teleren/devto huggingface dataset:
How to Become Data Scientist – A Road-map in 2023
Published: 2023-10-01 21:06:44

**Intro**
Data science is gaining immense significance in the 21st century. It's often called "The Sexiest Job" by Harvard Business Review. In the era of Big Data, organizations handle vast amounts of data, which used to be a storage challenge until 2010. With the advent of technologies like Hadoop, storage is no longer the primary concern. Instead, the focus has shifted to processing this data effectively. Data science plays a pivotal role in this process.

The field of data science is rapidly growing, offering numerous opportunities for those willing to learn. To prepare for the future, it's essential to understand what data science is and how it can create value.
**Data Science** can be simplified as the practice of using data to answer questions and gain insights. It involves:

Statistics, Computer Science, Mathematics: Data science combines skills from these areas to analyze data effectively.

Data Cleaning and Formatting: Ensuring data is accurate and organized.

Data Visualization: Creating visual representations of data for easier understanding.

**To get started in Data Science, consider your goals:**

Why do you want to learn Data Science? (e.g., for a career change or academic projects)
How to learn? (Consider online resources, workshops, and conferences)
**Key areas to focus on include:**

Domain Knowledge: Understanding the industry you work in can be valuable.

Math Skills: Linear algebra, calculus, and statistics are essential.

Computer Science: Learn programming languages (Python and R), data structures, SQL, and more.

Communication Skills: Effective communication is necessary for sharing project findings.

Learning Resources: Explore online resources and courses to enhance your skills.

**A Roadmap to Learn Data Science:**

Mathematics: Learn linear algebra, calculus, optimization, and probability.

Statistics: Study data description, hypothesis testing, and regression.

Programming: Master Python or R and learn about data structures.

Machine Learning: Understand supervised and unsupervised learning algorithms.

Deep Learning: Dive into neural networks using TensorFlow, Keras, or PyTorch.

Feature Engineering: Discover techniques to improve model performance.

Natural Language Processing (NLP): Work with text data.

Data Visualization: Create impactful data visualizations.

Deployment: Learn to deploy models in cloud platforms or frameworks like Flask and Django.

Other Skills: Domain knowledge, communication, reinforcement learning, and case studies.

Practice: Continuous practice is essential for mastering data science.

**Remember, data science is a dynamic field, so staying updated with new advancements is crucial. Practice and persistence are key to becoming proficient in data science.**
Data Science for Beginners: 2023 - 2024 Complete Road Map.
Published: 2023-10-01 20:41:54


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/306md5p6x5jliry55ron.jpg)



Navigating the diverse career paths within the expansive domain of data science unveils numerous opportunities, including roles such as data scientist, data analyst, data engineer, machine learning engineer, and computer vision engineer. Each role contributes to the broader spectrum of data science by harnessing specialized skills and knowledge. Delving into data science opens up possibilities to harness analytical methods in a structured way, like efficient data storage in databases, allowing for the emergence of practical solutions through insightful analytics.

In this journey of mastering data science, you not only acquire skills in collecting, reading, and analyzing data but also hone the vital ability to interpret it accurately. This proficiency in interpretation proves instrumental in making informed decisions, especially when scrutinizing diverse trends for companies. For instance, understanding trends can guide strategic alterations in a company's marketing brand, attracting a broader customer base. Ultimately, this comprehensive understanding of data science fosters a holistic perspective, enriching your ability to drive meaningful decisions and navigate the ever-evolving landscape of data-driven insights.

Embracing a career in data science unfolds a roadmap of continuous learning and growth. As you venture deeper into this dynamic field, you'll come to appreciate the blend of technology, statistical methodologies, and business acumen required. The journey involves honing skills in programming languages, machine learning algorithms, data visualization, and the ability to effectively communicate insights to diverse stakeholders. Moreover, understanding the nuances of data ethics, privacy, and governance becomes paramount in the responsible use of data. By navigating this diverse landscape, I believe you position yourself not only as a proficient data professional but also as a strategic asset to organizations seeking to harness the power of data to innovate, optimize processes, and achieve sustainable success.
Data Science for Beginners: 2023 - 2024 Complete Roadmap
Published: 2023-10-01 20:35:51

Data science involves the use of various techniques, processes and systems to extract valuable insights from data. To analyze complex datasets using data science computer science, statistics, mathematics, domain expertise and data engineering are combined.

