#Data Visualization with Python course

[trainingin2inglobal]

Visualizing Insights: A Pythonic Approach to Data Visualization

In in recent times’s statistics-driven global, the functionality to extract actionable insights from complex datasets is a valuable skill that marketers, analysts and experts all through industries require Data visualization to use specifically on this procedure, developing great insights into unstructured records. In this weblog we discover the energy of records visualization and the manner Python empowers us to unencumber our entire capacity.

Why Data Visualization Matters

Data visualization isn't quite much creating charts and fancy diagrams; It’s approximately reworking raw records into visible representations that supply meaningful insights efficaciously and efficaciously. Visualization permits us to see styles, tendencies and relationships in information that might otherwise continue to be hidden in a sequence of numbers and columns.

By presenting facts visually, we are able to communicate complicated ideas truly and concisely, making it much less difficult for stakeholders to recognize and make informed decisions. Whether it’s discovering market tendencies, studying consumer behavior, or tracking enterprise metrics, statistics visualization is a powerful device for producing actionable insights from statistics

Python advantages

Python has emerged as a famous preference for information evaluation and visualization due to its versatility, ease of use and robust environment of libraries and tools The first information visualization library in Python one in all this is Matplotlib, which gives a bendy framework for developing a massive style of static, interactive and energetic graphs

Matplotlib's intuitive interface allows users to create tremendous plots with only some strains of code, making it best for beginners and skilled statistics analysts further to Matplotlib, Python offers exceptional effective plotting libraries collectively with Seaborn, Plotly, and Bokeh, every with their private strengths and abilties.

Getting Started with Pythonic Data Visualization

To illustrate the strength of facts visualization in Python, let's bear in mind a sensible instance. Suppose we've got a dataset containing income records for a retail enterprise during the last yr. Our intention is to visualise the monthly sales traits and pick out any seasonal styles or anomalies.

The full sales for every month, and plot the month-to-month income tendencies. The resulting visualization gives a clear image of the way sales have evolved over the path of the year, allowing us to become aware of any patterns or developments.

Conclusion

Data visualization is a powerful device for reworking raw statistics into actionable insights. By leveraging the abilities of Python and its wealthy environment of visualization libraries, we are able to create compelling visualizations that speak complicated ideas efficaciously.

Whether you are a statistics analyst, enterprise expert, or researcher, learning the art of information visualization can enhance your capacity to extract insights, make knowledgeable choices, and pressure fine outcomes. With a Pythonic method to records visualization, the opportunities are infinite.

In destiny blog posts, we are able to delve deeper into superior statistics visualization strategies and explore additional Python libraries for growing interactive and dynamic visualizations. Stay tuned for greater insights and courses to your journey to learn Data  visualization with Python .

[tchaikovskym]

How's it going learning python? I needed to learn the very, very basics last year and it was fun, but at the same time so difficult to understand and remember all the codes, especially functions and all the stuff for diagrams like matplotlib etc. Hope it's going well for you :)

Listen bestie I'm at a print("Hello, World!") and add # before comments stage :D learned how to define structures and print(type(x)). I'm a baby.

But overall, I think it will be fun to use! If I manage to do anything :D because I have a shit ton to do on top of all that (full time work, writing an introduction to an article, making a med student a material for his cell bio microscope slides, and making an hour long lecture abt my thesis for school kids :D it's all due next week :D)

But overall I think I will be okay, because complicated things don't scare me as much as they used to. And besides I won't have to remember everything by heart. If it's me and stackoverflow against python let it be, I've been there with R.

And I'm actually very scared of matplotlib bc it was an extra credit task we had to perform on a hpc cluster and I had no idea how to do it (the rest of the code was given to us ready except the extra credit tasks). It was the end of the lesson so I just. Left.

[tandeminformaticsmadurai]

UI/UX Design Mastery Course in Madurai – Elevate Your Design Skills

The UI/UX Design Mastery Course in Madurai is an ideal program for aspiring designers and tech enthusiasts eager to break into the world of user interface and user experience design. Tailored for beginners as well as professionals looking to enhance their skills, this course offers a perfect blend of theoretical knowledge and hands-on experience.

