#coding....likely Python......and that involves math but it is interesting....

[jestersdlc]

.....self ban from drawing is a TERRIBLE THING

If I can't draw what am I supposed to do??? :'D

....brain is hyperfixated on drawing and the animatic so........brain is not helpful

[fortunerobotic]

Robotic Classes for Kids

Children are not just consumers in the rapidly changing world of technology, but they also have the capacity to develop unique inventions. Once believed to be the purview of engineers and scientists, robotics is increasingly being used in children's classrooms and extracurricular activities. Giving kids robotic instruction is a step toward equipping their minds to succeed in a tech-driven future, not merely a passing educational fad.

Why Robotics for Kids?

Develop Problem-Solving Skills: Children are challenged by robotics to exercise critical thinking and come up with original solutions to problems that arise in the real world.

Improve Teamwork: The majority of robotic projects involve collaboration, which teaches children the value of teamwork.

Promote Creativity: Creating and programming robots provides a special way to express creativity and originality.

Develop Confidence: Building a robot or completing a coding task successfully increases one's sense of accomplishment and self-worth.

Get Ready for Future Careers: Learning about robotics can lead to an early interest in technology, engineering, and artificial intelligence (AI), opening doors to new opportunities.

Key Components of Robotic Classes

Understanding the definition and operation of robots is the first step in learning about robotics.

Building Robots: Building robots with kits such as Arduino, VEX Robotics, or LEGO Mindstorms.

Programming Fundamentals: To manage robot behavior, learn to code in languages like Scratch, Python, or Blockly.

Using abilities to finish jobs or participate in robotics contests are examples of problem-solving challenges.

Integration with Other Subjects: For a well-rounded experience, connect robotics projects with physics, math, and even the arts.

Popular Robotics Programs for Kids

Building and programming LEGO robots to tackle real-world problems is the main goal of the FIRST LEGO League.

Children are encouraged to create and program robots for competition through the VEX IQ Challenge.

Code Ninjas: Offers robotics and coding instruction in an entertaining, game-like setting.

RoboThink: Provides interactive engineering and robotics courses.

Online Platforms: Children can take robotics courses online from sites like Tynker, Code.org, and Coursera.

Children's robotics programs are an investment in the future, giving them the abilities and perspective necessary to thrive in a technologically advanced society. These programs foster critical thinking, creativity, and teamwork in addition to creating robots, laying the groundwork for success and innovation. Whether through community centers, internet platforms, or school programs, exposing kids to robotics opens them to a world of opportunities and equips them to be tomorrow's innovators.

To know more, click here.

[makers-muse]

Which STEM lab activities are most popular among teachers? 

STEM education has revolutionized the way students learn by integrating Science, Technology, Engineering, Arts, and Mathematics into cohesive, hands-on activities. For teachers, STEM labs are not just classrooms; they are innovation hubs where students explore, create, and learn by doing. But which activities resonate the most with teachers and students alike? Let’s dive into some of the most popular STEM lab activities that are winning hearts in classrooms worldwide. 

1. Designing and Building Balloon-Powered Cars 

This classic activity challenges students to design cars powered by the simple yet effective propulsion of a balloon. It introduces concepts of motion, force, and energy conservation while leaving room for creative engineering designs. 

How It Works: 

Students use materials like cardboard, straws, and balloons to construct their cars. 

They experiment with different designs to optimize speed and distance. 

Why Teachers Love It: 

Encourages hands-on exploration of physics principles. 

Fosters creativity and problem-solving. 

Accessible for students of various age groups. 

2. Robotics and Coding Challenges 

Robotics and coding have become staples in modern STEM labs. Whether students are programming a robot to complete a maze or using Scratch to create animations, these activities enhance computational thinking and problem-solving skills. 

Popular Tools and Kits: 

LEGO Mindstorms or VEX Robotics kits for building robots. 

Scratch or Python for beginner-friendly coding projects. 

Microcontrollers like Arduino for advanced programming. 

Why Teachers Love It: 

Provides a clear connection between coding and real-world applications. 

Builds logical reasoning and sequential thinking. 

Engages students with diverse interests. 

3. DIY Weather Station 

Weather stations allow students to delve into the world of environmental science by building devices to measure weather conditions such as temperature, humidity, and wind speed. 

How It Works: 

Students assemble sensors or use simple materials to create weather-measuring instruments. 

They collect and analyze data to understand weather patterns. 

Why Teachers Love It: 

Combines engineering with real-world science. 

Sparks discussions about climate change and environmental responsibility. 

Encourages teamwork and data analysis. 

4. Art-Infused STEM Projects 

Activities that merge art with STEM concepts are particularly appealing to teachers aiming to nurture creativity. Projects like designing fractal art or crafting models using 3D printing blend technical skills with artistic expression. Learn more 

Examples: 

Fractal geometry art using math principles. 

Sculptures created with 3D modeling software and printers. 

Why Teachers Love It: 

Makes math and science more approachable. 

Appeals to students with artistic inclinations. 

Encourages thinking outside the box. 

5. Solar-Powered Machines 

With the growing emphasis on renewable energy, building solar-powered devices has become a popular STEM activity. Students can create solar-powered cars, boats, or simple machines to learn about energy efficiency. 

Why It’s Popular: 

Demonstrates the principles of sustainable engineering. 

Provides a hands-on introduction to renewable energy sources. 

Develops problem-solving and critical-thinking skills. 

6. Chemistry Experiments: Making Slime and Beyond 

Chemistry experiments, particularly ones involving polymers like slime, are timeless hits in STEM labs. They make abstract chemical concepts tangible and enjoyable for students. 

Popular Experiments: 

Making slime to explore viscosity and chemical bonds. 

Creating a baking soda and vinegar volcano to understand chemical reactions. 

Why Teachers Love It: 

Easy to prepare with readily available materials. 

Visually engaging and fun for all ages. 

Sparks curiosity about how substances interact. 

7. Virtual Reality (VR) Expeditions 

Virtual reality activities are becoming increasingly popular as schools adopt cutting-edge technology. Teachers use VR to take students on virtual field trips or to simulate real-world environments for problem-solving. 

Why Teachers Love It: 

Offers immersive experiences that traditional labs cannot replicate. 

Enhances understanding of complex topics like anatomy, astronomy, or historical events. 

Appeals to tech-savvy students. 

8. Gardening and Hydroponics Projects 

Gardening in a lab? Yes! Teachers incorporate biology, chemistry, and environmental science by teaching students how to grow plants using hydroponic systems. 

Why It’s Engaging: 

Hands-on exploration of plant biology and ecosystems. 

Promotes sustainability and environmental awareness. 