Getting started in data science involves:
**Mathematics fundamentals**
Start by learning basic mathematics.
This includes linear algebra, calculus and statistics which  is crucial for data analysis.

**Programming.**
Learn the basics of a programming language for data science. This can be either python or R programming language. In this roadmap I'll discuss python programming language.

**Exploratory data analysis** 
After learning and understanding the basics, study libraries like Pandas and NumPy which are used for data analysis and manipulation.
Also explore data analysis and visualization tools and libraries like matplotlib and seaborne which will help you to effectively communicate your findings.

**Machine Learning basics.**
Learn the fundamentals of machine learning and common algorithms used for building predictive models and recommendations based on data.

**Deep Learning.**
Understand libraries such as TensorFlow and PyTorch if you're interested in neural networks.

**Feature Engineering.**
Understand how to extract meaningful features from raw data to build a better performing model.

**Model building and Evaluation**.
Familiarize yourself with machine learning libraries such as scikit-learn in python.
Build machine learning models and evaluate their performance. Also, understand cross validation techniques to ensure your model generalizes well.

**Model Deployment**
Learn how to deploy models to production for use in real world applications.

**Projects**
Work on projects in your areas of interest to practice the newly learned skills. This way you'll learn how to solve different kinds of real world problems.

**Continuous learning**
Keep up with the latest trends in data science by reading books, blogs and also taking online courses.

**Network**
Join data science communities, attend events and collaborate with others to learn and grow.
Working directly with Postgres
Published: 2023-10-01 20:35:36

## Introduction to Databases
A database allows applications to store data permanently. Relational databases use Structured Query Language (SQL) to manage and interact with the data.

## Installing and Accessing PostgreSQL
1. After installing PostgreSQL, launch it from a bash prompt with `psql`.
2. Commands are issued at the `postgres=#` prompt.

## Navigating PostgreSQL
- Use `\list` to display all databases currently in PostgreSQL.
- If command output is long, `:▮` indicates more content. Scroll with `return` (line by line) or `space` (page by page), and press `Q` to exit.

## Creating Databases
- Create a new database with the following command:
  ```sql
  CREATE DATABASE database_name;
  ```
  - A semicolon is required at the end of SQL commands.
  - Confirmation is shown as `CREATE DATABASE`.

## Managing Databases
- Switch to a new database with:
  ```sql
  \connect database_name
  ```
  - The prompt will change to reflect the new database context, e.g., `database_name=#`.

## Creating Tables
- Example of creating a table named `contacts`:
  ```sql
  CREATE TABLE contacts (
    id SERIAL PRIMARY KEY,
    first_name TEXT,
    last_name TEXT,
    date_of_birth DATE,
    street_address_1 TEXT,
    street_address_2 TEXT,
    city TEXT,
    state TEXT,
    zip TEXT,
    phone TEXT,
    notes TEXT,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
  );
  ```

## Viewing Tables
- To see all tables, use:
  ```sql
  \dt
  ```

## Data Types
- Common data types include:
  - `TEXT`: Similar to String.
  - `INTEGER`: A whole number.
  - `BOOLEAN`: true, false, or nil.
  - `DATE`: A date value without a time part.
  - `TIME`: A time value without a date part.
  - `TIMESTAMP`: For values that have both date and time parts.
  - `DECIMAL`: Similar to Float.

## Inserting Data
- To insert data into the `contacts` table:
  ```sql
  INSERT INTO contacts (
    first_name, 
    last_name, 
    date_of_birth, 
    street_address_1, 
    street_address_2, 
    city, 
    state, 
    zip,
    phone,
    notes
  ) VALUES (
    'Carol', 
    'Reynolds', 
    '2016-10-20', 
    '4556 Mirna Shores', 
    'Apt. 111', 
    'Stromanhaven', 
    'DE', 
    '13654-8312',
    '308-571-8066 x3565',
    'We met at the networking event'
  );
  ```
Intro to Machine Learning
Tags: beginners, machinelearning, javascript
Published: 2023-10-01 20:29:15


## What is Machine Learning?
Machine learning is a branch of artificial intelligence. Machine learning uses algorithms to train computer systems and imitate the way that humans learn. The process of teaching a computer how to build associations through inputting data is referred to as **training**.