Participants will learn the fundamentals of design thinking, user research, wireframing, prototyping, and usability testing. The curriculum also covers industry-leading tools like Figma, Adobe XD, and Sketch, ensuring learners gain practical expertise. The course emphasizes real-world projects and case studies to help students build an impressive design portfolio.

Guided by experienced mentors and industry experts, students receive personalized feedback and career guidance throughout the program. Whether you aim to work with top tech companies, start your freelance design journey, or build your own product, this course equips you with the skills to deliver intuitive and engaging digital experiences.

Located in Madurai, this program offers in-person and hybrid learning options, making it accessible and flexible for learners from different backgrounds. With a focus on creativity, innovation, and user-centric design, the UI/UX Design Mastery Course is your gateway to a rewarding career in the dynamic field of digital design.

Enroll today to unlock the power of design and transform your ideas into user-friendly interfaces that make a lasting impact.

[igmpi]

Explore IGMPI’s Big Data Analytics program, designed for professionals seeking expertise in data-driven decision-making. Learn advanced analytics techniques, data mining, machine learning, and business intelligence tools to excel in the fast-evolving world of big data.

[uegub]

Exploring the Key Facets of Data in Data Science

Data is at the heart of everything in data science. But it’s not just about having a lot of data; it’s about knowing how to handle it, process it, and extract meaningful insights from it. In data science, we work with data in many different forms, and each form has its own unique characteristics. In this article, we’re going to take a closer look at the important aspects of data that data scientists must understand to unlock its full potential. 

1. Types of Data: Understanding the Basics 

When you dive into data science, one of the first things you’ll encounter is understanding the different types of data. Not all data is created equal, and recognizing the type you’re working with helps you figure out the best tools and techniques to use. 

Structured Data: This is the kind of data that fits neatly into rows and columns, like what you'd find in a spreadsheet or a relational database. Structured data is easy to analyze, and tools like SQL are perfect for working with it. Examples include sales data, employee records, or product inventories. 

Unstructured Data: Unstructured data is a bit trickier. It’s not organized in any set format. Think about things like social media posts, emails, or videos. This type of data often requires advanced techniques like natural language processing (NLP) or computer vision to make sense of it. 

Semi-structured Data: This type of data is somewhere in the middle. It has some structure but isn’t as organized as structured data. Examples include XML or JSON files. You’ll often encounter semi-structured data when pulling information from websites or APIs. 

Knowing the type of data you’re dealing with is crucial. It will guide you in selecting the right tools and methods for analysis, which is the foundation of any data science project. 

2. Data Quality: The Foundation of Reliable Insights 

Good data is essential to good results. Data quality is a huge factor in determining how reliable your analysis will be. Without high-quality data, the insights you generate could be inaccurate or misleading. 

Accuracy: Data needs to be accurate. Even small errors can distort your analysis. If the numbers you're working with are wrong, the conclusions you draw won’t be reliable. This could be something like a typo in a database entry or an incorrect sensor reading. 

Consistency: Consistency means the data is uniform across different sources. For example, you don't want to have "NY" and "New York" both used to describe the same location in different parts of your dataset. Consistency makes it easier to work with the data without having to constantly fix small discrepancies. 

Completeness: Missing data is a common issue, but it can be a real problem. If you’re working with a dataset that has missing values, your model or analysis might not be as effective. Sometimes you can fill in missing data or remove incomplete rows, but it’s important to be mindful of how these missing pieces affect your overall results. 

Data scientists spend a lot of time cleaning and validating data because, at the end of the day, your conclusions are only as good as the data you work with. 

3. Transforming and Preprocessing Data 

Once you’ve got your data, it often needs some work before you can start drawing insights from it. Raw data usually isn’t in the best form for analysis, so transforming and preprocessing it is an essential step. 

Normalization and Scaling: When different features of your data are on different scales, it can cause problems with some machine learning algorithms. For example, if one feature is measured in thousands and another is measured in ones, the algorithm might give more importance to the larger values. Normalizing or scaling your data ensures that everything is on a comparable scale. 