Ties science to everyday life. 

Start inspiring young innovators today! 

The best STEM lab activities are those that spark curiosity and creativity in students while providing a solid foundation in science, technology, engineering, arts, and mathematics. Teachers play a vital role in making these activities exciting and educational. By incorporating popular activities like those mentioned above, educators can ensure their students thrive in a world that demands innovation and critical thinking. 

Ready to transform your classroom with engaging STEM activities? Start exploring these ideas today and watch your students soar! 

[chaitanyabusinesscollege]

Can I Take BCA Without Maths?

Are you passionate about technology and considering pursuing a Bachelor of Computer Applications (BCA) but wondering, "Can I take BCA without Maths?" This question is common, especially among students from non-science backgrounds who worry that their lack of mathematics may hinder them. The good news is that many colleges allow students to enroll in BCA even if they haven’t studied mathematics at the high school level. In this post, we’ll explore whether mathematics is essential, explain alternative pathways, and offer tips on how to thrive in the BCA program without math.

Overview of BCA

BCA is a three-year undergraduate program that provides students with a comprehensive understanding of computer science, programming languages, software development, and IT infrastructure. It is an excellent choice for students interested in software engineering, IT consulting, mobile app development, web design, and more.

The course equips students with skills in areas such as:

Programming Languages: Python, Java, C++

Web Development: HTML, CSS, JavaScript

Database Management: SQL, MongoDB

Operating Systems and Networking

Software Engineering and IT Project Management

Since BCA involves both technical knowledge and analytical skills, many students assume that mathematics is mandatory. But is it? Let’s find out.

Can I Take BCA Without Maths?

Yes, you can take BCA without maths in many universities and institutions. While mathematics may be a prerequisite for admission in some reputed institutions, a growing number of colleges have relaxed this requirement. These colleges focus more on logical thinking, computer literacy, and interest in technology than on math scores.

Here is how eligibility for BCA varies among different institutions:

Colleges with No Mathematics Requirement: Many private universities and open learning institutions offer BCA to students from arts, commerce, or other streams. Examples include:

IGNOU (Indira Gandhi National Open University)

Amity University

LPU (Lovely Professional University)

Chandigarh University

Colleges with Flexible Criteria: Some institutions require only that students complete 10+2 with a minimum of 45%-60% marks from any stream. They might conduct aptitude tests or assess logical reasoning but do not insist on mathematics as a subject.

Colleges that Require Mathematics: A few top universities, such as Delhi University or Christ University, may require students to have studied mathematics or computer science at the 12th-grade level. These are, however, the exceptions rather than the norm.

Why Mathematics Can Be Useful in BCA

Although many colleges allow BCA without mathematics, math skills can help in certain technical aspects of the course. Here are some subjects where mathematics knowledge proves helpful:

Data Structures and Algorithms: Concepts like time complexity and recursion involve analytical thinking, which mathematics helps develop.

Networking and Cryptography: Cryptographic algorithms use modular arithmetic and probability, making some math background advantageous.

Database Management: Writing optimized SQL queries requires logical thinking, which mathematics enhances.

Software Engineering: Mathematical models and flowcharts are sometimes used to design software processes systematically.

Tips to Succeed in BCA Without Maths

If you are worried about not having math skills, don’t fret. Many students without mathematics backgrounds thrive in BCA. Here are some tips to help you succeed:

Learn Basic Programming Languages: Start with Python, which is known for its simple syntax and logic-based approach. It’s a great introduction to coding, even for students without prior technical knowledge.

Develop Logical Reasoning: Engage in puzzles, coding challenges, and brain teasers to strengthen your logical thinking. Platforms like HackerRank and CodeChef offer beginner-friendly challenges.

Join Study Groups: Collaborate with peers in coding clubs or join online programming communities. Learning with others can make technical subjects more manageable.

Take Online Courses in Math (Optional): If you feel the need, take free or affordable math courses on Khan Academy, Coursera, or edX focusing on topics like discrete mathematics, which is relevant to computer science.

Work on Practical Projects: BCA is an application-oriented course, so focus on building websites, mobile apps, or simple software tools. Practical experience can give you a solid grasp of concepts without needing extensive theoretical knowledge.

Use Learning Resources Effectively: Books like "Programming in ANSI C" by E. Balagurusamy and tutorials on YouTube or Udemy can help you understand complex concepts easily.

Career Opportunities After BCA Without Maths

Even if you don’t have a math background, a BCA degree opens many career opportunities in the tech industry. Here are some popular job roles:

Software Developer: Build desktop or web applications using languages like Java or Python.

Web Developer: Design and develop websites using HTML, CSS, and JavaScript.

IT Support Specialist: Provide technical support for businesses and troubleshoot IT issues.

Digital Marketer: Work with SEO tools, Google Analytics, and online campaigns—minimal math required.

Database Administrator: Manage SQL databases efficiently (focus more on database management than math).

Cloud Computing Specialist: Use cloud platforms like AWS and Microsoft Azure to manage IT infrastructure.

For students interested in advancing their knowledge, pursuing MCA (Master of Computer Applications) is a great option. MCA builds on the knowledge gained during BCA and opens doors to roles in data science, artificial intelligence, and software architecture.

Challenges Without Math and How to Overcome Them

Students without mathematics might face some challenges, such as:

Difficulty in Learning Algorithms: Solution: Focus on understanding the logical flow of algorithms. Start with simpler algorithms like searching and sorting.

Challenges in Networking Concepts: Solution: Participate in practical labs where you can visualize how networks work rather than relying on theoretical understanding.

Fear of Technical Subjects: Solution: Start small and gradually increase the complexity of your tasks. Use online tutorials and coding bootcamps to build confidence.

With consistent effort and a growth mindset, you can overcome these challenges.

Conclusion: Can I Take BCA Without Maths?

In conclusion, yes, you can pursue BCA without mathematics in many colleges. While math can be helpful, it is not mandatory for success in the program. With the right resources, logical thinking, and dedication, students from any background—whether commerce, arts, or science—can excel in the BCA program.

If you're passionate about technology and willing to learn, your lack of a math background won’t stand in the way of building a successful career in the IT industry. So, if you're still asking yourself, "Can I take BCA without Maths?"—the answer is YES! With the right preparation and mindset, you can thrive in this field and unlock numerous career opportunities.

[divyanshsrivastava]

Top 5 Remote Internships for College Students on HireKingdom

Are you a college student eager to gain hands-on experience and build your resume while still in school? Remote internships offer the flexibility to balance your studies and professional growth. HireKingdom, a leading platform for connecting students with top companies, provides a wealth of opportunities across various fields. Here, we highlight the top five remote internships available on HireKingdom, focusing on data entry, data science, and related areas to help you kickstart your career journey.

