Author Archives: sayed

About sayed

Sayed is a Backend Python Programmer at sayed.xyz  with 1+ years of experience in tech. He is passionate about helping people become better coders and climbing the ranks in their careers, as well as his own, through continued learning of leadership techniques and software best practices.

How can you be rich by being a programmer?

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Becoming rich as a programmer is certainly possible, but it typically requires a combination of factors, including skills, strategy, and some luck. Here are steps you can take to increase your chances of achieving financial success as a programmer:

  1. Master Programming Skills: Start by becoming an expert in one or more programming languages. Continuously improve your coding skills, problem-solving abilities, and knowledge of software development practices.
  2. Specialize: Consider specializing in a particular area of programming, such as web development, mobile app development, data science, machine learning, or cybersecurity. Specialization can lead to higher-paying opportunities.
  3. Education and Training: Invest in your education and training. A bachelor’s degree in computer science or a related field can open doors to higher-paying jobs, but it’s not always necessary. You can also learn through online courses, coding bootcamps, and self-study.
  4. Build a Strong Portfolio: Create a portfolio of projects that demonstrate your skills and expertise. Open-source contributions, personal projects, and freelance work can help build a compelling portfolio.
  5. Networking: Connect with other professionals in the industry. Attend conferences, meetups, and online forums to build a network of contacts that can lead to job opportunities or collaborations.
  6. Stay Current: The tech industry evolves rapidly. Stay up-to-date with the latest programming languages, frameworks, and industry trends to remain competitive.
  7. Work for Top Companies: Consider working for well-established tech companies or startups with the potential for rapid growth. These companies often offer competitive salaries and stock options.
  8. Freelancing and Consulting: Freelance or offer consulting services to diversify your income streams. Freelancers can often charge higher hourly rates than salaried employees.
  9. Entrepreneurship: Consider starting your own tech-related business or developing software products. Successful tech startups can lead to significant wealth if they solve a valuable problem and gain traction.
  10. Invest Wisely: As you earn more, be mindful of how you manage your finances. Invest wisely in stocks, real estate, or other opportunities to grow your wealth over time.
  11. Negotiate Salaries and Contracts: When changing jobs or taking on freelance projects, negotiate for competitive compensation packages. Don’t undervalue your skills and experience.
  12. Financial Discipline: Practice good financial habits, such as budgeting and saving. Avoid excessive debt and manage your expenses carefully.
  13. Seek Mentorship: Find a mentor who can provide guidance on career development, entrepreneurship, or financial planning.
  14. Patience and Persistence: Building wealth as a programmer often takes time. Be patient and persistent in your pursuit of opportunities and financial goals.
  15. Monitor Tax Efficiency: Be aware of tax strategies that can help you maximize your income and reduce tax liabilities.

It’s important to remember that becoming rich is not guaranteed, and financial success depends on various factors, including the demand for your skills, the economic environment, and personal choices. While programming can offer high earning potential, it’s essential to find a balance between financial goals and personal fulfillment in your career. Building wealth often requires discipline, continuous learning, and a long-term perspective.

Why meritorious person wants to become a programmer?

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A meritorious person may choose to become a programmer for various reasons, just like anyone else. Meritocracy, which rewards individuals based on their abilities and achievements, doesn’t necessarily dictate a specific career path like programming. However, there are several reasons why a meritorious individual might be drawn to a career in programming:

  1. Intellectual Challenge: Programming can be intellectually stimulating and challenging. Meritorious individuals who enjoy solving complex problems and working with logic may find programming to be a rewarding pursuit.
  2. Creativity: Programming allows for creative expression, as programmers design and build software, applications, and systems from scratch. This creative aspect can be appealing to those with meritorious qualities.
  3. Problem Solving: Meritorious individuals often excel at problem-solving, a crucial skill in programming. They may enjoy the process of identifying issues, developing solutions, and optimizing code for efficiency.
  4. High Demand: Programming skills are in high demand in today’s digital world. Meritorious individuals may see this as an opportunity to contribute to society by addressing important technological challenges.
  5. Financial Rewards: A career in programming can be financially rewarding, and meritorious individuals may be motivated by the prospect of earning a good income, especially if they have the skills and qualifications to excel in this field.
  6. Flexibility and Remote Work: Programming offers flexibility in terms of work location and hours, which may be attractive to meritorious individuals who value work-life balance.
  7. Continuous Learning: The tech industry is ever-evolving, requiring individuals to continuously learn and adapt. Meritorious individuals who enjoy learning and staying up-to-date with the latest technologies may thrive in this field.
  8. Entrepreneurship Opportunities: Programming skills can be a valuable asset for starting tech-related businesses, and meritorious individuals may see entrepreneurship as a path to make a significant impact.
  9. Contribution to Society: Programming can be a means to contribute positively to society, whether by developing software for healthcare, education, environmental monitoring, or other socially impactful areas.

It’s important to note that the motivation for becoming a programmer can vary greatly among individuals, regardless of their level of merit. People choose careers for a multitude of reasons, and personal interests, passions, and values often play a significant role in career decisions.

Here’s more detailed information about why a meritorious person might choose to become a programmer:

  1. Problem-Solving Skills: Meritorious individuals often possess strong problem-solving abilities, which are highly relevant in programming. They enjoy the process of breaking down complex issues into smaller, manageable parts and developing elegant solutions through coding.
  2. Analytical Thinking: Programmers need to think analytically to understand requirements, identify patterns, and optimize code. Meritorious individuals tend to excel in analytical thinking, making programming a natural fit for them.
  3. Attention to Detail: Programming requires precision and attention to detail to avoid errors in code. Meritorious individuals are often meticulous and can excel in ensuring their code is error-free.
  4. Continuous Learning: A meritorious person may be more inclined to embrace continuous learning and self-improvement. In the rapidly evolving field of technology, staying up-to-date with new programming languages, frameworks, and best practices is crucial for success.
  5. Innovation: Highly meritorious individuals often possess a drive for innovation and creativity. They may be drawn to programming because it allows them to create new solutions, applications, or software that can have a significant impact on various industries.
  6. Meritocratic Nature of Tech: The technology industry often places a strong emphasis on meritocracy, where skills and accomplishments are highly valued. Meritorious individuals may see programming as a field where their abilities can be directly translated into career success.
  7. Global Opportunities: Programming is a skill that transcends geographical boundaries. Meritorious individuals may appreciate the global job opportunities and the ability to work remotely or relocate to tech hubs around the world.
  8. Entrepreneurial Ventures: Some meritorious individuals may aspire to start their own tech companies or startups. Programming skills are invaluable for creating and developing innovative products or services in the tech sector.
  9. Social Impact: Many meritorious individuals have a strong sense of social responsibility. Programming can provide a platform to work on projects that address societal issues, such as healthcare applications, environmental monitoring, or educational software.
  10. Financial Rewards: While not the sole motivation, the potential for a lucrative career can be appealing to meritorious individuals. High demand for skilled programmers often leads to competitive salaries and benefits.
  11. Job Security: In a world increasingly reliant on technology, there is a level of job security associated with programming careers. Meritorious individuals may see this as a stable and dependable career choice.
  12. Cross-Disciplinary Opportunities: Programming skills can be applied across various domains, from finance and healthcare to gaming and artificial intelligence. Meritorious individuals may find the versatility of programming attractive.