Feature Engineering: This involves creating new features from the data you already have. It’s a way to make your data more useful for predictive modeling. For example, if you have a timestamp, you could extract features like day of the week, month, or season to see if they have any impact on your target variable. 

Handling Missing Data: Missing data happens. Sometimes it’s due to errors in data collection, and sometimes it’s just unavoidable. You might choose to fill in missing values based on the mean or median, or, in some cases, remove rows or columns that have too many missing values. 

These steps ensure that your data is clean, consistent, and ready for the next stage of analysis. 

4. Integrating and Aggregating Data 

In real-world projects, you rarely get all your data in one place. More often than not, data comes from multiple sources: databases, APIs, spreadsheets, or even data from sensors. This is where data integration comes in. 

Data Integration: This process involves bringing all your data together into one cohesive dataset. Sometimes, this means matching up similar records from different sources, and other times it means transforming data into a common format. Integration helps you work with a unified dataset that tells a full story. 

Data Aggregation: Sometimes you don’t need all the data. Instead, you might want to summarize it. Aggregation involves combining data points into a summary form. For example, if you're analyzing sales, you might aggregate the data by region or time period (like daily or monthly sales). This helps make large datasets more manageable and reveals high-level insights. 

When you're dealing with large datasets, integration and aggregation help you zoom out and see the bigger picture. 

5. The Importance of Data Visualization 

Data visualization is all about telling the story of your data. It’s one thing to find trends and patterns, but it’s another to be able to communicate those findings in a way that others can understand. Visualization is a powerful tool for data scientists because it makes complex information more accessible. 

Spotting Trends: Graphs and charts can help you quickly see patterns over time, like a rise in sales during the holiday season or a dip in customer satisfaction after a product launch. 

Identifying Outliers: Visualizations also help you identify anomalies in the data. For instance, a bar chart might highlight an unusually high spike in sales that warrants further investigation. 

Making Sense of Complex Data: Some datasets are just too large to process without visualization. A heatmap or scatter plot can distill complex datasets into something much easier to digest. 

Effective data visualization turns raw data into insights that are easier to communicate, which is a critical skill for any data scientist. 

6. Data Security and Privacy: Ethical Considerations 

In today’s data-driven world, privacy and security are huge concerns. As a data scientist, it’s your responsibility to ensure that the data you’re working with is handled ethically and securely. 

Data Anonymization: If you’re working with sensitive information, it’s crucial to remove personally identifiable information (PII) to protect people's privacy. Anonymizing data ensures that individuals’ identities remain secure. 

Secure Data Storage: Data should always be stored securely to prevent unauthorized access. This might mean using encrypted storage solutions or following best practices for data security. 

Ethical Use of Data: Finally, data scientists need to be mindful of how the data is being used. For example, if you’re using data to make business decisions or predictions, it’s essential to ensure that your models are not biased in ways that could negatively impact certain groups of people. 

Data security and privacy are not just legal requirements—they’re also ethical obligations that should be taken seriously in every data science project. 

Conclusion 

Data is the foundation of data science, but it's not just about having lots of it. The true power of data comes from understanding its many facets, from its structure and quality to how it’s processed, integrated, and visualized. By mastering these key elements, you can ensure that your data science projects are built on solid ground, leading to reliable insights and effective decision-making. 

If you’re ready to dive deeper into data science and learn more about handling data in all its forms, seeking out a data science course nearby might be a great way to gain hands-on experience and expand your skill set. 

[cacmsinsitute]

Why Data Analytics is the Skill of the Future (And How to Get Ahead)

In today's fast-paced digital landscape, the ability to analyse and interpret data is more important than ever. With the globe collecting data at an unprecedented rate, industries are turning to data analytics to drive decisions, enhance efficiency, and gain a competitive advantage. As a result, data analytics is rapidly becoming one of the most valued skills in almost every industry, and individuals who understand it are well-positioned for a prosperous career.

The Increasing Demand for Data Analytics

Data analytics is more than just a buzzword; it's a fast expanding field that is impacting industries around the world. According to the U.S. Bureau of Labour Statistics, demand for data science and analytics experts is predicted to increase by 35% between 2021 and 2031, greatly above the average for all occupations. This rapid expansion emphasizes the importance of data analytics as a vital business function, with organizations relying on data to make informed decisions and optimize operations.

