#Python data manipulation

[trendingnow3-blog]

Day-1: Demystifying Python Variables: A Comprehensive Guide for Data Management

Python Boot Camp Series 2023.

Python is a powerful and versatile programming language used for a wide range of applications. One of the fundamental concepts in Python, and in programming in general, is working with variables. In this article, we will explore what variables are, how to use them effectively to manage data, and some best practices for their usage. What are Variables in Python? Definition of Variables In…

[codewithnazam]

Cleaning Dirty Data in Python: Practical Techniques with Pandas

I. Introduction Hey there! So, let’s talk about a really important step in data analysis: data cleaning. It’s basically like tidying up your room before a big party – you want everything to be neat and organized so you can find what you need, right? Now, when it comes to sorting through a bunch of messy data, you’ll be glad to have a tool like Pandas by your side. It’s like the superhero of…

[izicodes]

Hi! I’m a student currently learning computer science in college and would love it if you had any advice for a cool personal project to do? Thanks!

Personal Project Ideas

Hiya!! 💕

It's so cool that you're a computer science student, and with that, you have plenty of options for personal projects that can help with learning more from what they teach you at college. I don't have any experience being a university student however 😅

Someone asked me a very similar question before because I shared my projects list and they asked how I come up with project ideas - maybe this can inspire you too, here's the link to the post [LINK]

However, I'll be happy to share some ideas with you right now. Just a heads up: you can alter the projects to your own specific interests or goals in mind. Though it's a personal project meaning not an assignment from school, you can always personalise it to yourself as well! Also, I don't know the level you are, e.g. beginner or you're pretty confident in programming, if the project sounds hard, try to simplify it down - no need to go overboard!!

But here is the list I came up with (some are from my own list):

Personal Finance Tracker

A web app that tracks personal finances by integrating with bank APIs. You can use Python with Flask for the backend and React for the frontend. I think this would be great for learning how to work with APIs and how to build web applications 🏦

Online Food Ordering System

A web app that allows users to order food from a restaurant's menu. You can use PHP with Laravel for the backend and Vue.js for the frontend. This helps you learn how to work with databases (a key skill I believe) and how to build interactive user interfaces 🙌🏾

Movie Recommendation System

I see a lot of developers make this on Twitter and YouTube. It's a machine-learning project that recommends movies to users based on their past viewing habits. You can use Python with Pandas, Scikit-learn, and TensorFlow for the machine learning algorithms. Obviously, this helps you learn about how to build machine-learning models, and how to use libraries for data manipulation and analysis 📊

Image Recognition App

This is more geared towards app development if you're interested! It's an Android app that uses image recognition to identify objects in a photo. You can use Java or Kotlin for the Android development and TensorFlow for machine learning algorithms. Learning how to work with image recognition and how to build mobile applications - which is super cool 👀

Social Media Platform

(I really want to attempt this one soon) A web app that allows users to post, share, and interact with each other's content. Come up with a cool name for it! You can use Ruby on Rails for the backend and React for the frontend. This project would be great for learning how to build full-stack web applications (a plus cause that's a trend that companies are looking for in developers) and how to work with user authentication and authorization (another plus)! 🎭

Text-Based Adventure Game

If you're interested in game developments, you could make a simple game where users make choices and navigate through a story by typing text commands. You can use Python for the game logic and a library like Pygame for the graphics. This project would be great for learning how to build games and how to work with input/output. 🎮

Weather App

Pretty simple project - I did this for my apprenticeship and coding night classes! It's a web app that displays weather information for a user's location. You can use Node.js with Express for the backend and React for the frontend. Working with APIs again, how to handle asynchronous programming, and how to build responsive user interfaces! 🌈

Online Quiz Game

A web app that allows users to take quizzes and compete with other players. You could personalise it to a module you're studying right now - making a whole quiz application for it will definitely help you study! You can use PHP with Laravel for the backend and Vue.js for the frontend. You get to work with databases, build real-time applications, and maybe work with user authentication. 🧮