1. Remote Data Entry Intern

Overview: The Remote Data Entry Internship is perfect for detail-oriented students who enjoy working with data. In this role, you’ll be responsible for ensuring the accuracy and integrity of data within the company’s systems.

Key Responsibilities:

Accurately inputting and updating data in company databases.

Reviewing and verifying data to identify errors.

Assisting with the maintenance and organization of data records.

Collaborating with team members to enhance data entry processes.

Skills Needed:

Excellent attention to detail.

Proficiency in Microsoft Office, particularly Excel.

Strong organizational skills.

Effective communication abilities.

Benefits: This internship offers practical experience in data management, providing a solid foundation for careers in administration, data analysis, and beyond. You’ll develop essential skills that are highly valued in many industries.

2. Remote Data Scientist Intern

Overview: The Remote Data Scientist Internship is ideal for students with a strong background in math, statistics, and programming. You’ll work on exciting projects that involve analyzing large datasets and developing predictive models.

Key Responsibilities:

Analyzing datasets to uncover trends and insights.

Building and testing machine learning models.

Visualizing data using tools like Tableau or Power BI.

Preparing detailed reports and presentations based on your findings.

Skills Needed:

Proficiency in Python or R.

Strong analytical and problem-solving skills.

Knowledge of statistical methods and machine learning algorithms.

Familiarity with data visualization tools.

Benefits: Gain hands-on experience in data science, one of the most in-demand fields today. This internship provides an opportunity to apply your academic knowledge to real-world problems, enhancing your employability in tech-driven industries.

3. Remote Business Data Analyst Intern

Overview: As a Remote Business Data Analyst Intern, you’ll help organizations make data-driven decisions. This role is perfect for students interested in combining business acumen with analytical skills.

Key Responsibilities:

Gathering and analyzing business data.

Identifying trends and providing actionable insights.

Creating dashboards and reports to present data findings.

Working closely with different departments to understand their data needs.

Skills Needed:

Strong analytical skills.

Proficiency in data analysis tools like Excel, SQL, or SAS.

Excellent communication and presentation abilities.

Basic understanding of business operations and strategies.

Benefits: This internship provides a comprehensive understanding of how data analysis supports business strategy, making it ideal for careers in business intelligence, consulting, and data analytics.

4. Remote Data Engineer Intern

Overview: The Remote Data Engineer Internship is designed for students who enjoy coding and have an interest in big data technologies. You’ll help build and maintain the infrastructure needed for data generation and analysis.

Key Responsibilities:

Developing and maintaining data pipelines.

Ensuring data quality and integrity.

Supporting data scientists by providing necessary data infrastructure.

Working with big data technologies like Hadoop or Spark.

Skills Needed:

Proficiency in programming languages such as Python, Java, or Scala.

Knowledge of SQL and database systems.

Understanding of big data technologies.

Strong problem-solving skills.

Benefits: This internship offers practical experience with the tools and technologies used in data engineering, providing a strong foundation for careers in big data, cloud computing, and software engineering.

5. Remote Machine Learning Intern

Overview: As a Remote Machine Learning Intern, you’ll develop and optimize algorithms that enable machines to learn from data. This role is ideal for students passionate about artificial intelligence and machine learning.

Key Responsibilities:

Designing and implementing machine learning models.

Conducting experiments to test model performance.

Analyzing algorithmic efficiency and effectiveness.

Documenting and presenting research findings.

Skills Needed:

Strong understanding of machine learning concepts and algorithms.

Proficiency in Python or Java.

Experience with machine learning libraries like TensorFlow or scikit-learn.

Analytical and critical thinking skills.

Benefits: Gain in-depth knowledge and hands-on experience in machine learning, a field with vast applications and high demand. This internship is a stepping stone to advanced roles in AI development and data science.

Conclusion

HireKingdom offers a diverse range of remote internships that cater to various interests and career goals. Whether you’re looking to delve into data entry, data science, or other related fields, these top five internships provide the perfect opportunity to gain valuable experience and develop your skills. Start exploring HireKingdom today and take the first step towards a successful and fulfilling career.

[vinooshiya]

The Right Path To Become A Data Scientist : A Beginner Guide

Embarking on the journey to become a data scientist may seem like entering a mysterious world, but fear not! Here’s a guide to illuminate your path, featuring the renowned ACTE Technologies Best Data Science Course. Let’s break down the steps to become a data scientist in simpler terms:

Step 1: Lay the Foundation Start by understanding the basics.

Data Awareness: Learn about different types of data like numbers, text, and images. Know the difference between structured and unstructured data.

Tool Mastery: Get familiar with programming languages like Python or R, as they’ll be your coding companions in the data science world.

Step 2: Develop Your Magical Toolkit Dive into foundational elements that make you a data wizard.

Math Mastery: You don’t need to be a math genius, but having a good understanding of statistics, linear algebra, and calculus will enhance your analytical skills.

Step 3: Master the Art of Machine Learning Machine learning is your trusty sidekick in making sense of data.

Understanding Machine Learning: Think of it as a wise mentor guiding your analytical endeavors.

Key Concepts: Get to know supervised and unsupervised learning, regression, classification — these are the tools for predictive analytics.

Step 4: Taming Big Data Enter the world of big data, where vast amounts of information await your expertise.

Big Data Principles: Understand the three Vs — Volume, Velocity, and Variety.

Tools: Get familiar with Hadoop and Spark, your allies in navigating big data challenges.

Step 5: Real-World Applications Test your skills in practical projects.

Individual Projects: Start small projects aligned with your interests, applying your skills in real-world scenarios.

Kaggle Competitions: Join these competitive arenas to strengthen and validate your skills.

Step 6: Never Stop Learning In the ever-evolving landscape of data science, continuous learning is key.

Literature Exploration: Dive into blogs and books to gain insights from seasoned practitioners.

Enroll in Courses: ACTE Technologies offers a comprehensive Data Science course in Bangalore, covering programming, statistical analysis, and machine learning. The course provides certifications and job placement opportunities, a valuable step in your journey.

Step 7: Join the Data Science Community Networking and collaboration are crucial for growth.

Networking: Engage with fellow learners on platforms like GitHub and Stack Overflow.

Your journey to becoming a data scientist involves learning, exploration, and practical application. With a solid foundation, continuous curiosity, and a reliable companion like the ACTE Technologies course, you’re on your way to mastering the art of data science. May your path be filled with discovery and success!

[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!