Data-driven tactics are being adopted in a variety of industries, including healthcare, finance, marketing, and ecommerce. Companies seek experienced people who can use data to foresee trends, analyze customer behavior, streamline operations, and improve overall decision-making. As a result, data analytics specialists are in high demand, and mastering this ability can lead to a wide range of opportunities in this competitive area.

Why Data Analytics is Important for Future Careers

Developing data analytics abilities is one of the most effective strategies for students and professionals to future-proof their careers. As businesses increasingly rely on data-driven insights, people who can comprehend and analyze data are well-positioned for long-term success.

Data analytics is a broad field that applies to almost every sector. Understanding data is essential for anyone who wants to work in corporate planning, marketing, finance, or healthcare. The capacity to analyze and interpret massive data sets enables professionals to make better decisions, discover hidden possibilities, and deliver actionable insights. Businesses will increasingly prioritize data-driven strategies, making data analytics experts invaluable assets.

How to Advance in Data Analytics: Enroll in Offline Courses

To succeed in this competitive sector, hands-on experience is required. While there are several online courses accessible, offline learning provides the benefits of personalized instruction, engaging learning environments, and direct access to knowledgeable professors. CACMS Institute in Amritsar offers offline data analytics courses that educate students with the practical skills and knowledge required to succeed in this rapidly expanding sector.

CACMS Institute provides expert advice in a classroom setting where you may ask real-time questions, work on actual projects, and engage with peers on data-driven challenges. The curriculum is intended to emphasize the fundamentals of data analytics, covering important tools such as Python, SQL, Power BI, Tableau, and Excel. These tools are vital for anyone interested in pursuing a career in data analytics since they allow experts to manage, visualize, and analyze data efficiently.

Future-Proof Your Career with CACMS Institute

CACMS Institute provides an organized, offline learning environment that teaches more than just theory; it also teaches hands-on, practical skills. CACMS' courses focus on practical data analytics applications, ensuring that students not only learn the tools and techniques but also understand how to apply them in real-world corporate contexts.

If you want to advance in the field of data analytics, there's never been a better opportunity to participate in an offline course at CACMS Institute. The combination of professional instructors, a well crafted curriculum, and an engaging classroom atmosphere will prepare you for success in tomorrow's data-driven world.

Take the first step towards safeguarding your future now! Contact CACMS Institute at +91 8288040281 or visit cacms.in for more information and to enrol in our data analytics courses in Amritsar.

[techyseeducation]

Data Analytics Training In Marathahalli

Techyse Education in Marathahalli, Bangalore, offers specialized Data Analytics Training in Marathahalli for individuals looking to build expertise in Python, Power BI, and data analysis techniques. Their industry-aligned courses focus on practical learning through real-world projects, ensuring students gain hands-on experience in data manipulation, visualization, and dashboard creation. Whether you are a beginner or an experienced professional, Techyse’s programs are designed to enhance your skill set, making you job-ready for roles in data analytics.

Comprehensive Data Analytics Training in Marathahalli Techyse Education takes pride in delivering high-quality Data Analytics Training in Marathahalli, backed by experienced instructors with deep industry knowledge. The curriculum covers essential tools and techniques, from data wrangling with Python to creating interactive dashboards using Power BI, ensuring students are prepared to meet industry demands. With personalized mentorship, career support, and placement assistance, Techyse provides a well-rounded learning experience. Whether aiming for career growth or a fresh start in data analytics, Techyse Education equips learners with the skills to excel in a competitive job market.

