Data Science for Beginner:2023-2024 Complete Roadmap
Tags: programming, datascience, beginners
Published: 2023-09-30 23:38:21

Data Science is a field of study involving statistical tools and techniques to extract meaningful insights from data. Data science has become a major part of the modern-day business world, as it helps organizations make informed decisions based on logic and reason rather than intuition alone. 

Data scientists work with structured, semi-structured, and unstructured data to find patterns that help them solve problems and predict future events. Data scientists work in teams with other members of their organization and often have advanced computer science or mathematics degrees. However, several data sciences for beginners' courses are now available, which teach individuals the basics of data science so they can get started immediately if they wish
**<u>What is data science?</u>**
Data science is a field of science  which involves extracting knowledge and insights from large and complex datasets using various techniques, such as data mining, statistical analysis, machine learning, and visualization. Traditionally, data science has been associated with specialized skills and technical expertise, requiring a strong background in mathematics, statistics, programming, Data cleaning and formatting, Data visualization and domain knowledge.
**<u>How to start learning Data Science</u>**


**1)Choosing the Right IDE**
For an effective data science learning experience, selecting a suitable Integrated Development Environment (IDE) is crucial. 
- PyCharm
-Anaconda
-Google Collab
-MySQL for databases

**Version Control**
Git
**2)****Foundational Skills**

. Mathematics and Statistics
Linear algebra
Calculus
Probability
Statistics

.Domain knowledge

Domain knowledge empowers data scientists to extract meaningful insights and create more impactful solutions in specific industries or problem domains. The combination of both technical and domain expertise often leads to more successful and impactful data science projects.

.communication skills
Data scientists need strong communication skills to bridge the gap between technical expertise and the understanding of business stakeholders. The ability to convey complex concepts, collaborate effectively, and present findings in a compelling manner is key to the success of data science projects

**3)** **Programming Languages**:
Learn a Programming Language
Python or R,SQL are commonly used in data science.

**4) Learn Data Manipulation and Visualization**
Data Manipulation
learn  Numpy and Pandas python libraries for easy manipulation of data
Data Visualization:
Learn Matplotlib/Seaborn Libraries for python that are used for visualizing data or Dive  into data visualization with tools like Tableau, Power BI
**5)  Data Cleaning and Preprocessing**
Techniques for handling missing data, outliers,
**6)** **Machine Learning**
Machine Learning is a crucial part of data science, and it's always at the forefront of research. Every year, researchers make new discoveries and improvements in this field, keeping it dynamic and ever-evolving
Learn and understand the fundamentals Supervised Learning  .learn its common algorithm such as
Regression
Classification
Unsupervised Learning and its common algorithm
Clustering
Dimensionality Reduction
**7)** **Deployment**
Deployment is an essential aspect of a data scientist's journey, irrespective of whether they are beginners, mid-career professionals with 5+ years of experience, or seasoned experts with over a decade in the field. It serves as a tangible testament to the effort and expertise invested, demonstrating a practical application of skills and affirming the value of the work performed. 

Microsoft Azure
Heroku
Google Cloud Platform
Flask
DJango

**8) keep practicing with projects**
Start putting your learning into action by taking on small machine learning projects. A great platform for this is Kaggle, where you can find a variety of datasets and participate in competitions to apply and reinforce your skills. This hands-on experience with real-world data will help solidify your understanding of machine learning concepts and enhance your problem-solving abilities.


**Data science/Machine learning lifecycle**
1. Gathering Data
2. Data preparation
3. Data Wrangling
4. Analyse Data
5. Train Model
6. Test Model
7. Deployment