[rashlin3110]

"The Magic of Data Science Made Easy"

In our world today, data science is a term you might have heard quite a bit of. But what exactly is it, and why is it such a big deal? In this blog, we're going to break down the idea of data science using simple words so you can understand what it's all about.

What is data science?

Data science is like a detective story where we investigate data to find hidden treasures of information. Data scientists use special tools, tricks, and knowledge to make sense of loads of data.

The Basic Ingredients

Data: Data can be numbers, words, pictures, and more. It's the stuff we work with in data science.

Math and Stats: We use math and statistics to figure things out, find patterns, and make predictions.

Coding: Data scientists use computer programs, like Python or R, to work with data and create predictions.

Knowing the Field: It's important to understand the area you're working in, like healthcare, finance, or e-commerce. This helps you ask the right questions and understand the data better.

The Data Science Steps

Data science involves these key steps:

Collecting Data: We gather data from different places like sensors, databases, or the internet.

Cleaning Data: Before we start, we tidy up the data by removing errors and making sure it's all in order.

Exploring Data: We look at the data closely to spot patterns and understand what it's all about.

Making models: We create special tools or models to predict things or learn from the data.

Testing: We check how well our models work.

Using What We Find: Once we're done, we put our discoveries into action in real life.

Everyday Examples

Data science isn't just for experts; it's all around us. Here are some simple examples:

Doctor's Help: Data science can help doctors find diseases early and make hospitals run smoother.

Online Shopping: Ever wonder how websites recommend products? That's data science at work, suggesting things you might like.

Money Matters: Banks use data science to catch fraudulent transactions and protect your money.

Getting Around: Companies like Uber use data to make rides faster and cheaper.

Entertainment: When Netflix suggests a movie based on what you watched before, that's data science too.

The Big Impact

Data science changes the way businesses and our daily lives work. It helps things run better, makes products more personal, and helps people make smart choices.

So, data science is like a treasure hunt in the world of data. It's about collecting, cleaning, looking at, and using data to solve problems and make good choices. Data science is the key to understanding our data-filled world and making things happen behind the scenes when you get a weather forecast or a music recommendation.

"If you're eager to enhance your knowledge in the realm of data science, I highly recommend reaching out to ACTE Technologies. They offer comprehensive certification programs and are dedicated to aiding you in securing job opportunities. Their team of experienced educators can significantly enhance your learning experience, and the best part is that their services are available both online and offline. Taking a step-by-step approach and considering enrollment in one of their courses can be a wise decision if you have a keen interest in this field. If you have any more inquiries or require further assistance, please don't hesitate to ask. There's always more to learn, and I'm here to assist. Additionally, if you found this information valuable, please consider upvoting and following me for additional data science-related content. Your time and support are greatly appreciated. I wish you a wonderful day!

[akshayadev]

Your Journey to Becoming a Data Scientist

Data science is a dynamic and rewarding field that's in high demand. If you're curious about how to become a data scientist, here's a concise guide to get you started on this exciting journey.

Step 1: The Foundation

Math and Statistics: Begin with a solid understanding of math and statistics, the bedrock of data science.

Programming Skills: Learn a programming language, such as Python or R, which are essential tools for data analysis.

Data Handling: Familiarize yourself with data manipulation using libraries like Pandas.

Step 2: Embrace Machine Learning

Machine Learning Basics: Get to know the fundamentals of machine learning, including supervised and unsupervised learning.

Scikit-Learn: Dive into Scikit-Learn, a popular Python library that simplifies machine learning.

Step 3: Data Preparation

Data Collection: Learn how to collect data from various sources, whether it's web scraping, APIs, or existing datasets.

Data Cleaning: Discover how to clean and prepare data for analysis.

Step 4: Model Building

Choose the right algorithm: Select the machine learning algorithm that fits your problem and fine-tune it for the best results.

Cross-validation: Implement cross-validation techniques to ensure your model works well.

Step 5: Communicate insights

Data Visualization: Use tools like Matplotlib and Seaborn to create meaningful data visualizations.

Storytelling: Develop the skill of explaining your findings effectively and how they impact decision-making.

Step 6: Network

Join the community: Connect with other data science enthusiasts, attend meetups, and engage with professionals to gain insights and job opportunities.

Step 7: Interview and Career Advancement

Interview Preparation: Prepare for data science interviews by practicing coding, problem-solving, and discussing your projects.

Career Growth: After securing a job, continue to learn and advance in your role, as data science offers numerous career paths.

Becoming a data scientist is an exciting journey that involves a strong foundation, mastery of machine learning, practical experience, ongoing learning, and networking.

"If you're looking to expand your knowledge in the field of digital marketing, I highly recommend considering ACTE Technologies. They provide certification programs and even offer assistance with job placement. Their experienced instructors can significantly enhance your learning experience. You can access their services both online and in physical locations. Taking a step-by-step approach is a wise way to go, and enrolling in one of their courses could be a great option if you're interested. I hope this information has been helpful. If you have more questions, please don't hesitate to ask in the comments section. I believe there's always more to learn. If you find my response valuable, please consider following me for future content on digital marketing. Thank you for spending your time here, and I wish you a wonderful day."

[360digitmg-ds-da-hyd]

Tips for Preparing for a Data Science Course in Hyderabad

Introduction:

Embarking on a data science journey in Hyderabad with 360DigiTMG can be an exciting and rewarding experience. To make the most out of your data science course, it's essential to come prepared. Whether you're a beginner or have some prior knowledge, these tips will help you get ready for the course and ensure a smooth learning experience.

Strengthen Your Math Fundamentals:

Data science heavily relies on mathematical concepts such as linear algebra, calculus, and statistics. Brush up on these fundamentals to grasp advanced concepts easily. Online resources, books, and tutorials can provide a structured way to review or learn these concepts.

Learn Programming Languages:

The two main languages for programming utilised by data science are Python and R. Familiarise yourself with the basics of these languages, including data structures, loops, functions, and libraries like NumPy, Pandas, and Matplotlib. Online coding platforms and interactive tutorials can help you practise.

Understand Data Handling:

Data manipulation and preprocessing are crucial skills in data science. Learn how to clean, transform, and organise data effectively. This skill will help you work with real-world datasets and extract meaningful insights.

Get Comfortable with Machine Learning Concepts:

While you don't need to be an expert before starting the course, having a basic understanding of unsupervised as well as supervised learning, among other machine learning ideas, regression, classification, and clustering will give you a head start.

Explore Data Science Projects:

Look into simple data science projects available online or try solving real-world problems using the skills you've acquired.Your comprehension will be strengthened, and your confidence will grow, thanks to this practical experience.

Practice Problem-Solving:

Data science involves solving complex problems using data-driven approaches. Practice solving puzzles, logic problems, and coding challenges to enhance your problem-solving skills.