Chatbot

(My favourite, I'm currently planning for this one!) A chatbot that can answer user questions and provide information. You can use Python with Flask for the backend and a natural language processing library like NLTK for the chatbot logic. If you want to mauke it more beginner friendly, you could use HTML, CSS and JavaScript and have hard-coded answers set, maybe use a bunch of APIs for the answers etc! This project would be great because you get to learn how to build chatbots, and how to work with natural language processing - if you go that far! 🤖

Another place I get inspiration for more web frontend dev projects is on Behance and Pinterest - on Pinterest search for like "Web design" or "[Specific project] web design e.g. shopping web design" and I get inspiration from a bunch of pins I put together! Maybe try that out!

I hope this helps and good luck with your project!

[ebabunnii]

I need to talk about an alarming trend I'm seeing on reddit, trans people either

a: being downvoted to hell for simply existing as trans people

b: trans people being banned from other subreddits, not for anything said and posted in that subreddit, but for simply posting in trans/and or bimbofication related kink subredits

free speech is free speech, but algorithmic api manipulation is a hateful action. You're not voicing your opinion. You're blocking others' rights to exist and use their own free speech and right to freedom of expression

imo, I don't think it's okay to be banned from r/outfits for answering posts in good faith

ciswomen have many of the same kinks as trans people, but trans women get punished for it

I have just as much as a right to exist as you, and if I have a hard time convincing you, well here's a brick

also fuck you

also I'm running a b data science tests with api users against the reddit api to prove communities are being algorithmically transphobic, if you know python and wanna help hit me up

finally, terfs aren't feminists because you're fundamentally defining women by their ability to breed

[uthra-krish]

From Curious Novice to Data Enthusiast: My Data Science Adventure

I've always been fascinated by data science, a field that seamlessly blends technology, mathematics, and curiosity. In this article, I want to take you on a journey—my journey—from being a curious novice to becoming a passionate data enthusiast. Together, let's explore the thrilling world of data science, and I'll share the steps I took to immerse myself in this captivating realm of knowledge.

The Spark: Discovering the Potential of Data Science

The moment I stumbled upon data science, I felt a spark of inspiration. Witnessing its impact across various industries, from healthcare and finance to marketing and entertainment, I couldn't help but be drawn to this innovative field. The ability to extract critical insights from vast amounts of data and uncover meaningful patterns fascinated me, prompting me to dive deeper into the world of data science.

Laying the Foundation: The Importance of Learning the Basics

To embark on this data science adventure, I quickly realized the importance of building a strong foundation. Learning the basics of statistics, programming, and mathematics became my priority. Understanding statistical concepts and techniques enabled me to make sense of data distributions, correlations, and significance levels. Programming languages like Python and R became essential tools for data manipulation, analysis, and visualization, while a solid grasp of mathematical principles empowered me to create and evaluate predictive models.

The Quest for Knowledge: Exploring Various Data Science Disciplines

A. Machine Learning: Unraveling the Power of Predictive Models

Machine learning, a prominent discipline within data science, captivated me with its ability to unlock the potential of predictive models. I delved into the fundamentals, understanding the underlying algorithms that power these models. Supervised learning, where data with labels is used to train prediction models, and unsupervised learning, which uncovers hidden patterns within unlabeled data, intrigued me. Exploring concepts like regression, classification, clustering, and dimensionality reduction deepened my understanding of this powerful field.

B. Data Visualization: Telling Stories with Data

In my data science journey, I discovered the importance of effectively visualizing data to convey meaningful stories. Navigating through various visualization tools and techniques, such as creating dynamic charts, interactive dashboards, and compelling infographics, allowed me to unlock the hidden narratives within datasets. Visualizations became a medium to communicate complex ideas succinctly, enabling stakeholders to understand insights effortlessly.

C. Big Data: Mastering the Analysis of Vast Amounts of Information

The advent of big data challenged traditional data analysis approaches. To conquer this challenge, I dived into the world of big data, understanding its nuances and exploring techniques for efficient analysis. Uncovering the intricacies of distributed systems, parallel processing, and data storage frameworks empowered me to handle massive volumes of information effectively. With tools like Apache Hadoop and Spark, I was able to mine valuable insights from colossal datasets.