## Training Models
There are four main training models for machine learning. **Supervised**, **Unsupervised**, **Semi-supervised** and **Reinforcement** training.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/n18t47drpmcz4igzgvw7.jpg)

- **Supervised Machine Learning**: In supervised machine learning the system is fed labeled datasets (input = output) into an algorithm so that is can accurately learn to predict future outcomes.

- **Unsupervised Machine Learning**: In unsupervised machine learning the system is fed an unlabeled dataset (input = ?) and the algorithm is left to discover patterns without human intervention.

- **Semi-Supervised Machine Learning**: In semi-supervised machine learning the system is fed a small labeled dataset, as it is in supervised, and a larger unlabeled dataset. This allows for some level of control like in supervised training without the need to create a large labeled dataset.

- **Reinforcement Machine Learning**: In reinforcement machine learning the system is forced to iterate through a problem multiple times and learns based on trial and error.

## Machine Learning in JS

JavaScript has multiple libraries that allow for building machine learning algorithms. Some of these include Brain.js, Math.js, ml5.js and TensorFlow. Following is an example of reinforcement machine learning called _neuroevolution_. 
- https://github.com/xviniette/FlappyLearning
The Data Science Roadmap for absolute beginners.
Published: 2023-10-01 20:23:42

Data science is a multidisciplinary field that combines mathematics, statistics, programming and domain knowledge to extract valuable insights from data. If you are a beginner looking to embark on a journey in this field, this roadmap will help you navigate through the key concepts, tools and skills necessary to become a proficient data scientist.

 **Understanding the basics**
 Mathematics : linear algebra, calculus and probability theory
 Statistics  : concepts like probability distributions, 
                hypothesis testing and regression analysis

 **Master programming skills**
programming languages like python and R are commonly used in 
data science.
Python programming language in particular offers a variety of libraries for data science, which include; pandas for data analysis, numpy for numerical calculations and scientific computing and matplotlib for visualizations.

**Data Acquisition And Cleaning**
  **SQL**
  Learn Structured Querry Language (SQL) to retrieve and manipulate 
  data from databases.
  **Data cleaning**
  You need to understand data preprocessing techniques, dealing 
  with missing values and handling outliers.

 **Exploratory Data Analysis EDA**
  While performing EDA , you use libraries like pandas, seaborn and 
  plotly to explore and visualize data.
 
 **Machine learning Fundamentals**
   Machine learning is key as models are trained using machine learning algorithms. These algorithms are either supervised or unsupervised. Supervised learning involves use of labelled data while in unsupervised learning the data is unlabelled.
Scikit learn is a popular library for implementing machine learning models.

 **Domain knowledge**
 Gain expertise in the industry you want to apply data science to. It is useful for meaningful analysis.

 **Version control**
 Learn Git for version control to manage your projects effectively and collaborate with others. 

**Data Visualization**
Master data visualization tools such as Tableau and Power Bi to communicate your findings effectively.
Complete Roadmap to Become a DataScientist in 2023/2024
Published: 2023-10-01 20:16:40

We will follow a brief but precize steps to becoming a Data Scientist..Data Science is a wide area of study but we are going to dig into the best way of learning DataScience extensively yet laying a good foundation on the core aspects and tools used in DataScience.The steps below is based on my Experience and it should benefit you as well.I will give recommendation on books and tutorials where neccesary
1.Read 
"Think like a data scientist book by Brian Godsey"
 ~ To get a whole picture of Data Science.
       ...Data Science Movies(https://www.mygreatlearning.com/blog/top-data-science-movies/) i'll recommend just watching one(The Immitation Game)
## **2.Learn to code(python/R, SQL)**
     i.(Datacamp/Dataquest to learn SQL   https://app.datacamp.com/)
     ii.(https://www.freecodecamp.org/   to learn Python)
## **3.Learn Statistics part of Data Science ~ Practical Statistics for Datascientist by Peter Bruce and Andrew Bruce.**
## **4.Learn Real world Datascience through trusted sites on youtube,Bootcamp,Data Science schools.(Any of those)**
   i.https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw
   ii.Lux Dev Bootcamp
   iii.Data science schools eg Explore datascience academy, Moringa School
**
## 5.Apply your Skills
**
    ~join Data Science Platforms to do real world projects eg Zindi Platform https://zindi.africa/?referralCode%3Djlt93W, Omdena https://omdena.com/
DATA SCIENCE FOR BEGINNERS: 2023-2024 COMPLETE ROADMAP
Published: 2023-10-01 20:13:31

**Introduction**
Every industry or organization in the world do produce a
lot of data, and this has led to a demand for Data Scientists.
In this article I will make it easier for you, to learn everything to do with Data Science.