It’s important to note that while meritorious qualities can be advantageous in a programming career, anyone with dedication, a willingness to learn, and a passion for problem-solving can become a successful programmer. The decision to pursue programming ultimately depends on an individual’s interests, goals, and personal inclinations, regardless of their perceived merit.

Top tech skills to grow for the future-ready job

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  1. AI
  2. AR
  3. Cloud Computing
  4. Data Analytics
  5. Smart Contracts

Let’s discuss the details of the above 5 jobs:

AI skills are becoming increasingly important for future-ready jobs as artificial intelligence continues to play a significant role in various industries. To prepare for a career in AI or to enhance your existing skills, consider the following steps:

  1. Fundamental Knowledge:
    • Start with a solid foundation in mathematics, especially linear algebra, calculus, and statistics.
    • Learn programming languages commonly used in AI, such as Python and R.
  2. Machine Learning and Deep Learning:
    • Gain expertise in machine learning techniques and algorithms.
    • Understand neural networks, deep learning, and popular deep learning frameworks like TensorFlow and PyTorch.
  3. Data Handling and Preprocessing:
    • Learn how to collect, clean, and preprocess data for AI applications.
    • Explore data visualization techniques for data analysis.
  4. Feature Engineering:
    • Understand feature selection and extraction methods to improve model performance.
  5. Model Building:
    • Learn how to design, train, and evaluate machine learning models.
    • Experiment with different model architectures and hyperparameters.
  6. Natural Language Processing (NLP):
    • If relevant to your interests, dive into NLP, which involves working with text data, sentiment analysis, language generation, and chatbots.
  7. Computer Vision:
    • Explore computer vision techniques for image and video analysis, including object detection, image segmentation, and facial recognition.
  8. Reinforcement Learning:
    • Gain knowledge of reinforcement learning algorithms used in areas like autonomous systems and game AI.
  9. AI Ethics and Responsible AI:
    • Understand the ethical implications of AI and learn about responsible AI practices to ensure fairness, transparency, and accountability.
  10. Big Data Technologies:
    • Familiarize yourself with big data tools and platforms such as Apache Hadoop and Spark for handling large datasets.
  11. Cloud Computing:
    • Learn how to leverage cloud platforms like AWS, Azure, or Google Cloud for AI model deployment and scalability.
  12. Collaboration and Communication:
    • Develop strong communication skills to explain AI concepts and findings to non-technical stakeholders.
  13. Continuous Learning:
    • AI is a rapidly evolving field, so stay updated by reading research papers, attending conferences, and enrolling in online courses.
  14. Practical Projects:
    • Apply your skills by working on real-world AI projects. Building a portfolio of projects can be invaluable when seeking AI-related jobs.
  15. Certifications:
    • Consider obtaining certifications in AI and machine learning from reputable organizations, which can boost your credibility.
  16. Networking:
    • Join AI communities, attend meetups, and network with professionals in the field to stay informed about job opportunities and industry trends.
  17. Internships and Job Experience:
    • Seek internships or entry-level positions in AI-related roles to gain practical experience and build your resume.
  18. Soft Skills:
    • Develop problem-solving, critical thinking, and creativity, as these skills are highly valuable in AI-related positions.

By developing a well-rounded skill set and staying updated with the latest AI advancements, you’ll be better prepared for future-ready jobs in this exciting and rapidly evolving field. Remember that adaptability and a willingness to learn are key attributes for a successful AI career.

Augmented Reality (AR) is a rapidly growing field with significant potential for future-ready jobs across various industries. To acquire AR skills for future job opportunities, consider the following steps:

  1. Understand the Basics:
    • Begin by learning the fundamentals of AR technology, including how it works and its applications.
  2. Programming Skills:
    • Gain proficiency in programming languages often used in AR development, such as C#, C++, Unity, or Unreal Engine.
  3. 3D Modeling and Design:
    • Learn how to create 3D models and assets, as AR often involves overlaying digital objects onto the real world.
  4. Computer Vision:
    • Understand computer vision concepts and techniques, which are essential for tracking and recognizing real-world objects in AR applications.
  5. AR Development Platforms:
    • Familiarize yourself with popular AR development platforms like ARKit (iOS), ARCore (Android), and AR Foundation (cross-platform).
  6. Unity or Unreal Engine:
    • Explore Unity or Unreal Engine, as they are widely used for creating interactive AR experiences.
  7. ARKit and ARCore:
    • If you’re interested in mobile AR, delve into ARKit for iOS and ARCore for Android to create AR apps for smartphones and tablets.
  8. Hardware Knowledge:
    • Understand the hardware components and devices used for AR, such as AR glasses, headsets, and smartphones.
  9. User Interface (UI) and User Experience (UX) Design:
    • Develop skills in designing intuitive and user-friendly AR interfaces and experiences.
  10. Spatial Computing:
    • Learn about spatial computing, which is central to creating AR applications that interact with the physical world.
  11. Simulations and Prototyping:
    • Practice creating AR prototypes and simulations to experiment with different AR concepts and ideas.
  12. AR Content Creation Tools:
    • Explore tools for creating AR content, including 3D modeling software like Blender and Maya.
  13. AI and Machine Learning:
    • If relevant to your field, acquire knowledge of AI and machine learning, as they can enhance AR applications through object recognition and tracking.
  14. Cloud Services:
    • Familiarize yourself with cloud services like AWS, Azure, or Google Cloud, which can support AR applications with backend functionality.
  15. Collaboration and Communication:
    • Develop strong communication skills to explain AR concepts and designs to non-technical stakeholders.
  16. Industry-Specific Knowledge:
    • Depending on your career goals, specialize in an industry that leverages AR, such as healthcare, education, gaming, or automotive.
  17. Portfolio Building:
    • Create a portfolio of AR projects that showcase your skills and demonstrate your ability to develop compelling AR experiences.
  18. Networking:
    • Join AR communities, attend AR-related events, and network with professionals in the field to stay updated and discover job opportunities.
  19. Certifications:
    • Consider obtaining relevant AR certifications or taking online courses from reputable organizations to enhance your qualifications.
  20. Stay Informed and Innovate:
    • AR is a dynamic field with continuous advancements. Stay informed about the latest trends, technologies, and innovations to remain competitive in the job market.