D. Natural Language Processing: Extracting Insights from Textual Data

Textual data surrounds us in the digital age, and the realm of natural language processing fascinated me. I delved into techniques for processing and analyzing unstructured text data, uncovering insights from tweets, customer reviews, news articles, and more. Understanding concepts like sentiment analysis, topic modeling, and named entity recognition allowed me to extract valuable information from written text, revolutionizing industries like sentiment analysis, customer service, and content recommendation systems.

Building the Arsenal: Acquiring Data Science Skills and Tools

Acquiring essential skills and familiarizing myself with relevant tools played a crucial role in my data science journey. Programming languages like Python and R became my companions, enabling me to manipulate, analyze, and model data efficiently. Additionally, I explored popular data science libraries and frameworks such as TensorFlow, Scikit-learn, Pandas, and NumPy, which expedited the development and deployment of machine learning models. The arsenal of skills and tools I accumulated became my assets in the quest for data-driven insights.

The Real-World Challenge: Applying Data Science in Practice

Data science is not just an academic pursuit but rather a practical discipline aimed at solving real-world problems. Throughout my journey, I sought to identify such problems and apply data science methodologies to provide practical solutions. From predicting customer churn to optimizing supply chain logistics, the application of data science proved transformative in various domains. Sharing success stories of leveraging data science in practice inspires others to realize the power of this field.

Cultivating Curiosity: Continuous Learning and Skill Enhancement

Embracing a growth mindset is paramount in the world of data science. The field is rapidly evolving, with new algorithms, techniques, and tools emerging frequently. To stay ahead, it is essential to cultivate curiosity and foster a continuous learning mindset. Keeping abreast of the latest research papers, attending data science conferences, and engaging in data science courses nurtures personal and professional growth. The journey to becoming a data enthusiast is a lifelong pursuit.

Joining the Community: Networking and Collaboration

Being part of the data science community is a catalyst for growth and inspiration. Engaging with like-minded individuals, sharing knowledge, and collaborating on projects enhances the learning experience. Joining online forums, participating in Kaggle competitions, and attending meetups provides opportunities to exchange ideas, solve challenges collectively, and foster invaluable connections within the data science community.

Overcoming Obstacles: Dealing with Common Data Science Challenges

Data science, like any discipline, presents its own set of challenges. From data cleaning and preprocessing to model selection and evaluation, obstacles arise at each stage of the data science pipeline. Strategies and tips to overcome these challenges, such as building reliable pipelines, conducting robust experiments, and leveraging cross-validation techniques, are indispensable in maintaining motivation and achieving success in the data science journey.

Balancing Act: Building a Career in Data Science alongside Other Commitments

For many aspiring data scientists, the pursuit of knowledge and skills must coexist with other commitments, such as full-time jobs and personal responsibilities. Effectively managing time and developing a structured learning plan is crucial in striking a balance. Tips such as identifying pockets of dedicated learning time, breaking down complex concepts into manageable chunks, and seeking mentorships or online communities can empower individuals to navigate the data science journey while juggling other responsibilities.

Ethical Considerations: Navigating the World of Data Responsibly

As data scientists, we must navigate the world of data responsibly, being mindful of the ethical considerations inherent in this field. Safeguarding privacy, addressing bias in algorithms, and ensuring transparency in data-driven decision-making are critical principles. Exploring topics such as algorithmic fairness, data anonymization techniques, and the societal impact of data science encourages responsible and ethical practices in a rapidly evolving digital landscape.

Embarking on a data science adventure from a curious novice to a passionate data enthusiast is an exhilarating and rewarding journey. By laying a foundation of knowledge, exploring various data science disciplines, acquiring essential skills and tools, and engaging in continuous learning, one can conquer challenges, build a successful career, and have a good influence on the data science community. It's a journey that never truly ends, as data continues to evolve and offer exciting opportunities for discovery and innovation. So, join me in your data science adventure, and let the exploration begin!