**The Main Outlines For Data Science**
1.Learn Python language
2.Statistics and mathematics 
3.SQL programming languages 
4.Data wrangling and visualization( pandas, NumPy, matplotlib and scipy libraries of python)
5.Use Keras for Deep learning 
6.Machine learning
7.NLP concepts(Natural language processing)
8.Deployment

When you learn this concepts and also do your own practice projects, you will be on your way to be a great Data scientist.
My Hacktober10 Pledge
Tags: hacktoberfest23
Published: 2023-10-01 19:46:51


**Background**
Hello everyone, I am a passionate AR/VR developer from India.
This is going to be my first time participating in hacktoberfest and writing a post on dev.to!


**Pledge**
Participating in hacktoberfest as a contributor, I pledge to make quality contributions to interesting repositories and guide new contributors for the same!
Web Design Wonderland: 10 Portfolio Inspirations to Elevate Your Site 🚀
Tags: webdev, frontend, javascript, design
Published: 2023-10-01 19:31:09

Looking for some inspiration for your own portfolio website or other projects? Just want to check out some well-designed sites? Look no further than these 10 digital portfolios that are bound to get your creative juices flowing. ✨

## 1) <a href="https://www.jashsak.com/" target="_blank">Jo Ash Sakula</a>
With a satisfying intro animation and an interesting horizontal-scrolling projects section, Jo's portfolio is sure to captivate visitors right from the start.

## 2) [Effy Zhang](http://effyzhang.com/)
Effy's portfolio is a perfect example of a single-page portfolio—simple yet effective and infused with personal flair.

## 3) [Robin Clediere](https://robinclediere.com/)
As an experienced designer, Robin's portfolio boasts an elegant layout with a seamless flow, eliminating distractions.

## 4) [Timothy Achumba](http://timothyachumba.com/)
Timothy's portfolio resembles a high-end fashion magazine layout, bringing an air of sophistication to your screen.

## 5) [Grace Larosa](http://grace-larosa.com/)
Grace's portfolio exudes elegance and refinement. Similar to Robin's, it flows gracefully, offering just the right blend of style without overwhelming.

## 6) [Damjan Stankovic](https://www.damjanstankovic.com/)
Damjan's portfolio stands out with its innovative approach. His homepage proudly showcases his projects, and the navigation element in the bottom right adds a unique touch.

## 7) [Ram Maheshwari](https://www.rammaheshwari.com/)
Ram's portfolio can be described in one word: clean. It presents everything with crystal-clear precision, combining features and functionality seamlessly.

## 8) [Jonnie Hallman](https://destroytoday.com/)
While simplicity is often praised, Jonnie's portfolio is an exception. It impresses with beautiful animations that seamlessly enhance the user experience.

## 9) [Karolina Szczur](https://thefox.is/)
Karolina's site is organized into different pages, which works brilliantly. Each section has its dedicated space, making it easy for visitors to find what they need.

## 10) [Matt Farley](https://mattfarley.ca/)
Matt's portfolio has served as a significant inspiration to me. It is fun, friendly, and informative, providing everything you need to know about him and his projects, along with testimonials from peers— all just a scroll away.

🌟🌟🌟

Thanks for reading my first DEV post! I'm excited to share my learning as I journey into the world of web development.
I made Simba using pure CSS
Tags: codepen, css, webdev
Published: 2023-10-01 19:22:49

Hey folks 👋

I made Simba using only CSS. Check out this Pen! Like or dislike? Please, share your thoughts 🙏

{% codepen https://codepen.io/melnik909/pen/qaOwqV %}

Full code: https://codepen.io/melnik909/pen/qaOwqV
Step-by-Step Guide: Creating Your First React App
Tags: react, reactjsdevelopment, webdev, javascript
Published: 2023-10-01 19:19:09

Setting up your first React project involves several steps. Here's a step-by-step guide to help you get started:

**1. Install Node.js and npm:**
   - React requires Node.js and npm (Node Package Manager) to manage dependencies. Download and install them from the official website: https://nodejs.org/

**2. Create a New Project Directory:**
   - Create a new directory for your React project using your terminal or command prompt:
     ```
     mkdir my-react-app
     cd my-react-app
     ```

**3. Initialize a New React Project:**
   - Inside your project directory, initialize a new React application using `create-react-app`, which is a popular tool for creating React projects with a pre-configured setup:
     ```
     npx create-react-app .
     ```
     The `.` at the end of the command makes sure that the project is created in the current directory.