AR skills are in demand across various sectors, including entertainment, education, healthcare, and industrial applications. By building a strong foundation and staying current with AR developments, you can position yourself for future-ready job opportunities in this exciting field.

Cloud computing skills are highly relevant and in demand for future-ready jobs, as organizations across various industries continue to adopt cloud technologies for their operations. Here’s how to develop and leverage cloud computing skills for future job opportunities:

  1. Understanding Cloud Fundamentals:
    • Begin by gaining a solid understanding of cloud computing concepts, including the different service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid).
  2. Cloud Service Providers:
    • Familiarize yourself with major cloud service providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others. Each provider has its own ecosystem and offerings.
  3. Hands-on Experience:
    • Practice setting up cloud environments, provisioning resources, and managing cloud infrastructure using the cloud provider’s console or command-line interface.
  4. Virtualization and Containers:
    • Learn about virtualization technologies (e.g., VMware) and containerization platforms (e.g., Docker, Kubernetes) commonly used in cloud environments.
  5. Security and Compliance:
    • Understand cloud security best practices, encryption, identity and access management, and compliance requirements specific to your industry.
  6. Networking:
    • Develop knowledge of cloud networking, including virtual networks, subnets, load balancers, and content delivery networks (CDNs).
  7. Serverless Computing:
    • Explore serverless computing platforms like AWS Lambda or Azure Functions, which allow you to build and deploy applications without managing servers.
  8. Infrastructure as Code (IaC):
    • Learn IaC tools like Terraform and AWS CloudFormation to automate the provisioning and management of cloud resources.
  9. Data Management:
    • Gain expertise in cloud-based databases, data warehousing solutions, and big data technologies like AWS Redshift, Azure SQL Data Warehouse, and Google BigQuery.
  10. Cloud Development:
    • If you’re interested in application development, understand cloud-native development practices and tools, such as AWS Elastic Beanstalk, Azure App Service, or Google App Engine.
  11. Cloud DevOps:
    • Explore DevOps practices tailored to the cloud, including continuous integration and continuous delivery (CI/CD), automated testing, and monitoring.
  12. Cost Management:
    • Learn how to optimize cloud costs by monitoring resource usage, leveraging reserved instances, and implementing cost control strategies.
  13. Migration and Hybrid Cloud:
    • Specialize in cloud migration strategies for moving on-premises workloads to the cloud and managing hybrid cloud environments.
  14. Certifications:
    • Consider earning certifications from cloud service providers, as they can validate your expertise and enhance your job prospects.
  15. Soft Skills:
    • Develop skills in communication, collaboration, problem-solving, and adaptability, as these are crucial in cloud-related roles.
  16. Industry Focus:
    • Depending on your career goals, specialize in an industry that relies heavily on cloud computing, such as fintech, healthcare, e-commerce, or gaming.
  17. Continuous Learning:
    • Stay updated with the latest cloud technologies and trends by attending webinars, conferences, and online courses.

Cloud computing skills are versatile and can open doors to a wide range of job opportunities, including cloud architects, cloud engineers, cloud administrators, and cloud developers. As organizations increasingly move their operations to the cloud, professionals with cloud expertise will be in high demand for future-ready jobs.

Data analytics skills are highly valuable for future-ready jobs, as businesses and organizations increasingly rely on data to make informed decisions and gain a competitive edge. Here’s how you can develop and leverage data analytics skills for future job opportunities:

  1. Data Fundamentals:
    • Start by building a strong foundation in data fundamentals, including data types, data sources, and data structures.
  2. Data Analysis Tools:
    • Learn how to use popular data analytics tools and languages such as Python, R, SQL, and data visualization libraries like Matplotlib and Seaborn.
  3. Statistical Analysis:
    • Gain proficiency in statistical analysis, hypothesis testing, regression analysis, and other statistical techniques commonly used in data analytics.
  4. Data Wrangling:
    • Master data cleaning, transformation, and preparation techniques to ensure that data is accurate and suitable for analysis.
  5. Data Visualization:
    • Develop skills in creating compelling data visualizations using tools like Tableau, Power BI, or data visualization libraries in Python and R.
  6. Machine Learning:
    • Familiarize yourself with machine learning concepts and algorithms to analyze data, build predictive models, and automate decision-making processes.
  7. Big Data Technologies:
    • Learn about big data technologies like Hadoop, Spark, and distributed computing frameworks to work with large datasets efficiently.
  8. Database Management:
    • Acquire knowledge of database systems, both relational and NoSQL, to store and retrieve data effectively.
  9. Business Intelligence (BI):
    • Understand BI platforms and practices for creating dashboards and reports that provide actionable insights to business stakeholders.
  10. Data Ethics and Privacy:
    • Be aware of data ethics, privacy regulations, and best practices for handling sensitive data responsibly.
  11. Domain Expertise:
    • Specialize in a particular industry or domain (e.g., healthcare, finance, marketing) to develop domain-specific knowledge and analytics skills.
  12. Data Governance:
    • Learn about data governance principles and practices to ensure data quality, security, and compliance.
  13. Data Storytelling:
    • Hone your ability to communicate data findings effectively through storytelling, making data-driven insights accessible to non-technical stakeholders.
  14. Cloud-Based Analytics:
    • Familiarize yourself with cloud-based analytics platforms like Google Analytics, AWS QuickSight, or Azure Analytics services.
  15. Coding and Scripting:
    • Develop coding and scripting skills to automate data analysis tasks and build custom analytics solutions.
  16. Certifications:
    • Consider earning certifications in data analytics or related fields, such as data science or business intelligence, to demonstrate your expertise.
  17. Continuous Learning:
    • Stay up-to-date with the latest trends and technologies in data analytics by reading books, attending webinars, and participating in online courses and communities.
  18. Problem-Solving Skills:
    • Cultivate problem-solving skills to tackle complex data challenges and provide actionable insights.