[sadwinning]

Devlog 1 (1/25/24): Why This Is Pointless

In my intro post, I mentioned how it would be much easier to map the 12 chromatic notes of Western music to the 3 action buttons and 8 directions of Undertale, and how I won't be doing that for purely aesthetic reasons. I also want to mention why everything I'm doing to my violin is completely stupid.

If you want to follow in my footsteps, you shouldn't do it the way I'm doing it. You probably can't.

My violin is a Yamaha EV-205 five-string electric from the late aughts/early 10's. I recently learned that this violin is no longer in production, so there's no way your standard Joe Schmoe can pick up this tutorial, nor would they want to if they were in the market for an electric violin, because they already sell electric violins that are MIDI controller enabled. You should buy that and follow the software specs of CZR drums and their MIDI-to-controller software partner/whatever. I simply do not want to spend more money on an electric violin when I already have one with the right hardware (individual pickups for each of the five strings). So I will be voiding the warranty that likely no longer exists and busting open my violin to see what I can patch together.

When I busted this component (pictured above) open I immediately found a not-so-complex PCB where I could locate each of the individual string inputs. I have yet to see whether those ports will give me the inputs I need - golly, I have yet to learn how to solder enough to access those ports!! - but the visibility gives me hope. it doesn't look hard, especially for someone who has been low-key interested in soldering for like 15 years (since my Pokemon Gold copy's battery died and I learned the ways to replace it) but I can't say I know exactly what data flows through that part of the circuit and how easy it would be to extract and manipulate.

I've done a lot of research into what I would need to take analog audio signal(s) and transform them into MIDI or some other binary/digital data. The first thing I found was an Arduino library, so I knew this wouldn't be hard. I only have one Arduino (knock-off) and I didn't like the idea of buying four more (one for each string) to get the MIDI values when I would probably be connected to a computer the whole time no matter what.

This led me to where I'm sitting pretty right now, at a Python library (Python being my favorite language) that uses its GitHub .md file to explain why Markov chains are important. Reader, do you know how much I love Markov chains? Did you know that in my sophomore year of college I created a musical AI by programming Markov chains in Python??? How is it that all of my interests loop in upon each other in the same way that my first and only job out of college involved natural language processing in Python just like my senior project where I did language analysis on okcupid profiles???? Is time in fact a flat circle? I don't have time to think about this because I want to program violin to play undertale pleas

Where I'll be starting is with this library and with monophonic input (one note at a time rather than interpreting multiple notes at once e.g. multiple strings played simultaneously) to make a controller of any kind work. But I have a lot of reading to do to see how Markov chains are involved. With it being both Python and linear algebra, I have the capacity to adjust the code to do whatever I want it to do. Given this insane opportunity I can't not do all the research possible to finetune things to my precise desires. If I were satisfied with "good enough", I would be playing monophonic input the whole way through. Let's go insane, boys.

[abalidoth]

What are some of your favorite parts of Haskell? Do you know any other languages?

Haskell's mathematical completeness is super cool. It's quite amazing that you can actually prove, with a theorem, that the language doesn't have any ambiguous statements. (I don't know exactly how the theorem goes, I haven't dug into it enough to word it properly, but it's cool.) Also, it's just fun to solve problems in functional programming; it requires a whole different section of my brain from my usual programming.

My bread and butter, the language I use for work as well as my personal projects, is Python. It's the language I'm most fluent in by a long shot; there's no delay between coming up with an idea and executing it in code. I know there are a lot of folks who aren't fond of Python, and to be fair people try to apply it where it's just not really a good tool, but as a rapid prototyping, data manipulation, and fun-code-toy language it's phenomenal. Also there's a package for, like, everything.

I've used a TON of other languages in bits and pieces in the past -- Java, C++, a bit of C#, a lot of SQL and HTML (neither is exactly a PROGRAMMING language, but eh) and a lot of obscure math languages like Magma, GAP, and Macsyma.

The thing I'm most interested to learn is Lean, a theorem proving language. It's pretty amazing what you can do with it, and I think it'll revolutionize math academia once it's in wide use.