**4. Start the Development Server:**
   - After the project is created, you can start the development server by running:
     ```
     npm start
     ```
   - This command will start the development server and open your React app in a web browser at `http://localhost:3000`.

**5. Explore the Project Structure:**
   - React projects created with `create-react-app` come with a predefined project structure. Familiarize yourself with important directories and files such as `src/`, `public/`, and `package.json`.

**6. Write Your React Components:**
   - You can start building your application by creating React components inside the `src/` directory. The entry point for your application is usually `src/index.js`.

**7. View Your App in the Browser:**
   - As you make changes to your React components, the development server will automatically reload the app, allowing you to see the changes in real-time.

**8. Install Additional Packages:**
   - Depending on your project's requirements, you may need to install additional npm packages for routing, state management, styling, etc. You can install these packages using `npm install package-name`.

**9. Build for Production:**
   - When you're ready to deploy your React app, you can create a production-ready build by running:
     ```
     npm run build
     ```
   - This will create an optimized build of your app in the `build/` directory.

**10. Deploy Your App:**
    - You can deploy your React app to a web server or hosting platform of your choice. Popular options include Netlify, Vercel, GitHub Pages, and AWS.

That's it! You've successfully set up your first React project. You can now start building your web application using React components and exploring the vast ecosystem of React libraries and tools to enhance your development process.
Ayu's Hacktoberfest 2023 Pledge
Tags: hacktoberfest23, opensource, codenewbie
Published: 2023-10-01 18:41:48

<!-- ✨This template is only meant to get your ideas going, so please feel free to write your own title, structure, and words! ✨ -->

![cute pumpkin GIF](https://media.giphy.com/media/3oz8xLSmmt0WaMTbX2/giphy.gif)

Hi friends!

I'm Ayu, a tech blogger, front-end developer, open-source contributor, and tech community enthusiast based in the Netherlands. 👋

I've been a member of the DEV community for 4+ years, and I'm part of the moderator team here on DEV and CodeNewbie!😄

<!-- If you are new to the DEV community, introduce yourself and share a bit about why you joined DEV and your motivation to participate in Hacktoberfest this year. -->


## Background

This year marks the 5th Hacktoberfest that I'm participating in! 🤩

This time, I want to go beyond by supporting beginners in open source! So, here comes my pledge.

 
<!-- Are you brand new to Hacktoberfest? What about coding in general? Tell us about how you got here -->


## Pledge

### One Accepted PR

As in previous years, I aim to have at least 1 PR accepted for Hacktoberfest.

I know, I know. You might be saying, "Are you serious? We need **four** PRs to complete Hacktoberfest."

This is what works for me. Setting achievable and realistic goals gives less pressure and makes me enjoy the process. And when I've achieved it, I constantly challenge myself to do more.

So, if you are wondering, I completed each of my previous Hacktoberfest, and in the last three years, with more than four PRs accepted and merged during the events. 😊

### Supporting Open Source Community

This year, I want to focus more on supporting the open-source community, especially beginners in open-source.

I will guide and answer open-source questions for anyone who needs one. And I'm volunteering in some tech communities — [Virtual Coffee](https://virtualcoffee.io/), [OpenSauced](https://opensauced.pizza/) and [SheSharp](https://www.shesharp.co/) — to be a mentor, a Hacktoberfest support, and a maintainer during the event.

![Cat put on sunglasses with text I'm ready GIF](https://media.giphy.com/media/CjmvTCZf2U3p09Cn0h/giphy-downsized.gif)

<!-- Share with us a bit about your goals during your Hacktoberfest journey this month. Are you a contributor looking to hone your skills or a maintainer looking for some help on a new project? Let us know!  -->