Data analytics skills are in high demand across various industries, including finance, healthcare, retail, and technology. As organizations continue to accumulate vast amounts of data, professionals who can extract meaningful insights from this data will be essential for making informed decisions and driving business success in the future.

Smart contracts skills are becoming increasingly valuable for future-ready jobs, especially in the blockchain and cryptocurrency industries. Here’s how to develop and leverage smart contract skills for future job opportunities:

  1. Understanding Smart Contracts:
    • Start by gaining a solid understanding of what smart contracts are and how they work. Smart contracts are self-executing contracts with the terms of the agreement directly written into code.
  2. Blockchain Technology:
    • Learn the fundamentals of blockchain technology, which underlies smart contracts. Understand the concepts of decentralization, distributed ledgers, and consensus mechanisms.
  3. Ethereum:
    • Ethereum is one of the most popular platforms for creating smart contracts. Learn how to develop smart contracts using Ethereum’s programming languages like Solidity.
  4. Programming Skills:
    • Develop strong programming skills, as most smart contract development involves writing code. Knowledge of languages like Solidity, Vyper, or Rust is crucial.
  5. Blockchain Platforms:
    • Familiarize yourself with other blockchain platforms that support smart contracts, such as Binance Smart Chain, Cardano, Polkadot, and more.
  6. Development Tools:
    • Explore development tools and environments tailored for smart contract development, such as Remix, Truffle, and Hardhat.
  7. Security Best Practices:
    • Understand security best practices for smart contracts to prevent vulnerabilities and exploits. Audit your code thoroughly or seek professional audits.
  8. Testing and Debugging:
    • Learn how to test and debug smart contracts effectively to ensure their functionality and security.
  9. Decentralized Applications (DApps):
    • Consider expanding your skills to include the development of decentralized applications (DApps) that interact with smart contracts.
  10. Token Standards:
    • If you’re interested in creating tokens on blockchain platforms, understand token standards like ERC-20, ERC-721, and others for Ethereum-based tokens.
  11. Blockchain Governance:
    • Gain knowledge of blockchain governance mechanisms, as they are essential for proposing and implementing changes to blockchain protocols and smart contracts.
  12. Use Cases:
    • Explore various use cases for smart contracts beyond cryptocurrency, such as supply chain management, voting systems, legal agreements, and more.
  13. Legal and Regulatory Understanding:
    • Stay informed about the legal and regulatory aspects of smart contracts, which may vary by jurisdiction.
  14. Networking:
    • Join blockchain and smart contract communities, attend conferences, and network with professionals in the field to stay updated and discover job opportunities.
  15. Certifications:
    • Consider obtaining certifications related to blockchain and smart contract development to validate your expertise.
  16. Problem-Solving Skills:
    • Cultivate problem-solving skills, as identifying and addressing issues in smart contract code can be challenging.
  17. Continuous Learning:
    • The blockchain and smart contract space is continually evolving. Keep up-to-date with the latest developments and innovations.

Smart contract skills are in demand across a range of industries, including finance, supply chain, healthcare, and more. As blockchain technology continues to gain traction, professionals who can develop secure and efficient smart contracts will be well-positioned for future job opportunities in this exciting and evolving field.

In sum, we can say

Certainly, the tech skills I mentioned—AI, AR, Cloud Computing, Data Analytics, and Smart Contracts—are all crucial for future-ready jobs. Here’s a summary of the top tech skills to grow for future job opportunities:

  1. Artificial Intelligence (AI):
    • AI skills encompass machine learning, deep learning, natural language processing, and computer vision.
    • Learn programming languages like Python and specialized AI libraries and frameworks.
    • Understand data preprocessing, feature engineering, and model deployment.
    • Gain knowledge of AI ethics, responsible AI practices, and AI deployment on the cloud.
  2. Augmented Reality (AR):
    • Develop proficiency in AR development platforms and tools.
    • Learn 3D modeling, computer vision, and spatial computing.
    • Understand the integration of AR with hardware like AR glasses or smartphones.
    • Explore AR in various industries, from gaming to education and healthcare.
  3. Cloud Computing:
    • Familiarize yourself with major cloud service providers like AWS, Azure, and Google Cloud.
    • Learn cloud infrastructure setup, management, and optimization.
    • Understand serverless computing, containers, and infrastructure as code (IaC).
    • Explore cloud security, networking, and cost management.
  4. Data Analytics:
    • Master data analysis using programming languages like Python, R, and SQL.
    • Gain expertise in data visualization and storytelling.
    • Develop statistical and machine learning skills for predictive analytics.
    • Understand big data technologies and database management.
  5. Smart Contracts and Blockchain:
    • Learn blockchain fundamentals and how distributed ledger technology works.
    • Gain proficiency in smart contract development, particularly on platforms like Ethereum.
    • Understand blockchain security, governance, and token standards.
    • Explore real-world use cases beyond cryptocurrencies, such as supply chain or voting systems.

These skills are highly relevant across various industries, including tech, finance, healthcare, e-commerce, and more. However, it’s essential to note that future-ready jobs may also require a combination of these skills, as technology continues to evolve and become more interconnected. Additionally, soft skills such as problem-solving, critical thinking, adaptability, and effective communication are valuable complements to technical expertise for thriving in future roles. Continual learning and staying updated with emerging technologies will also be essential to remain competitive in the job market.

Why you should use coding best practices

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As a coder, developing a coding style that’s mindful of these best practices will make it easier to extend and debug the code in the future. Even if you’re no longer working with that codebase, the legacy of your work will affect the community of developers who work with that code in the future.

Best Programming Practices for Beginner Programmers follow the current industry methods 

Code indentation

Meaningful naming

Comments that add context

Don’t repeat yourself

Low coupling and high cohesion

Consider your context

Wrapping up and next steps

Find A Stronger Editor

Write Human-Friendly Code

Planning Before Coding

Write Manageable Code

Stop Overdoing Features

Do Version Control

Always Learn Something New

Optimize your code for efficiency through arrays, loops, etc.

Test and debug your code as you build

Educate and expand your coding skill set

How to Make Programming a Daily Habit?