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[python-programming-language]

The use of variables in Python ...

Post #126: Real Python, Variables in Python, 2024.

[trendingnow3-blog]

Day-4: Unlocking the Power of Randomization in Python Lists

Python Boot Camp 2023 - Day-4

Randomization and Python List Introduction Randomization is an essential concept in computer programming and data analysis. It involves the process of generating random elements or sequences that have an equal chance of being selected. In Python, randomization is a powerful tool that allows developers to introduce an element of unpredictability and make programs more dynamic. This article…

[blech]

tumblr-backup and datasette

I've been using tumblr_backup, a script that replicates the old Tumblr backup format, for a while. I use it both to back up my main blog and the likes I've accumulated; they outnumber posts over two to one, it turns out.

Sadly, there isn't an 'archive' view of likes, so I have no idea what's there from way back in 2010, when I first really heavily used Tumblr. Heck, even getting back to 2021 is hard. Pulling that data to manipulate it locally seems wise.

I was never quite sure it'd backed up all of my likes, and it turns out that a change to the API was in fact limiting it to the most recent 1,000 entries. Luckily, someone else noticed this well before I did, and a new version, tumblr-backup, not only exists, but is a Python package, which made it easy to install and run. (You do need an API key.)

I ran it using this invocation, which saved likes (-l), didn't download images (-k), skipped the first 1,000 entries (-s 1000), and output to the directory 'likes/full' (-O):

tumblr-backup -j -k -l -s 1000 blech -O likes/full 

This gave me over 12,000 files in likes/full/json, one per like. This is great, but a database is nice for querying. Luckily, jq exists:

jq -s 'map(.)' likes/full/json/*.json > likes/full/likes.json

This slurps (-s) in every JSON file, iterates over them to make a list, and then saves it in a new JSON file, likes.json. There was a follow-up I did to get it into the right format for sqlite3:

jq -c '.[]' likes/full/likes.json > likes/full/likes-nl.json

A smart reader can probably combine those into a single operator.

Using Simon Willison's sqlite-utils package, I could then load all of them into a database (with --alter because the keys of each JSON file vary, so the initial column setup is incomplete):

sqlite-utils insert likes/full/likes.db lines likes/full/likes-nl.json --nl --alter

This can then be fed into Willison's Datasette for a nice web UI to query it:

datasette serve --port 8002 likes/full/likes.d

There are a lot of columns there that clutter up the view: I'd suggest this is a good subset (it also shows the post with most notes (likes, reblogs, and comments combined) at the top):

select rowid, id, short_url, slug, blog_name, date, timestamp, liked_timestamp, caption, format, note_count, state, summary, tags, type from lines order by note_count desc limit 101

Happy excavating!

[codewithnazam]

DataFrame in Pandas: Guide to Creating Awesome DataFrames

Explore how to create a dataframe in Pandas, including data input methods, customization options, and practical examples.

Data analysis used to be a daunting task, reserved for statisticians and mathematicians. But with the rise of powerful tools like Python and its fantastic library, Pandas, anyone can become a data whiz! Pandas, in particular, shines with its DataFrames, these nifty tables that organize and manipulate data like magic. But where do you start? Fear not, fellow data enthusiast, for this guide will…

[izicodes]

Any good python modules I can learn now that I'm familiar with the basics?

Hiya 💗

Yep, here's a bunch you can import them into your program to play around with!

math: Provides mathematical functions and constants.

random: Enables generation of random numbers, choices, and shuffling.

datetime: Offers classes for working with dates and times.

os: Allows interaction with the operating system, such as file and directory manipulation.

sys: Provides access to system-specific parameters and functions.

json: Enables working with JSON (JavaScript Object Notation) data.

csv: Simplifies reading and writing CSV (Comma-Separated Values) files.

re: Provides regular expression matching operations.

requests: Allows making HTTP requests to interact with web servers.

matplotlib: A popular plotting library for creating visualizations.

numpy: Enables numerical computations and working with arrays.

pandas: Provides data structures and analysis tools for data manipulation.

turtle: Allows creating graphics and simple games using turtle graphics.

time: Offers functions for time-related operations.

argparse: Simplifies creating command-line interfaces with argument parsing.