Techyse Education | Data Analyst, Python, Power BI Training in Marathahalli, Bangalore

18, Krishna Summit, 307, 3rd Floor, Aswath Nagar, Next to Canara Bank, Marathahalli, Bangalore, Karnataka 560037

Phone: 098445 14333 Website : https://techyse.in/

Our Google Map Location is : https://maps.app.goo.gl/dLsBM669nKHTutxu9

Follow us: Facebook : https://www.facebook.com/techyse.education/ Twitter: https://x.com/techyse_edu/ Instagram : https://www.instagram.com/techyeseducation/ LinkedIn : https://www.linkedin.com/company/techyse-education/ Youtube: https://www.youtube.com/@TechyseEducation

[mvishnukumar]

Popular Data Visualization Tools:

H,

Data visualization is essential for interpreting complex data, and there are several tools available for businesses to create meaningful visual representations. Tableau is one of the most widely used tools, known for its ease of use and ability to connect to multiple data sources, including spreadsheets, databases, and cloud services. It offers a variety of visual options, from bar charts to geographic maps, and allows users to create interactive dashboards. Microsoft Power BI is another popular tool, especially for businesses already using Microsoft products. Power BI integrates seamlessly with Excel and other Microsoft tools, and provides a robust platform for data modeling, visualization, and reporting. Google Data Studio is a free tool that’s great for creating simple, shareable dashboards, particularly when working with Google Analytics data. Other tools like QlikView and D3.js offer more advanced customization for those with specific needs. 

These tools not only help businesses understand their data but also communicate insights to stakeholders in an intuitive and visually appealing way.

[sandy1674686]

How to Become a Data Scientist: Essential Skills and Training Programs

In today's data-driven world, the role of a data scientist has become increasingly valuable across industries. With the ability to analyze complex data sets and derive meaningful insights, data scientists play a crucial role in decision-making processes for businesses. If you're interested in pursuing a career in this field, it's important to understand the essential skills and training programs that can help you become a successful data scientist.

Quantitative and Analytical Skills: First and foremost, data scientists must possess strong quantitative and analytical skills. They should have a solid foundation in mathematics, statistics, and computational methods. Proficiency in programming languages such as Python or R is also essential for data manipulation and analysis. These skills form the building blocks for data scientists to effectively understand, interpret, and work with data.

Data Visualization and Communication: Data scientists need to not only analyze data but also present their findings in a clear and concise manner. data visualization using Power Bi or Tableau allow them to create compelling visual representations of complex data sets. Effective communication skills, both written and verbal, are also required to explain their findings to non-technical stakeholders within the organization.

Machine Learning and Artificial Intelligence: Machine learning and artificial intelligence (AI) are fundamental tools in the data science toolkit. Data scientists must have a good understanding of various machine learning algorithms and techniques. Knowledge of frameworks like TensorFlow, PyTorch, or scikit-learn is essential for building predictive models, clustering, and classification tasks. Staying updated with the latest advancements in AI is crucial for harnessing the true potential of data science.

Domain Expertise: While technical skills are important, having domain expertise is equally vital for a data scientist. Understanding the industry or field in which you work allows you to ask the right questions, identify relevant data sources, and generate valuable insights. Whether it’s finance, healthcare, marketing, or any other sector, acquiring domain knowledge will set you apart as a data scientist.

Training Programs:

Academic Degrees: Pursuing a Bachelor's, Master's, or Ph.D. degree in a field like computer science, statistics, mathematics, or data science itself provides a solid foundation for a career in data science. These programs cover theoretical concepts, statistical methods, programming skills, and practical applications.

Online Courses and Bootcamps: There has been a rise in online platforms offering data science courses and bootcamps. Platforms like Coursera, edX, and Udacity provide comprehensive courses covering various aspects of data science, including programming, statistics, and machine learning with python. These courses are flexible and allow you to learn at your own pace.

Industry-Specific Training: Some organizations offer specialized training programs tailored to the specific needs of their industry. These programs often provide hands-on experience with real-world data sets and practical problem-solving scenarios. Attending industry conferences, webinars, and workshops can also supplement your learning journey.

Conclusion:

Becoming a successful data scientist requires a combination of technical expertise, analytical skills, and domain knowledge. By honing your skills in mathematics, programming, machine learning, and data visualization, you can navigate the world of data science more effectively. Additionally, pursuing academic degrees or enrolling in online courses and industry-specific training programs can provide you with a well-rounded education. With a consistent effort to upgrade your skills and stay updated with the latest trends, you can establish yourself as a valuable data scientist in today's data-driven landscape.