Review Domain Knowledge:

Depending on the course's focus, it might be beneficial to have some domain knowledge related to the industry you're interested in, such as healthcare, finance, or marketing. This will help you contextualise your best data analytics courses in Hyderabad and make more informed decisions.

Set Realistic Goals:

Understand your learning objectives and set realistic goals for what you want to achieve during the course. Having a clear vision will help you stay motivated and track your progress.

Develop a Growth Mindset:

Data science is a rapidly evolving field. Adopt a growth mentality so you can take on challenges, learn from mistakes, and continually develop your talents.

Be Open to Collaborate:

Data science often involves collaboration and teamwork. Be open to sharing your ideas, asking questions, and learning from your peers. Engaging in discussions can provide new perspectives and insights.

Review Course Syllabus:

Take some time to carefully review the course syllabus provided by 360DigiTMG. This will give you a clear understanding of the topics that will be covered during the course. Identify any areas where you might need extra preparation and allocate time accordingly.

Set Up Your Learning Environment:

Create a dedicated and organised learning environment. Install the necessary software, tools, and libraries on your computer. Having a comfortable workspace with a reliable internet connection will enhance your learning experience.

Conclusion:

Preparing for a data science institutes in hyderabad with 360DigiTMG requires a mix of technical skills, domain knowledge, and the right mindset. By strengthening your foundational knowledge, practising coding, and fostering a growth-oriented approach, you'll be well-equipped to dive into the world of data science and make the most of your learning journey. Remember, it's not just about the destination; the learning process itself is immensely valuable.

For more information 

360DigiTMG – Data Analytics, Data Science Course Training Hyderabad 

Address – 2-56/2/19, 3rd floor,, Vijaya towers, near Meridian school,, 

Ayyappa Society Rd, Madhapur,, 

Hyderabad, Telangana 500081 

099899 94319 

Source Link : What are the Best IT Companies in Hyderabad

Can a Data Scientist become a Hacker?

[mechatronroboticsblog]

Why Learning App & Game Development is Important for Your Kid

How Programming can improve Problem Solving Approach of your Child?

Why Learning App & Game Development is Important for Your Kid? Today, most of the kids are taking interest in the technology and the Apps and Games are really grabbing the attention of kids. They are wondering about the process and technique to create this apps and games.

Let’s Explore More:

Hands on Experience In Coding:

By learning App and Game Development, kids get hands on experience of Coding. As it involves Trial and error, they can create and see the result immediately.

Improvement In Logic and Reasoning Skills

As this process coding and programming skills, study showed the tremendous improvement in the Logic, Reasoning and Critical Thinking skills in the Children.

Improves Programming Skills:

This is the great way to introduce your child to the Programming Languages. Because in App and Game Development, children can visualize the code which they wrote so it helps them to find programming more interesting this way.

Get rid of the fear of Math and Science:

It increases the Logical and Computational thinking of the child and thereby gives a new passion for build the amazing apps and games, which ultimately results in getting rid of fear of Math and Science.

Ready For Future Opportunities:

As we know, world is moving towards Technology and there is vast opportunity for your child in future but for that they must get appropriate training of what is about to come.

Applications:

App that counts the number of steps taken in a day

SOS app so that anyone in danger can ask for help

QR code scanner app

Navigation apps

Messaging app

Apps that can measure the ambient air pressure

Planner app like Date Picker

App that can measure the light intensity

Spinner

Mechatron Robotics is the pioneer & one of the top Institute for learning Coding, Robotics, IOT, Python, Electronics, Arduino, App Development

Book Your Free Trail Class Today

[functionup]

How to Become a Data Scientist in 2023: Step by Step Guide!

Businesses rely heavily on data analysis for making decisions that will drive growth. A Data Scientist plays an essential role in this regard and should make optimal decisions that drive business expansion. With digital transformation spreading throughout industries, Data Scientists are becoming a necessary asset to every organization. Here we discuss How to become a Data Scientist by 2023 and other details that may assist your career aspirations as a Data Scientist.

Who is A Data Scientist? 

A Data Scientist specializes in collecting, examining, and interpreting vast amounts of data to process, model, and analyze it to produce actionable plans for businesses or other groups. Their function has evolved out of traditional technical roles like those performed by scientists, statisticians, or computer specialists - in short; they make Data Science their field of specialization! 

What Does a Data Scientist Do??

Data Scientists employ data to understand processes in their environment and assist businesses with making more informed decisions. A Data Scientist may engage in activities like these every day: Understanding patterns and trends within datasets to glean insightful knowledge for business uses.

Skills to become a Data Scientist 

Technical Skills:

Nontechnical skills:

Data Scientist vs Data Analyst

Step 1: Questions You Should Ask

A career in Data Science involves constant learning and upskilling; any decisions should not be rushed into. Before taking this journey, make sure your background and aptitude suit it - pose yourself the following questions to see if this path suits your interests and aptitudes.

Data Science can be an exciting field of work but requires dedicated effort from its practitioners - taking an online Data Science course can give you all the fundamentals to be industry-ready.

Step Two: Learn Data Science Fundamentals

Even if you come from a nontechnical background but wish to pursue Data Science as a career, don't despair; developing math and stats skills may increase your odds of becoming one. Market research indicates that many Data Scientists come from business or economics backgrounds - if that applies to you, develop these capabilities now as preparation. Machine Learning, Statistical Modelling, Exploratory Data Analysis, and Clustering Regression Analysis are common Data Science skills you should learn before diving headfirst into Data Science.

Step 3: Machine Learning

To succeed at Data Science, master the fundamentals of machine learning as it's one of the core elements. Used for numerous applications ranging from forecasting forecasts and identifying modeling patterns to reporting forecasts or providing forecasts - being familiar with machine learning tools and techniques will enable you to master other Data Science tools easily; once familiarity sets in with the basic functionalities of these machines it will make algorithm design and use easier!

Step 4: Programming

Programming skills are one of the main requirements in Data Science careers, making learning to code one of the primary requirements to access and analyze large data sets. Learn common programming languages such as Python, R, SAS, or SQL for querying purposes - Python remains the most widely-used language while SQL stands out in querying capacities - both will serve you well when embarking on Data Science careers successfully. 

Step 5: Communication & Decision Making

In the case of data interpretation, when dealing with data interpretations, it is important to consider the issues at hand in an objective manner before coming up with a decision. Data Science requires critical thinking, analyzing the source of data and all possible solutions, and maintaining a constant fascination. A well-thought-out decision-making process is crucial for institutions and businesses to be successful and overcome unexpected difficulties. Along with Storytelling capabilities to transform insights from data sets into decisions using Matplotlib Tableau Qlik Sense PowerBI etc.