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Any good habit makes that task easy, and successful and no difficult task seems difficult, it can be done easily. Likewise, I was thinking about how to make the things we do daily as programmers into a habit.

So today I came up with the idea of how to make my programming career, programming into a habit, a habit. Making programming a daily habit requires dedication and consistency. 

Here are some steps to help you establish and maintain a daily programming routine:

Set Clear Goals:

Define why you want to make programming a daily habit. Are you learning to code, working on a personal project, or aiming to improve your skills? Having clear goals will motivate you.

Start Small:

If you’re new to programming or trying to build a habit, start with a manageable amount of time each day. Even dedicating just 15-30 minutes initially can be effective.

Create a Schedule:

Allocate a specific time each day for programming. Consistency is key, so choose a time that works best for you and stick to it.

Remove Distractions:

Find a quiet and comfortable workspace where you can focus without interruptions. Turn off notifications on your devices to minimize distractions.

Set Daily Targets:

Set achievable daily programming goals. These could be as simple as completing a coding exercise, reading a chapter in a programming book, or working on a small project feature.

Use a To-Do List:

Maintain a to-do list or a task tracker to keep track of your programming goals. Checking off completed tasks can be motivating.

Learn Incrementally:

If you’re learning a new programming language or technology, break it down into smaller chunks. Focus on mastering one concept or skill at a time.

Stay Accountable:

Share your daily programming commitment with a friend, family member, or an online community. This can provide external motivation and accountability.

Seek Inspiration:

Read about successful programmers, projects, or technologies that inspire you. Learning about the achievements of others can fuel your motivation.

Variety and Fun:

Keep things interesting by working on diverse projects or experimenting with different programming languages and technologies. Fun and variety can prevent burnout.

Learn from Others:

Engage with the programming community, either through online forums, social media, or local meetups. Sharing your experiences and learning from others can be highly motivating.

Review and Reflect:

At the end of each day, review what you’ve accomplished and reflect on what you’ve learned. This can help reinforce your progress and identify areas for improvement.

Adapt and Adjust:

Be flexible and willing to adapt your schedule and goals as needed. Life can be unpredictable, and it’s okay to make adjustments to maintain your daily programming habit.

Reward Yourself:

Celebrate your achievements, no matter how small. Rewarding yourself for sticking to your programming habit can provide positive reinforcement.

Don’t Be Too Hard on Yourself:

There may be days when you miss your programming session due to unforeseen circumstances or lack of motivation. It’s okay; just get back on track the next day without self-criticism.

Remember that building a daily programming habit takes time and patience. The key is to make consistent, small efforts, and over time, programming will become a natural part of your daily routine.

In fact, I often think of them and list them at different times, thinking about where to put the lists, if I need to look at them once, I post them in a blog so that I can read them from my blog anytime. It can inspire more and do it through a process right that’s why blog post and share it with you

generative ai

Fundamental types of AI

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I am very much interested in AI to learn and apply as a programmer. So now I want to learn the types of AI. That’s why today I have started to learn the very basics things of AI, some of the fundamental types of AI:

Artificial Intelligence is a broad field with various subfields and types. Here are some of the fundamental types of AI:

  1. Narrow or Weak AI (Artificial Narrow Intelligence – ANI): This type of AI is designed for a specific task or a narrow range of tasks. It operates under a predefined set of rules and does not possess general intelligence or consciousness. Examples include virtual personal assistants like Siri or Alexa, recommendation systems, and chatbots.
  2. General AI (Artificial General Intelligence – AGI): AGI is a theoretical form of AI that possesses human-like intelligence. It can understand, learn, and apply knowledge across a wide range of tasks, just like a human. Achieving AGI is a long-term goal of AI research, and it doesn’t currently exist.
  3. Superintelligent AI: This is a hypothetical AI that surpasses human intelligence in every aspect. It’s often portrayed in science fiction and remains a topic of debate and speculation among AI researchers and ethicists.
  4. Machine Learning (ML): Machine learning is a subset of AI that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions from data. It includes supervised learning, unsupervised learning, and reinforcement learning.
  5. Deep Learning: Deep learning is a subfield of machine learning that uses artificial neural networks inspired by the structure and function of the human brain. It’s particularly effective for tasks involving large datasets and has been used in image and speech recognition, natural language processing, and more.
  6. Reinforcement Learning: This type of learning is inspired by behavioral psychology and focuses on training AI agents to make sequential decisions by rewarding or punishing them based on their actions. It’s commonly used in game playing, robotics, and autonomous systems.
  7. Natural Language Processing (NLP): NLP is a branch of AI that deals with the interaction between computers and human language. It enables machines to understand, interpret, and generate human language, making it crucial for applications like chatbots, language translation, and sentiment analysis.
  8. Computer Vision: Computer vision involves teaching machines to interpret and understand visual information from the world, such as images and videos. Applications include image recognition, object detection, and autonomous vehicles.
  9. Expert Systems: Expert systems are AI programs designed to mimic the decision-making abilities of a human expert in a specific domain. They use rule-based reasoning and knowledge representation to provide solutions to complex problems.
  10. Robotics: AI plays a significant role in robotics, enabling robots to perceive their environment, make decisions, and perform tasks autonomously. This includes industrial robots, drones, and humanoid robots.
  11. Cognitive Computing: Cognitive computing combines AI techniques, natural language processing, and other technologies to simulate human thought processes. IBM’s Watson is a well-known example used in various fields, including healthcare and finance.
  12. Autonomous Systems: These are AI-powered systems that can operate independently without human intervention. Examples include autonomous cars, drones, and smart home devices.

As you delve deeper into AI, you can explore these different types and their respective applications. It’s essential to start with the basics and gradually build your knowledge and skills in the specific areas that interest you the most. There are numerous online courses, tutorials, and books available to help you get started with AI programming and development.

GPT (Generative Pretrained Transformer) is a language model developed by OpenAI, which has taken the world of natural language processing (NLP) by storm.

That’s why a peep has come to mind that Chat GPT is a generative AI type. Actually, generative AI is not a distinct type of AI like narrow AI or deep learning but rather a category of AI techniques and models used to generate data, content, or other outputs. Generative AI systems create new data samples, such as images, text, or even music, based on patterns and information learned from existing data.