[tandeminformaticsmadurai]

At Tandem Informatics, we are dedicated to empowering the next generation of tech professionals through cutting-edge training and hands-on experience. Our courses cover essential programming languages, frameworks, and industry best practices to ensure you are job-ready. With our top-tier corporate-level training, we have established a benchmark for placing students in leading companies. We specialize in equipping individuals with the skills and certifications needed to thrive in the dynamic world of technology. Imagine becoming a Cisco Certified Network Associate (CCNA), a Microsoft Certified Technology Specialist (MCTS), or an Oracle Certified Professional Java Programmer (OCPJP). The possibilities are endless! Our expert trainers also offer comprehensive courses in areas such as 3D Animation, Visual Effects, UI/UX Designing, and much more.

[subo797112]

What steps can I take to get ready for a data science course prior to enrolling?

The promising discipline of data science combines statistics, machine learning, and data analysis to analyze large databases for insightful information. It's crucial to get ready for the future  journey if you're preparing to start a data science degree. You may use this article as a guide to prepare for a data science course before enrolling.

1. Strengthen Your Math and Statistics Fundamentals

Statistical analysis and mathematical ideas are fundamental to data science. Be sure you understand the fundamentals of algebra, calculus, and probability before enrolling in a data science course. Learn about statistical concepts such as standard deviation, mean, median, and hypothesis testing. You may improve your math's and statistics abilities for free by using resources like Coursera and Khan Academy.

2. Learn Programming Languages

Programming is at the heart of data science. Python and R are the most commonly used languages in the field. Familiarize yourself with the basics of at least one of these languages. Online tutorials and coding platforms like Nearlearn, Codecademy or DataCamp provide interactive courses to get you started. Learning the basics of data manipulation, visualization, and statistical analysis in your chosen language is a great start.

3. Get Comfortable with Data Tools

Data science often involves working with large datasets. Tools like Jupyter Notebook, pandas, and NumPy in Python, or libraries like dplyr and ggplot2 in R, are essential for data manipulation and visualization. Explore these tools and practise with sample datasets to become proficient in data handling and analysis.

4. Understand Data Cleaning

Data is rarely perfect; it often requires cleaning and preprocessing. Familiarize yourself with techniques for data cleaning, such as handling missing values, outliers, and data imputation. Learning how to prepare data for analysis is a crucial skill in data science.

5. Explore Online Courses and Resources

Before enrolling in a formal course, take advantage of free online courses and resources. Platforms like Coursera, edX, and Udacity offer introductory data science courses. These courses will give you a taste of what to expect and help you determine if data science is the right path for you.

6. Practice Problem Solving

Data science is about solving complex problems. Engage in problem-solving challenges on platforms like Kaggle and LeetCode. These platforms offer real-world data science problems and coding challenges. They're a great way to apply your knowledge, learn from others, and build your problem-solving skills.

7. Read Relevant Books and Blogs

Explore books like "Python for Data Analysis" by Wes McKinney and "Introduction to Statistical Learning" by Gareth James. Blogs like Towards Data Science, Data Science Central, and FlowingData provide valuable insights and the latest trends in data science.

8. Build a Portfolio

Create a portfolio of data science projects. Even if they're small, hands-on projects, they demonstrate your practical skills to potential employers. Share your portfolio on platforms like GitHub to showcase your work.

9. Connect with the Data Science Community

Join data science forums, attend meetups, and follow data scientists on social media. Networking with others in the field can provide valuable insights and connections that can benefit your journey in data science.

10. Set Clear Goals

Before enrolling in a data science course, define your goals. Understand why you want to pursue data science and what specific areas or industries interest you. Setting clear objectives will help you choose the right course and focus your efforts on what matters most to you.

By following these steps and dedicating time to prepare for your data science course, you'll be better equipped to grasp the concepts and excel in your studies. Remember that the world of data science is vast and ever-evolving, so stay curious, persistent, and eager to learn. Your journey in data science awaits!