How to actually import to your program?

Just in case you don't know, or those reading who don't know:

Use the 'import' keyword, preferably at the top of the page, and the name of the module you want to import. OPTIONAL: you could add 'as [shortname you want to name it in your program]' at the end to use the shortname instead of the whole module name

Hope this helps, good luck with your Python programming! 🙌🏾

[codewithishraq]

Null Stack to Full Stack

OVERVIEW

Full stack technology refers to the entire depth of a computer system application, and full stack developers straddle two separate web development domains: the front end and the back end.

The front end includes everything that a client, or site viewer, can see and interact with. By contrast, the back end refers to all the servers, databases, and other internal architecture that drives the application; usually, the end-user never interacts with this realm directly. 

The easiest way to put the full stack into perspective is to imagine a restaurant. The front end encompasses the well-decorated, comfortable seating areas where visitors enjoy their food. The kitchen and pantry make up the “back end” and are typically hidden away from the customer’s view. Chefs (developers) gather permanently stored materials from the pantry (the database) and perform operations on it in the kitchen (the server), and then serve up fully-prepared meals (information) to the user. 

ADVANTAGES OF LEARNING FULLSTACK DEVELOPMENT

You can master all the techniques involved in a development project

You can make a prototype very rapidly

You can provide help to all the team members

You can reduce the cost of the project

You can reduce the time used for team communication

You can switch between front and back end development based on requirements

You can better understand all aspects of new and upcoming technologies

SKILLS NEEDED

In this case, you might find various things in the internet. They all might vary. But I am keeping things simple. Among the things I am going to share, you need to focus on one stack instead of all of them. So, here are some of the skills needed to be a fullstack developer.

Front-end programming technologies: HTML, CSS, JavaScript, Angular, ReactJS, Bootstrap, jQuery, SASS, Tailwind etc.

Back-end programming technologies:Python, NodeJS, Django, Express etc.

Database: PostgreSQL, MongoDB, MySQL, etc. 

Version Control System: git, GitHub, GitLab, etc

HTTPS and REQUEST Methods (GET, POST, PUT, DELETE, OPTIONS)

Now, it iis important to understand that, the basics are same for all stacks but then the technologies vary. For example the frontend can be built with either React, Angular or Vue or any other framework/library. On the other hand, the backend can be built with either of Node.js, Django (Python) or Spring Boot (Java) or any other framework. I will go to that in the coming lines.

ROADMAP / PLAN FOR THE FIRST SIX MONTHS

About this, there might be multiple other roadmaps that you can follow on your path to become a fullstack developer. I came up with the idea that this path, that I am about to share, can be a planned start to your journey with all the content structured at the right time. So, let's see the plan for the first six months.

🔵 Month 1: HTML, CSS, Javascript

The basic skills required to create a website in HTML and CSS. Javascript adds functionalities to a website and makes the project responsive. HTML is for structure and CSS for Styling. DOM Manipulation and Responsive Web Design are important to practice. Learn about these from W3Schools.

🔵 Month 2: Web Design and Frameworks, Git, HTTPs

Work on Open Source Projects. Once you have good practice with HTML and CSS you can use frameworks like Bootstrap or Material CSS which makes it easy to create websites. Alongside that, it is very important to learn about version control systems (preferrably git) so that you can save and manage your code at GitHub, GitLab, BitBucket or any other similar tool. Also, it is important to learn about HTTPS and REQUEST METHODS (GET, POST, PUT, DELETE and OPTIONS).

🔵 Month 3: Javascript Programming Language

The most important skill and most asked in Interviews and Job portals for Web Development are Javascript. You can expect a lot of interview questions from Javascript, So it's important to learn how javascript works, data structures, and asynchronous javascript.

🔵 Month 4 & 5: Frontend and Backend

Once you are thorough with the above concepts then you can take your skills to the next level by learning Javascript frameworks/libraries like React and Node JS. Point to be noted, I am a big fan of MERN (Mongo, Express, React, Node) stack, so I am always talking about React and Node. But there are other options as well.