Some common examples of generative AI techniques include:

  1. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, that are trained in a competitive manner. The generator tries to create data samples that are indistinguishable from real data, while the discriminator tries to tell the difference between real and generated data. This competition results in the generation of realistic data.
  2. Variational Autoencoders (VAEs): VAEs are a type of neural network architecture used for data generation and dimensionality reduction. They are often employed in generating images or encoding and decoding data in a continuous latent space.
  3. Recurrent Neural Networks (RNNs) and Transformers: These are used in natural language generation tasks. RNNs can generate sequences of text or other data, while Transformers are especially powerful in tasks like language translation and text generation.
  4. Autoregressive Models: These models generate data one element at a time, conditioning each element on the previously generated ones. Examples include autoregressive language models like GPT (Generative Pre-trained Transformer) and autoregressive generative models like PixelCNN for image generation.
  5. Markov Models: These models use the Markov property to generate sequences of data based on probabilistic transitions between states. Hidden Markov Models (HMMs) are an example used in speech recognition and bioinformatics.

Generative AI has a wide range of applications, from generating realistic images and text to creating music and even designing novel molecules for drug discovery. While generative AI techniques are incredibly powerful, they also come with ethical considerations, such as the potential for generating fake content or infringing on copyrights.

In summary, generative AI refers to the subset of AI techniques and models that are used to create new data or content, and it encompasses various methods and approaches within the field of artificial intelligence.

So what is Generative AI?

Generative AI, short for Generative Artificial Intelligence, is a subset of artificial intelligence that focuses on creating machines capable of generating content that is similar to what a human might produce. In other words, it involves AI systems that can generate new data, such as text, images, audio, or even video, often by learning from existing data.

The key characteristic of generative AI is its ability to produce novel content rather than just making decisions or predictions based on existing data. It involves modeling complex patterns and relationships within data to generate new, meaningful, and often creative outputs. Generative AI techniques often rely on deep learning and neural networks to achieve this capability.

Here are some common applications and examples of generative AI:

  1. Text Generation: AI models like GPT (Generative Pre-trained Transformer) can generate coherent and contextually relevant text. They are used in chatbots, content generation, and even creative writing.
  2. Image Generation: Generative Adversarial Networks (GANs) are widely used to create realistic images that may not exist in reality. These can be applied in art, design, and even generating realistic faces of people who don’t exist.
  3. Music Composition: AI systems can generate music compositions based on input data, style, or preferences, which is useful in the music industry for creating background tracks or even entirely new pieces of music.
  4. Data Augmentation: Generative AI can be used to augment datasets by generating additional data points, which is valuable in machine learning for improving model training.
  5. Drug Discovery: In the field of pharmaceuticals, generative AI is used to design and suggest new molecules with potential therapeutic properties.
  6. Game Content: Game developers use generative AI to create game levels, characters, and content dynamically, enhancing gameplay experiences.
  7. Language Translation: Some AI models are capable of generating translations for languages even if they were not explicitly trained on that language pair.

Generative AI has made significant advancements in recent years, thanks to deep learning techniques and the availability of large datasets. It has the potential to automate and enhance creative and content generation tasks across various industries, making it a fascinating and rapidly evolving field within artificial intelligence. However, it also raises ethical concerns, such as the potential for generating misleading or harmful content, which must be carefully considered and addressed.

What type of PC do I need to learn, practise, doing projects in Python?

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To learn, practice, and work on Python projects, you don’t necessarily need a high-end or specialized PC. Python is a versatile programming language that can be run on a wide range of hardware configurations.

The type of PC you need depends on the complexity of your projects and your specific requirements, but here are some general recommendations:

  1. Operating System: Python is compatible with Windows, macOS, and various Linux distributions. You can choose the operating system that you are most comfortable with or the one required by your specific project or job.
  2. Hardware Requirements:
    • Processor: A modern dual-core processor (e.g., Intel Core i3 or AMD Ryzen 3) should be sufficient for most Python programming tasks. For more computationally intensive work, such as machine learning or scientific computing, a faster CPU can be beneficial.
    • RAM: 8GB of RAM is a good starting point for general Python development. If you plan to work on more memory-intensive tasks, like large datasets or complex simulations, consider getting 16GB or more.
    • Storage: A standard SSD (Solid State Drive) is recommended for faster code compilation and shorter loading times. A 256GB SSD or larger should suffice for most development needs.
    • Graphics: Integrated graphics are typically sufficient for Python programming. You may need a dedicated GPU for machine learning or data visualization tasks.
  3. Development Environment:
    • Text Editor or IDE: You can use simple text editors like Notepad++ or more feature-rich Integrated Development Environments (IDEs) like PyCharm, Visual Studio Code, or Jupyter Notebook. These are available on all major operating systems.
  4. Version Control:
    • Consider using a version control system like Git to manage your code and collaborate with others on projects.
  5. Python Installation:
    • Install Python on your PC. You can download the latest version of Python from the official website (https://www.python.org/downloads/). Python 3.x is the most commonly used version for new projects.
  6. Package Management:
    • Familiarize yourself with Python package managers like pip or conda, as you will often use them to install third-party libraries and packages.
  7. Internet Connection:
    • A stable internet connection can be helpful for installing packages, accessing documentation, and collaborating with others.
  8. Optional Extras:
    • If you plan to work on machine learning projects, you might consider a PC with a more powerful CPU and GPU.

Remember that Python is an excellent language for beginners, and you can get started with minimal hardware. As your projects become more complex or you venture into specialized areas like machine learning, you can always upgrade your hardware accordingly. The key is to focus on learning and building your programming skills, and then tailor your hardware and software choices as needed for your specific projects.

What is MSBI

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MSBI stands for Microsoft Business Intelligence. It is a suite of tools and services provided by Microsoft for business intelligence (BI) and data analytics. MSBI is designed to help organizations collect, transform, analyze, and visualize data to make informed business decisions.