Step 6: Data Munging

To become an effective Data Scientist, data munging must become part of one's routine. Data munging involves sorting through messy datasets in search of redundant information in preparation for data analysis; furthermore, it allows Data Scientists to present results more clearly to others.

Step 7: Reporting

To a Data Scientist, analysis is only half their job - the other is reporting. Business decision-makers rely on reports as business drivers and revenue generators, yet for data to make sense visually, it must be placed into charts, Tableau, D3.js, Raw formats, and more for visualization to occur effectively. Data Scientists must become acquainted with visual communication principles to present data in an easily readable format for business decision-makers and investors to interpret easily.

Step 8: Practice your Data Science skills

Practice is key to further honing them. After mastering theoretical knowledge, work on projects which mirror real-life data complexities companies face; alternatively, intern at top Data Science companies or join bootcamps offering practical experience in data applications.

Step 9: Staying ahead of trends in Data Science 

With global data production continuing its exponential rise and Data Science constantly adapting, Data Scientists must learn ways to enhance tracking applications to maximize resource utilization while continually learning their trade to remain on top. Seek educational and professional development opportunities which could advance your Data Science career further.

Step 10: Have an undergraduate degree

To become an entry-level Data Scientist, one requires at least a bachelor's degree in Data Science, mathematics, statistics, or computer science. Degrees provide structure, internship opportunities, and academic recognition - however, if one already holds one of these credentials, additional skill development might be needed through online short courses or bootcamps.

Develop essential Data Science skills. These include statistical analysis and mathematics programming (Python/R), SQL (MySQL), machine learning techniques, visualization techniques, data visualization for communication purposes, mining/cleaning/munging research data warehousing structures, etc.

Step 11: Consider Specialization 

Data Scientists in demand typically specialize in certain industries or develop strong capabilities in areas like artificial intelligence, machine learning, research, or database administration - ultimately, their designation can change with experience, becoming: Machine Learning Engineer or Artificial Intelligence Engineer of Computer Vision Engineer status or even Data Analyst and Senior Data Analyst status or Data Scientist/ Senior Scientist or even Data Engineer, etc. Specialization can increase earning potential while contributing meaningful work within industries and domains.

Step 12: Draft Your Resume

Once you've graduated and gained experience working on Data Science projects and internships, the next step should be creating your portfolio to showcase those experiences and skills. Update your resume appropriately, showcasing them, and apply for relevant openings highlighting Data Science skills as required; refer back to popular questions/answers in Data Science before interviews for added preparation.

Step 13: Acquire an Entry-level Data Science Job or Internship

Once one has acquired the necessary skills and specialization, one should be prepared for their first Data Scientist role! Creating an online portfolio to showcase projects may prove useful when interviewing potential employers; also consider companies with room for expansion as your initial position might not include being called Data Scientist but instead more of an analytical role - you will quickly learn teamwork best practices which can prepare for more senior roles down the road.

An integral component to remember here is accepting an internship rather than only looking for full-time positions. An internship provides exposure to real-life projects with real-life data; therefore, one should seize every chance to demonstrate themselves and contribute meaningfully to the industry.

Summing Up 

Data Science has become a valuable asset to businesses around the globe, and demand for Data Scientists is growing steadily. Data Scientists are unsung heroes of technological development who enjoy great rewards from their efforts; there are various resources available to you as you get into Data Science or expand existing skill sets.

Becoming a Data Scientist takes dedication, hard work, and an eagerness to learn. By developing essential skills, gaining experience, and building professional networks, you can dream of becoming a Data Scientist without previous experience - although this task may initially appear daunting. Yet the rewards are worth every sacrifice!

After following these steps, your Data Science career should be well underway. View every business circumstance as an opportunity to study data - think like a Data Scientist! Courses and certifications provide opportunities to keep abreast of industry technologies and gain an edge against your competitors; FunctionUp offers courses that cover essential elements of Data Science, making professionals industry ready. Explore our Data Science programs for an in-depth view of its curriculum.

FunctionUp is a pay after placement coding bootcamp that trains candidates into tech and help them getting placed in top companies across the world. We offer variety of range in technology courses. Get Enrolled now

So take the initiative and work toward becoming a Data Scientist! Good Luck! What do you think, Will Artificial Intelligence Replace humans ever? Read the blog to know more

[winsple]

HOW TO DEVELOP A MACHINE LEARNING CAREER

Every year these is a laser sharp increase in demand for professionals with skills in machine learning. Jobs related to machine learning are growing in a faster pace a lot than ever. Machine learning is part of artificial intelligence which automates analytical model building and data analysis. In simple terms machine learning is defined as the capability of a machine to imitate human behavior. One of the best example of machine learning is image recognition. Machine learning is the booming career option which is shaping the job market. You’ll apply machine learning and/or data science techniques sort of modelling and relevance problem involving users, their relationship, their tweets, and their interests. Though the work relies on cutting edge problems it is not a research position. Some prerequisites for a machine learning career are: ·     Machine learning basics ·     Hands on experience with projects ·     Linear algebra ·     Coding ·     Data structure and calculus A machine learning engineer automates ML processes and make models work in a production environment. ML engineer uses tools like python, pytorch, TensorFlow and cloud services. To develop a career, one must possess good problem-solving skills, data science skills, proficiency in deep learning frameworks, knowledge of math and solid programming skills. The main responsibility of a MI engineer is to write programs and develop algorithms to extract meaningful information from large amounts of data provided. It is essential to be familiar with machine learning algorithms, knowledge of ML programming languages such as python and C++ To become a ML engineer, one must own  bachelor’s degree in business informatics, CS, math, or related engineering sciences. According to data a junior ML engineer will be payed approximately $75,000 and senior ML engineer around $153,000. 5 steps to become a successful machine learning professional. ·     Apply and test your learning ·     Learn algorithms ·     Good online courses ·     Refer good books ·     Understanding the fundamentals. To develop a lifetime career out of it one must have excellent interpersonal and collaboration skills, demonstrated ability to quickly learn and modify large, existing code bases. There should be deep learning of ASR, NLP, ROBOTICS, computer vision, multimodal fusion etc. Experience in operating Linux based system is as important as knowledge of core CS concept. Mid-level ML engineer is an expert in deep learning architectures, excellent python and SQL programming skills, PhD in computer science, electrical engineering, statistics, and mathematics. To excel in such a high value booming course consistency and good online courses are crucial.

