Data Science for Beginners: 2023 - 2024 Complete Roadmap.
Tags: beginners, codenewbie, learning
Published: 2023-09-28 20:40:29

-Data Science is a field that involves extracting insights and knowledge from data using various techniques and tools. If you are a beginner in Data Science, here are some steps you can follow to get started;

- Prepare your workspace

-To learn DATASCIENCE right, you should have an IDE installed on your local machine. Suggestions will be a marketplace with many options and few improvements from one platform to another. These include;
  -Anaconda
  -Google collab
  -Pycharm
  -Mysql for databases

- Learn Programming
    -Python language which is the most used, resources like Codecademy or Python.org can be helpful. Under Python, we have libraries like NumPy, Pandas, and Scikit-learn used in data science.
    -R language
- Learn Statistics
    -Brush up on essential math concepts, particularly linear algebra, calculus, and statistics such as mean, median, variance, and standard deviation. Khan Academy and Coursera offer excellent courses on these topics.
   
- Learn Data Visualization
    -Dive deeper into data visualization with tools like Tableau, Power BI, or Python libraries such as Plotly and Seaborn.
- Learn Machine Learning
   -Understand the basics of supervised and unsupervised learning, as well as common algorithms like linear regression decision trees, and also reinforcement learning.
  
- Practice with Projects
  -Begin working on small machine learning projects to apply what you've learned. Kaggle provides numerous datasets and competitions to practice.
- Learn from the Community
    -Join data science communities on platforms like LinkedIn, Reddit, Twitter and GitHub. Attend meetups and conferences to connect with professionals in the field.
- Continuously Learn -Stay updated with the latest trends and research in data science by following blogs, podcasts, and academic journals.

### Data Science Life Cycle

- It is a methodology followed to solve the data science problem.

1. Business Understanding
2. Data Understanding
3. Preparation of Data
4. Exploratory Data Analysis
5. Data Modeling
6. Model Evaluation
7. Model Deployment

![Data Science Life Cycle](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/n3kp3gmm0jmxi8qip9x5.png)

### Applications of Data Science

-There are many applications of data science as follows:- 

1. Search Engines 
2. Transport, Finance
3.  E-Commerce 
4. Health Care 
5. Image Recognition
6.  Targeting recommendations


### Prerequisites & Tools for Data Science
-To be precise to gain expertise in the field of data science. firstly, you need to have a strong foundation in various aspects of data science. which includes knowledge of query languages like:- SQL, programming languages like R and Python, and visualization tools like:- PowerBI, Quilsense, Quilview, and Tableau. Additionally, having a basic understanding of statistics for machine learning is crucial. To effectively apply machine learning algorithms, it is essential to practice and implement them with use cases relevant to your desired domain. 
Best of luck on your journey, and may you find success and fulfillment in the fascinating world of data science in 2023 and beyond!