[ayeforscotland]

Ad | Hi folks, I'd previously been getting into the swing of posting Humble Bundle deals and the charities they were helping. With any non-bundle purchases helping to raise money for Endometriosis research and support. Then Humble decided to outsource their partner program to a system called 'Impact' which has honestly been a massive pain to get my head round. Looks like I can't link to bundles directly and they only give me the above link to share.

There is currently:

The TellTale Games Bundle featuring Batman, the Expanse, the Walking Dead, the Wolf Among Us - Currently raising money for Save the Children

The Sid Meier Collection with every Civilization game and DLC I can think of - Raising money for Covenant House and Michael J. Fox Foundation for Parkinson's Research

Math for Programmers Book Bundle which contains a whole bunch of data science, cryptography and Python books - Raising money for Girls Who Code

Learn Unit Game Development Course - From Shaders to 3D to a course on Game Feel - Raising money for Oceana

Super Game Asset Bundle for Unreal, Godot, and Unity. Over 7000 audio, visual and environmental assets - Raising money for Direct Relief.

Not sure if this format is okay, it requires you to visit the link and navigate but hope it helps? Let me know.

[macademiastudies]

November 8th Journal

Looking through my to-do lists this month, I'm realizing that part of the reason why I'm not able to cross things off my list so easily is because the points I add to my list are fairly ambiguous and don't have a clear finish point. For instance, when I would write "learn binary trees on Python"; well what does that mean? Having actual actionable and completable items on my to-do list helps to better set daily goals for myself as well as to actually complete those goals. It often get intimidated seeing such large tasks that I didn't know how to approach so breaking those down for myself has made a world's difference. Yes, yes I'm a yapper.

To-do: 🪶Data course 4.3 🪶Data course 4.4 🪶Data course assignment 1 - unit 3 🪶Data course assignment 2 - unit 4 🪶Type out the rest of my notes 🪶Add visuals to my notes 🪶Finish text-based slot machine game 🪶Latin level 2 🪶Some data tasks for work

[frank-olivier]

The story of BASIC’s development began in 1963, when Kemeny and Kurtz, both mathematics professors at Dartmouth, recognized the need for a programming language that could be used by non-technical students. At the time, most programming languages were complex and required a strong background in mathematics and computer science. Kemeny and Kurtz wanted to create a language that would allow students from all disciplines to use computers, regardless of their technical expertise.

The development of BASIC was a collaborative effort between Kemeny, Kurtz, and a team of students, including Mary Kenneth Keller, John McGeachie, and others. The team worked tirelessly to design a language that was easy to learn and use, with a syntax that was simple and intuitive. They drew inspiration from existing programming languages, such as ALGOL and FORTRAN, but also introduced many innovative features that would become hallmarks of the BASIC language.

One of the key innovations of BASIC was its use of simple, English-like commands. Unlike other programming languages, which required users to learn complex syntax and notation, BASIC used commands such as “PRINT” and “INPUT” that were easy to understand and remember. This made it possible for non-technical users to write programs and interact with the computer, without needing to have a deep understanding of computer science.

BASIC was first implemented on the Dartmouth Time-Sharing System, a pioneering computer system that allowed multiple users to interact with the computer simultaneously. The Time-Sharing System was a major innovation in itself, as it allowed users to share the computer’s resources and work on their own projects independently. With BASIC, users could write programs, run simulations, and analyze data, all from the comfort of their own terminals.

The impact of BASIC was immediate and profound. The language quickly gained popularity, not just at Dartmouth, but also at other universities and institutions around the world. It became the language of choice for many introductory programming courses, and its simplicity and ease of use made it an ideal language for beginners. As the personal computer revolution took hold in the 1970s and 1980s, BASIC became the language of choice for many hobbyists and enthusiasts, who used it to write games, utilities, and other applications.

Today, BASIC remains a popular language, with many variants and implementations available. While it may not be as widely used as it once was, its influence can still be seen in many modern programming languages, including Visual Basic, Python, and JavaScript. The development of BASIC was a major milestone in the history of computer science, as it democratized computing and made it accessible to a wider range of people.

The Birth of BASIC (Dartmouth College, August 2014)

Friday, April 25, 2025