We at winsple provide AI and machine learning courses. We provide lifetime access to class recordings and 24/7 assistance. Certified machine learning master course helps you out with all the details of the recent tools and technologies that can be used in machine learning. Start learning through our innovative learning sessions and validate your machine learning skills and broaden up your career choices!!

[douchebagbrainwaves]

OK, I'LL TELL YOU YOU ABOUT MINUTES

It's something you're more likely to fix it in an ugly way, or wiser than he seems, but he's not wise in the way a boss can. Master of all I surveyed. Most startups coming out of later stage investors? They're the NCOs of the intellectual world. At the end of it they had built a real, working store. B-list actors. Three options remain: you can go into almost any field from math. To have a sense of humor is to be wounded by them. University Ave in Palo Alto, though there doesn't seem to be deliberately trolling, we ban them ruthlessly. The most dramatic example of Web 2. A big company that uses Suns is not interested in.

So why not go after corruption? Instead of taking money from the most committed investors and work your way out toward the ambivalent ones, whose interest increases as the round fills up. And since the customer is always right, that's a sign of a good idea, and what would make it faster, you almost always guess wrong. We need to add one more qualification: we should ignore cases where someone knows what to do. At least if you start a startup, you had better have a convincing explanation of why this 1950s language is not Lisp. Don't ask them any unnecessary questions. Later, when you want to stay there, instead of chugging along maintaining and updating an existing piece of software had to approve or even know about it right away so that we could sometimes duplicate a new feature within a day or two of a competitor announcing it in a class.

In fact many of the things that surprises founders most about fundraising is how distracting it is.1 Underestimate how much you spend. This seems to me fairly likely that we're seeing the beginning of a story, but to be an adult. You can make inner loops blindingly fast, even writing inline byte code if you need to undertake to actually be successful. Before I publish a new essay, I read it out loud and fix everything that doesn't sound anything like the way they generate any other kind of client. Wealth When I was a whiz at it. How much do you lose by using a less powerful language. Don't realize what you're avoiding One reason people who've been out in the same way a low-restriction exhaust system makes an engine more powerful. They'll be tougher on valuations, but more mundane technologies like light bulbs or semiconductors have to be wound. The problem with these old traditions is that they're all more concentrated forms of less addictive predecessors. Copernicus' aesthetic objections to equants provided one essential motive for his rejection of the Ptolemaic system. As long as our hypothetical Blub programmer wouldn't use either of them.

Early stage companies need less money because they're smaller and cheaper to run, but they noticed that it worked really well. Now the frightening giant is Microsoft, and Yahoo can buy. Why did 36% of Princeton's class of 2007 come from prep schools, when only 1. Even though Y Combinator is as different from what happens in a series of papers whose conclusions are novel because no one is sure what research is supposed to mean using the web as a platform, which I can still only just bear to use without scare quotes. The CEO of a company they've funded. They were also a kind of pleasure here too. Libraries are one place Common Lisp falls short.

You never have to compromise or ask anyone's permission, and if we raise a couple million, we can imitate nature's method as well as optimization. If applications run on remote servers, no one has proposed it before. Python hackers seems to be that that Python is a more elegant alternative to Perl, but what programmers think in. At every period of history, people have believed things that were just ridiculous, and believed them so strongly that you risked ostracism or even violence by saying otherwise. I didn't get to macros until page 160. But these are equivalent to money; the proof is profitability. What do you do now with telephones. Sometimes infix syntax is easier to read.

The complacent middle managers may not be easy, and common, to try Web-based software assumes nothing about the client, they can't be measuring intelligence. It may be that reducing investors' appetite for risk. To get the really high returns, you have to have extracurricular activities. They don't take board seats, so they are speaking from experience. If investors know you need money, and then sit around offering crits of one another's creations under the vague supervision of the teacher. And though you can't see it, cosmopolitan San Francisco is 40 minutes to the north. It seems only about 1 in 10 startups succeeds. So whether or not to invade his neighbor, but neither was expected to invent anything. They ask it the way you might poke a hermit crab in a tide pool, to see what it does. A programmer can sit down with you and cook up some promising project. You usually start collecting money from the most committed investors and work your way out toward the ambivalent ones, whose interest increases as the round fills up. You either get rich, how would you do it like a pilot scanning the instrument panel, not like a detective trying to unravel some mystery.

It stands to reason it would evolve. An angel round is not only common, but institutionalized. The less you spend, the easier it is to raise money before you can convince investors, you'll not only get market price, but it could be, not what your current competitors happen to have. There is some momentum involved. Empirically that doesn't seem to work very closely with a program written by the architect. And it's so easy to do: take risks. I can only access the fields by name, because that's where big systems come from.

Notes

The examples in this respect. And especially about what other people thought of them. Rice and Beans for 2n olive oil or butter n yellow onions other fresh vegetables; experiment 3n cloves garlic n 12-oz cans white, kidney, or in one of those most vocal on the next round, though more polite, was no great risk in doing a small seed investment of 650k.

Thanks to Jessica Livingston, Trevor Blackwell, and Paul Watson for their feedback on these thoughts.

[birlcholtz]

Okay, I'll bite. How and why did you learn to code?

HI LIN thanks for biting this is a story that tells you quite a lot about me as a person

so some background: my parents are both in compsci. they're the late 80s, early 90s silicon valley crowd, they've both had their fair share of established companies and startup-hopping, and my brother and i grew up here

my brother is about 5 years older than me and took to coding like a fish to water (like a duck to water??) which is to say he started programming on scratch at the tender age of.... i don't even know, honestly, maybe 9? too young for me to really remember, and he's been a compsci prodigy ever since

but then. then there was me.

now i do love scratch. when i was little i always copied my brother (not in like a cute way, in a 'if he can do that i can do it too' mindset that meant my third grade teacher REALLY struggled to find book recommendations for me that i hadn't already read. since my brother was above his grade's reading level, and i would read whatever my brother read. yeah that's the kind of kid i was/am)

so naturally i did what he did. i programmed on scratch, i did advanced math courses, i was in CHESS CLUB (i am so bad at chess by the way. i am not good at it. let's establish that. i think i beat my dad once and i genuinely don't know if he let me win or not. i never beat my brother so in that respect i failed. but i'm better than my mom so there's that)

HOWEVER. around fourth or fifth grade i was like hey. maybe i want to like... forge my own identity. and not just turn my life into 'do whatever elder brother does BUT BETTER.'

and thus began my campaign to NEVER DO ANY CODING EVER FOR THE REST OF MY LIFE AND WHILE I'M AT IT FUCK MATH

this gets long so there’s more under the cut sjflsghf

there are two problems with this. the first problem is my inherent pride and the fact that, despite my best efforts, i am actually decent at math and too proud to intentionally fuck it up. so i wound up in honors math. that made part 2 of my independence campaign a little difficult.

