analysis, and Data Engineering: What's the Difference? Data science, data analysis, and data engineering are all closely related fields that involve working with data. However, there are some key differences between these three disciplines. Data Science Data science is a broad field that encompasses the collection, analysis, interpretation, and presentation of data. Data scientists use a variety of tools and techniques to extract insights from data, including machine learning, statistical analysis, and visualization. Data scientists typically have a strong background in mathematics, statistics, and computer science. Data Analysis Data analysis is a more focused field than data science. Data analysts use data to answer specific questions or solve particular problems. They typically use a variety of tools and techniques, such as SQL, Excel, and Tableau. Data analysts typically have a strong background in mathematics, statistics, and business. Data Engineering Data

Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision- making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data analysis often employs statistical and mathematical methods, but it also includes techniques such as machine learning and artificial intelligence. The goal of data analysis is to extract knowledge from data in order to make better decisions. Data analysis is a rapidly growing field, driven by the increasing availability of data and the development of new data analysis techniques. The future of data analysis is bright, with the potential to revolutionize many industries. What is Data Analysis? Data analysis is the process of extracting knowledge from data. It involves collecting, cleaning, organizing, and analyzing data to ide

P ython is a powerful high level object oriented programming language with a simple syntax. It has many applications but the major ones are web development, software development and data science. Data science is a field where meaningful insights are extracted from data to allow for decision making and planning in businesses. It combines math, statistics, programming, machine learning, artificial intelligence and advanced analytics.  Data Science project life cycle The data science project life cycle consists of various processes which include: data collection, data cleaning, exploratory data analysis, model building and model deployment. Python has multiple libraries which facilitates these processes hence making it suitable for data science. Examples of these libraries are pandas for data analysis, wrangling and cleaning, matplotlib and seaborn for data visualization, tensor flow and scikit-learn for machine learning, keras and pyTorch for deep learning, SciPy and NumPy for mathematic

We are pleased to announce a major update to JupyterLab Desktop which adds many new features with main focus on the user experience. JupyterLab Desktop is the cross-platform desktop application for JupyterLab and it is the quickest and easiest way to get started with Jupyter notebooks on your personal computer. JupyterLab Desktop Welcome Page Users are now presented with the Welcome Page when the app is first launched. It contains links to several session create options on the left and the Jupyter News feed on the right. The news feed is populated using the Jupyter blog contents and is aimed to keep you up to date with the news and events related to Jupyter ecosystem projects. Clicking on a news item opens the blog post in browser. Sessions and Projects With this release we are introducing the concept of sessions and projects. Sessions are representations of local project launches and connections to existing JupyterLab servers. Each JupyterLab UI window in the app is associated with a