The MSBI stack includes several key components:

  1. SQL Server Integration Services (SSIS):
    • SSIS is a data integration and ETL (Extract, Transform, Load) tool. It allows users to extract data from various sources, transform and clean the data, and then load it into a data warehouse or other target systems. SSIS provides a visual design interface for creating data integration workflows.
  2. SQL Server Analysis Services (SSAS):
    • SSAS is an online analytical processing (OLAP) and data modeling tool. It enables users to design multidimensional and tabular data models for efficient data analysis. SSAS supports features like data mining, cube processing, and advanced calculations.
  3. SQL Server Reporting Services (SSRS):
    • SSRS is a reporting and visualization tool that allows users to create and publish interactive reports and dashboards. It offers various data visualization options, parameterized reports, and the ability to schedule and deliver reports to users via email or web access.
  4. Power BI:
    • While Power BI is not part of the traditional MSBI suite, it is often included in discussions of Microsoft’s BI offerings. Power BI is a separate but closely integrated tool for data visualization and analytics. It allows users to create interactive reports and dashboards, connect to various data sources, and share insights with others. Power BI can be used in conjunction with other MSBI components for a comprehensive BI solution.
  5. Azure Analysis Services:
    • Azure Analysis Services is a cloud-based version of SSAS that allows users to create and manage analytical models in the Azure cloud. It offers scalability and flexibility for organizations looking to leverage cloud-based BI solutions.
  6. Azure Data Factory:
    • Azure Data Factory is a cloud-based ETL service provided by Microsoft. While it’s not part of the traditional MSBI stack, it can be used alongside MSBI components to create end-to-end data integration and analytics solutions in the cloud.

MSBI is widely used by organizations to build and maintain their business intelligence solutions. It provides a comprehensive set of tools and services to handle data integration, modeling, reporting, and visualization, making it easier for businesses to derive insights from their data and make data-driven decisions. Additionally, with the integration of Power BI and cloud-based services like Azure Analysis Services, Microsoft has expanded its BI offerings to address modern data analytics needs.

What is Data Warehouse?

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A data warehouse is a centralized repository that stores data from various sources within an organization in a structured and optimized format for efficient querying, reporting, and analysis. It is a core component of business intelligence (BI) and data analytics, serving as a foundation for decision-making processes.

Here are the key characteristics and components of a data warehouse:

1. Data Integration: Data warehouses consolidate data from diverse sources such as transactional databases, external data feeds, spreadsheets, and more. This integration process involves transforming, cleaning, and harmonizing the data to ensure consistency and accuracy.

2. Historical Data: Unlike operational databases that primarily store current transactional data, data warehouses retain historical data over time. This historical perspective is essential for trend analysis, forecasting, and identifying long-term patterns.

3. Structured Schema: Data warehouses typically use a structured schema, often referred to as a star schema or snowflake schema. These schemas organize data into fact tables (containing quantitative measures) and dimension tables (containing descriptive attributes). This structure facilitates efficient query performance.

4. Optimized for Querying: Data warehouses are designed for complex querying and reporting. They often employ indexing, partitioning, and materialized views to accelerate query execution.

5. Data Transformation: Data in a data warehouse is preprocessed and transformed to ensure consistency and quality. ETL (Extract, Transform, Load) processes are commonly used to perform these tasks.

6. Historical Snapshots: Data warehouses may include historical snapshots, allowing analysts to compare data at different points in time. This is valuable for trend analysis and historical reporting.

7. Business Intelligence: Data warehouses serve as the foundation for business intelligence and reporting tools. Users can create ad-hoc queries, generate reports, and perform data analysis to extract insights and make informed decisions.

8. Data Marts: In some cases, organizations create data marts, which are subsets of the data warehouse tailored to specific business units or departments. Data marts provide a more focused view of data for particular needs.

9. Scalability: As data grows, data warehouses can be scaled vertically or horizontally to accommodate increasing storage and processing demands.

10. Security and Access Control: Data warehouses implement robust security measures and access controls to ensure that sensitive data is protected and only accessible to authorized users.

11. Data Governance: Establishing data governance practices is essential in managing the quality, lineage, and compliance of data within a data warehouse.

12. Cloud Data Warehouses: With the advent of cloud computing, many organizations are adopting cloud-based data warehouses like Amazon Redshift, Google BigQuery, and Azure Synapse Analytics, which offer scalability and ease of management.

Data warehouses are instrumental in enabling data-driven decision-making by providing a unified and reliable source of information for analysts, data scientists, and business users. They help organizations gain insights from historical and current data, identify trends, improve operational efficiency, and enhance strategic planning.

Here’s a roadmap to help you become an expert in data warehousing:

Becoming an expert in data warehousing is a rewarding journey that requires a combination of education, hands-on experience, and continuous learning. Data warehousing is a complex field that involves data integration, modeling, ETL (Extract, Transform, Load) processes, and more.

1. Learn the Fundamentals:

  • Start with the basics of data warehousing. Understand what a data warehouse is, its purpose, and its role in business intelligence and analytics.

2. Relational Database Knowledge:

  • Gain a solid understanding of relational databases and SQL. Data warehouses often use SQL-based databases for storage and retrieval of data.

3. ETL Processes:

  • Learn about ETL processes, which involve extracting data from various sources, transforming it into a consistent format, and loading it into the data warehouse. Understand ETL tools like Informatica, Talend, or Microsoft SSIS.

4. Data Modeling:

  • Master data modeling concepts, including star schema, snowflake schema, and fact-dimension modeling. Learn to design efficient and scalable data models.

5. Data Integration:

  • Explore data integration techniques, including data consolidation, data quality, data profiling, and data mapping.

6. SQL and Query Optimization:

  • Develop advanced SQL skills and understand query optimization techniques to ensure efficient data retrieval from the data warehouse.

7. Data Warehouse Architectures:

  • Study various data warehouse architectures, including traditional on-premises data warehouses and cloud-based data warehouses (e.g., Azure Synapse Analytics, Amazon Redshift).

8. ETL Tool Proficiency:

  • Gain proficiency in ETL tools commonly used in the industry. This includes understanding how to design, develop, and maintain ETL workflows.

9. Business Intelligence Tools:

  • Familiarize yourself with business intelligence tools like Power BI, Tableau, or QlikView, as these are often used to create reports and dashboards on top of data warehouses.

10. Data Governance and Security:

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- Learn about data governance practices and security measures in data warehousing, including access control, encryption, and compliance.

11. Master Data Management:

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- Understand master data management (MDM) principles to maintain data quality and consistency across the data warehouse.

12. Cloud Data Warehousing:

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- Explore cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. Cloud data warehouses offer scalability and cost-effectiveness.

13. Continuous Learning:

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- Stay updated with industry trends, new technologies, and best practices through blogs, books, online courses, and webinars.

14. Hands-On Projects:

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- Apply your knowledge by working on real-world data warehousing projects. Building and maintaining data warehouses in practical scenarios will provide invaluable experience.