Other options:

Frontend: Angular, Vue or any other frontend technology

Backend: Django, Flask, Spring Boot, ASP.Net or any other backend technology

Please do some research in google about the 'FULLSTACK TECH STACKS' and choose the one that you are the most comfortable with. Just a reminder, if you want to be a Java Fullstack Developer, then you need to have Java knowledge before stepping on to learning Fullstack development. Same case goes for Python, C# or any other technologies.

Most importantly, when you start learning a new technology, please start by learning from the official documentation of each individual technology. Then maybe go for other resources from the internet.

🔵 Month 6: Database and Projects

In the final month, create a portfolio and create projects using frontend and backend technologies you’ve learnt. Also, an important skill to have is knowledge of Database Management Systems like PostgreSQL, MySQL and MongoDB. Also, you need to understand how to connect the Database to Server using the backend Framework.

LEARNING RESOURCES OF FULLSTACK DEVELOPMENT

In the internet today, you can find various courses and tutorials on Fullstack development. But I know for sure that Freecodecamp website as well as YouTube channel covers all stack, so you can easily learn from them. On the other hand, there is The Odin Project. You can learn about JavaScript Fullstack Developer or Ruby on Rails Fullstack Developer. Here are the links to them.

Freecodecamp Website

Freecodecamp YouTube Channel

The Odin Project

Of course, as I said, you can look for courses in other websites as well. Here are some of the best platforms to look for courses.

Codecademy

Coursera

EdX

PROJECT IDEAS

Here are some projects that you can try when you are learning or after you have gone through all the things needed.

E-commerce website

Food delivery app

Social media app

Chat messaging app

Content management system

Project management app

Gym Tracking System

Real-time Chat App

Bug Report App

Hotel Booking App

Staff Management System

Online Store

INTERVIEW PREPS AND RESOURCES

Remember that a fullstack developer job is a vast space and thus there are many things that you need to keep focus on to ace the interviews. Here are some points where you need to take special care of for the interviews.

Javascript Programming Language and Data Structures

CSS Concepts like Flexbox, Grid, Inheritance, Specificity, etc.

React JS and new features e.g: Context API and Hooks

REST API’s and SQL and DBMS

HTTPS, Requests, Response, Servers.

Of course there are more things to focus as well, so research about the most important topics from the internet and then take special care in preparing for those questions.

Here are a few links to resources which will help you preparing for the interviews.

Coding Interview University

Interview Cake

Interview Bit

Tech Interview Handbook

Fullstack Cafe

Word of advice for newbies

Please don’t wait for people to spoon-feed you with every single resource and teachings because you’re on your own in your learning path. So be wise and learn yourself.

About Me

I am Ishraq Haider Chowdhury from Bangladesh, currently living in Bamberg, Germany. I am a fullstack developer mainly focusing on MERN Stack applications with JavaScript and TypeScript. I have been in this industry for about 9 years and still counting. If you want to find me, here are some of my social links....

Instagram

TikTok

YouTube

Facebook

Twitter

GitHub

[uthra-krish]

The Skills I Acquired on My Path to Becoming a Data Scientist

Data science has emerged as one of the most sought-after fields in recent years, and my journey into this exciting discipline has been nothing short of transformative. As someone with a deep curiosity for extracting insights from data, I was naturally drawn to the world of data science. In this blog post, I will share the skills I acquired on my path to becoming a data scientist, highlighting the importance of a diverse skill set in this field.

The Foundation — Mathematics and Statistics

At the core of data science lies a strong foundation in mathematics and statistics. Concepts such as probability, linear algebra, and statistical inference form the building blocks of data analysis and modeling. Understanding these principles is crucial for making informed decisions and drawing meaningful conclusions from data. Throughout my learning journey, I immersed myself in these mathematical concepts, applying them to real-world problems and honing my analytical skills.