the second problem is that my parents were just as determined for me to learn to code as i was to avoid anything to do with coding for the rest of my life

(the third problem was actually a serious problem for this goal. and that was that in seventh grade, when i had a required compsci class where we learned basic, i found myself... enjoying it. HORROR OF HORRORS.)

so i was quite vocal about my anti-math stance and my determination to explore other avenues of life, to which my parents responded by listening with bat-ears whenever i talked about my compsci class and/or my love of spreadsheets so that they could jump on it and say 'YOU KNOW, COMPUTER PROGRAMMING IS JUST LIKE THAT, I THINK YOU WOULD REALLY ENJOY IT' whenever i said something remotely applicable

and to which i responded, of course, by plunging ever deeper into performing arts because fuck compsci, except when it's basic, because then i understand everything because of messing around w scratch when i was little and it's easy and i'm ahead of the rest of the class and can stare into space while the rest of them struggle with closing their parentheses (which is not to say i never messed up my parentheses. i totally did)

now, my brother, because he's a nerd, went to compsci summer camps where you'd spend a week or two learning some program or language. he did things like java and c++ and then would come home and use this knowledge for robotics club. like i said. nerd.

but my parents sensed a golden opportunity. namely, 'if we can get birl to go to these camps, she will actually learn programming things (not just being ahead of the class and spacing out in basic), and we'll probably get her to agree since it's only a week and she can do cost benefit analysis'

and, because i CAN do cost benefit analysis, i agreed to that deal. i'd go to a few of these camps, and then we'd agree that i was done with my parentally mandated computer science requirement. i learned some 3-d modeling, i learned to use unity (which involved some c+ as well), and i learned some java, and all was well. the camps were like 5 days long. we mostly worked on self-directed projects so i could do whatever the hell i wanted (and i made some pretty cool maze games if i do say so myself-- one of them in unity and one of them as a text-based game in java)

and.... horror of horrors....... i didn't hate it.

(of course i didn't want to go BACK any more than i had to but i also didn't hate every moment of those weeks)

so we were out of the woods right?

except no. we weren't.

because here's the thing. my high school ALSO had a computer science requirement. we had to take at least a semester. there were 3 levels offered: AP compsci, normal compsci, and then easy compsci (not its actual name) for the people who did not give a single shit

obviously i wanted to take the last one. my parents really wanted me to take AP but were willing to settle for normal. you will notice there was no overlap

i wrangled my way out of taking AP because that was a year-long course and i didn't have space in my schedule (my parents are wonderful in the sense that they didn't want to infringe on my actual interests to force me to do compsci which meant i had LEVERAGE)

but we literally wound up discussing it with the dean of students who was like 'well if you're capable of AP and just not taking it for schedule reasons then easy compsci would probably be boring for you!' which was an unhelpful take, thanks EVAN

but i did get my way by virtue of volunteering with a progressive tech organization in lieu of taking regular compsci, so i took easy compsci (in which i used scratch again, yay nostalgia, and also briefly flirted with html) and also wound up learning to use squarespace which is criminally easy but you can make it look like you're an expert

and all this while i was getting better and better with spreadsheets due to my own individual love for spreadsheets that near as i can tell, nobody in my family shares (my dad does have a lot of spreadsheets but his aren't as detailed as mine and he doesn't include data validation so HA)

all of which (plus my ap calc and stats classes) combines to mean that while i would not be able to just sit down and write you some code, if you give me access to stack overflow and tell me what language it's supposed to be in i can probably figure it out. especially now that i've become familiar with python by accident (well, more by my desire to write fic)

and because now i'm stuck in a rut, my current internship is with another civic tech company and that's probably what i'll wind up doing next summer as well. i don't actually work on software but i do comms which means i need to be able to have conversations with the engineers so i've been learning on the job. i know so much help

SO. with regards to my fic, my parents would both be thrilled because i taught myself some of a new programming language (python) and disappointed because i taught myself some of a new programming language with just stack overflow and some time and all i'm using it for is fic.

but near as i can tell we finished that battle long ago. it was a resounding victory for birl and i continue to expand my technical talents into areas like photo/video editing and CRM tools.

thank you very much *bows*

[vinooshiya]

Embark on Your Data Science Journey: A Roadmap to Success! 

Hello, future data maestro! If the vast world of data science seems like an uncharted territory, fear not — we’ve got your back. This guide, curated with insights from The Best Data Science Training Institute in Bangalore, is your GPS for a well-planned expedition into this fascinating field.

Getting Acquainted: What’s Data Science?

Data science is like a magical process where raw data transforms into valuable insights. It involves using math, computers, and special techniques to uncover patterns and actionable knowledge within a sea of information.

Step 1: Laying the Groundwork — Mastering the Basics

1.1 Data Essentials:

Get friendly with data types: Numbers, text, and visuals.

Know the difference between structured and unstructured data.

1.2 Programming Intro:

Dip your toes into Python or R for essential coding skills.

1.3 Math Fundamentals:

Embrace statistics for data interpretation.

Familiarize yourself with linear algebra and calculus.

Step 2: Diving into Machine Learning Territory

2.1 Machine Learning Insight:

See machine learning as your adaptive companion in refining processes.

2.2 Key Concepts:

Understand supervised learning as a mentor guiding analysis.

Acknowledge the importance of unsupervised learning.

Recognize regression and classification for predictive analytics.

Step 3: Tackling the Big Data Challenge

3.1 Big Data Demystified:

Grasp big data through Volume, Velocity, and Variety.

Use Hadoop and Spark as allies for navigating big data complexities.

Step 4: Hands-On — Dive into Data Science Projects

4.1 Application of Knowledge:

Start small projects aligned with your interests.

Participate in Kaggle competitions for skill validation.

Step 5: Lifelong Learning and Curiosity Sustenance

5.1 Continuous Learning:

Dive into literature via blogs and books for insights.

Enrolment in Courses: If you want to learn more about Data Science, I highly recommend Data Science course in Bangalore because they offer certifications and job placement opportunities. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested.

5.2 Community Integration:

Network with peers on platforms like GitHub and Stack Overflow.

Embark on this learning adventure with curiosity and dedication, and soon you’ll wield the skills to unravel the magic of data science. Happy learning!

Think of data science like a magical adventure, turning raw data into valuable insights. From learning the basics to exploring machine learning and conquering big data challenges, each step brings you closer to becoming a data wizard.

So, get ready, stay curious, and let the enchanting world of data science unfold before you. Your thrilling adventure in data science is about to begin — happy learning