15. Networking and Collaboration:

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- Engage with the data warehousing community through networking events, conferences, and online forums. Collaborate with professionals who share similar interests.

16. Certifications:

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- Consider earning certifications in data warehousing or related areas. Certifications from vendors like Microsoft, Oracle, and others can validate your expertise.

17. Mentorship:

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- Seek mentorship or guidance from experienced data warehousing professionals. Learning from their experiences can accelerate your growth.

18. Teach and Share:

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- Teaching others and sharing your knowledge through blogs, presentations, or workshops can solidify your expertise and help others in the field.

Remember that expertise in data warehousing is a continuous journey. Technology and best practices evolve, so staying current and adapting to new tools and techniques is crucial for long-term success in the field.

What is Power BI & Learning Roadmap

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Power BI is a business analytics service and data visualization tool developed by Microsoft. It allows users to connect to various data sources, transform and clean data, create interactive reports and dashboards, and share them with others in their organization. Power BI is designed to help individuals and organizations make data-driven decisions by providing insights into their data through visually appealing and interactive reports.

Here are some key features and aspects of Power BI:

  1. Data Connectivity: Power BI can connect to a wide range of data sources, including databases (SQL Server, Oracle, MySQL, etc.), cloud services (Azure, AWS, Salesforce, etc.), Excel spreadsheets, and many others. It also supports real-time data streaming.
  2. Data Transformation: Users can shape and transform their data using Power Query, a data transformation and ETL (Extract, Transform, Load) tool integrated into Power BI. This allows for data cleaning, merging, and transformation operations.
  3. Data Modeling: Power BI provides a data modeling engine that allows users to create relationships between different data tables, define calculated columns, and build measures (aggregated calculations) to perform complex data analysis.
  4. Data Visualization: The core strength of Power BI lies in its data visualization capabilities. Users can create interactive and visually appealing reports and dashboards using a wide variety of charts, graphs, tables, and other visualization elements.
  5. Sharing and Collaboration: Power BI allows users to share their reports and dashboards with others within their organization. It supports collaboration features such as content sharing, commenting, and real-time collaboration on reports.
  6. Mobile Accessibility: Power BI offers mobile apps for iOS and Android devices, enabling users to access their reports and dashboards on the go.
  7. Security and Compliance: Power BI provides robust security and compliance features, including data encryption, role-based access control, and integration with Azure Active Directory for identity and access management.
  8. Integration: Power BI seamlessly integrates with other Microsoft products like Excel, SharePoint, and Azure, as well as third-party applications and services through connectors and APIs.
  9. AI and Machine Learning: Power BI incorporates artificial intelligence and machine learning capabilities for features like natural language queries, automated insights, and predictive analytics.
  10. Customization: Users can extend Power BI’s functionality by creating custom visuals, and custom connectors, and using Power BI’s developer APIs.

Power BI is used across various industries and business functions to analyze data, monitor key performance indicators (KPIs), track business metrics, and gain actionable insights from data. It is suitable for businesses of all sizes, from small startups to large enterprises, and it can be deployed on the cloud (Power BI Service) or on-premises (Power BI Report Server).

Here’s a roadmap to help you get started and progress in your journey of learning Power BI:

Learning Power BI can be an excellent investment in your data analytics and visualization skills.

1. Understand the Basics:

  • Introduction to Power BI: Start by understanding what Power BI is and what it can do for data analysis and visualization.
  • Power BI Components: Learn about the different components of Power BI, including Power BI Desktop, Power BI Service (PowerBI.com), and Power BI Mobile.
  • Getting Data: Explore how to connect Power BI to various data sources, import data, and perform basic data transformations using Power Query.

2. Power BI Desktop Mastery:

  • Installation and Setup: Download and install Power BI Desktop, the application used for creating reports and dashboards.
  • Data Transformation: Dive deeper into Power Query and learn how to shape and clean your data.
  • Data Modeling: Understand how to create relationships between tables, define calculated columns, and write DAX (Data Analysis Expressions) measures for more advanced calculations.
  • Data Visualization: Explore different types of visualizations (e.g., charts, tables, maps) and best practices for creating effective visuals.
  • Power BI Custom Visuals: Learn how to use and even create custom visuals for unique reporting needs.
  • Advanced Features: Explore features like drill-through, bookmarks, report themes, and dynamic filtering.

3. Power BI Service:

  • Publishing Reports: Learn how to publish your Power BI reports to the Power BI Service (PowerBI.com) for sharing and collaboration.
  • Sharing and Collaboration: Understand how to share reports with colleagues, set up dashboards, and collaborate on reports.
  • Security and Permissions: Familiarize yourself with role-based access control and how to manage permissions for data and reports.
  • Data Refresh: Learn how to schedule and configure data refreshes for your reports in the Power BI Service.

4. Advanced Topics:

  • Advanced DAX: Deepen your understanding of Data Analysis Expressions (DAX) for complex calculations and modeling.
  • Power Query M Language: Explore the M language to perform advanced data transformations and scripting.
  • Row-level Security: Implement row-level security to restrict data access based on user roles.
  • API Integration: Learn how to integrate Power BI with other services and applications using APIs.
  • Advanced Visualization Techniques: Master advanced visualization techniques, including custom themes and layouts.
  • Performance Optimization: Discover techniques to optimize report and query performance.

5. Real-World Projects:

  • Build Projects: Start working on real-world projects or scenarios to apply your Power BI skills.
  • Data Modeling Challenges: Tackle complex data modeling challenges commonly encountered in business scenarios.
  • Problem Solving: Use Power BI to solve specific business problems or analyze data from your work or personal interests.

6. Community and Resources:

  • Online Communities: Join Power BI communities, forums, and groups to ask questions, share knowledge, and learn from others.
  • Tutorials and Courses: Take online courses or follow tutorials on Power BI, which are available on platforms like Microsoft Learn, Udemy, Coursera, and LinkedIn Learning.
  • Official Documentation: Refer to the official Power BI documentation for in-depth information on features and functions.

7. Stay Updated:

  • Keep Learning: Power BI is continuously evolving, so stay updated with the latest features and best practices by following Microsoft’s announcements and blogs.

Remember that learning Power BI is a progressive journey, and you can tailor this roadmap to your specific needs and interests. As you gain experience and confidence, you can tackle more complex projects and explore advanced topics to become a proficient Power BI user.