Programming Proficiency

Proficiency in programming languages like Python or R is indispensable for a data scientist. These languages provide the tools and frameworks necessary for data manipulation, analysis, and modeling. I embarked on a journey to learn these languages, starting with the basics and gradually advancing to more complex concepts. Writing efficient and elegant code became second nature to me, enabling me to tackle large datasets and build sophisticated models.

Data Handling and Preprocessing

Working with real-world data is often messy and requires careful handling and preprocessing. This involves techniques such as data cleaning, transformation, and feature engineering. I gained valuable experience in navigating the intricacies of data preprocessing, learning how to deal with missing values, outliers, and inconsistent data formats. These skills allowed me to extract valuable insights from raw data and lay the groundwork for subsequent analysis.

Data Visualization and Communication

Data visualization plays a pivotal role in conveying insights to stakeholders and decision-makers. I realized the power of effective visualizations in telling compelling stories and making complex information accessible. I explored various tools and libraries, such as Matplotlib and Tableau, to create visually appealing and informative visualizations. Sharing these visualizations with others enhanced my ability to communicate data-driven insights effectively.

Machine Learning and Predictive Modeling

Machine learning is a cornerstone of data science, enabling us to build predictive models and make data-driven predictions. I delved into the realm of supervised and unsupervised learning, exploring algorithms such as linear regression, decision trees, and clustering techniques. Through hands-on projects, I gained practical experience in building models, fine-tuning their parameters, and evaluating their performance.

Database Management and SQL

Data science often involves working with large datasets stored in databases. Understanding database management and SQL (Structured Query Language) is essential for extracting valuable information from these repositories. I embarked on a journey to learn SQL, mastering the art of querying databases, joining tables, and aggregating data. These skills allowed me to harness the power of databases and efficiently retrieve the data required for analysis.

Domain Knowledge and Specialization

While technical skills are crucial, domain knowledge adds a unique dimension to data science projects. By specializing in specific industries or domains, data scientists can better understand the context and nuances of the problems they are solving. I explored various domains and acquired specialized knowledge, whether it be healthcare, finance, or marketing. This expertise complemented my technical skills, enabling me to provide insights that were not only data-driven but also tailored to the specific industry.

Soft Skills — Communication and Problem-Solving

In addition to technical skills, soft skills play a vital role in the success of a data scientist. Effective communication allows us to articulate complex ideas and findings to non-technical stakeholders, bridging the gap between data science and business. Problem-solving skills help us navigate challenges and find innovative solutions in a rapidly evolving field. Throughout my journey, I honed these skills, collaborating with teams, presenting findings, and adapting my approach to different audiences.

Continuous Learning and Adaptation

Data science is a field that is constantly evolving, with new tools, technologies, and trends emerging regularly. To stay at the forefront of this ever-changing landscape, continuous learning is essential. I dedicated myself to staying updated by following industry blogs, attending conferences, and participating in courses. This commitment to lifelong learning allowed me to adapt to new challenges, acquire new skills, and remain competitive in the field.

In conclusion, the journey to becoming a data scientist is an exciting and dynamic one, requiring a diverse set of skills. From mathematics and programming to data handling and communication, each skill plays a crucial role in unlocking the potential of data. Aspiring data scientists should embrace this multidimensional nature of the field and embark on their own learning journey. If you want to learn more about Data science, I highly recommend that you contact ACTE Technologies because they offer Data Science courses and job placement opportunities. Experienced teachers can help you learn better. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested. By acquiring these skills and continuously adapting to new developments, they can make a meaningful impact in the world of data science.

[samvavlabs]

Data Analyst Roadmap for 2024!

Cracking the Data Analyst Roadmap for 2024! Kick off your journey by mastering and delving into Python for data manipulation magic, and dazzle stakeholders with insights using PowerBi or Tableau. Don't forget, that SQL proficiency and hands-on projects refine your skillset, but never overlook the importance of effective communication and problem-solving. Are you checking off these milestones on your path to success? 📌 For more details, visit our website: https://www.samvavlabs.com  . . . #DataAnalyst2024 #CareerGrowth #roadmap #DataAnalyst #samvavlabs #roadmap2024 #dataanalystroadmap #datavisualization