Data Science for beginners 2023 - 2024 Complete Roadmap

Data Science is an Interdisciplinary field that focuses on analyzing massive amounts of data to automatically identify inherent patterns, extract underlying models, and make relevant predictions.
It Impacts virtually all areas of the economy, including science, engineering, medicine, banking, finance, sports and the arts hence offering endless opportunities.
With the right Data science Roadmap, dedication, practice and mentorship, one can become a good data scientist.
This Roadmap provides a solid foundation you can rely to kickstart your Data science Career.

1. Understanding the basics:

Involves understanding what you want to become: a data scientist, understanding the roles and career paths in this field.
Some career paths in Data Science include;

• Data Analyst
• Data Engineer
• Machine Learning Engineer
• NLP Engineeer
• Business Analyst
• Power BI Engineer

• Data Scientist
  Major Roles of Data Scientists are;

• Identify valuable data sources and automate collection processes

• Undertake preprocessing of structured and unstructured data

• Analyze large amounts of information to discover trends and patterns

• Build predictive models and machine-learning algorithms

• Combine models through ensemble modeling

• Present information using data visualization techniques

• Propose solutions and strategies to business challenges

• Collaborate with engineering and product development teams

2.Mathematics

Mathematical knowledge is essential for data science since it is the building foundation of Machine Learning and Data analysis.
learn;

• Statistics
• Probability
• calculus

3. Learn Programming

Understand and get hands-on experience in programming languages to implement various algorithms.

• Python
• SAS
• R

4. Data Science tools

Familiarize yourself with Data science tools such as Jupyter Notebooks, Kaggle Notebooks, Google Colab and their environments for interactive coding and Git for Version Control.

5. Database Knowledge

Have a sound database knowledge to deal with the structured data stored in RDBMS.
Learn Data Manipulation Language, Data Definition language and Data Control Language. Get to know about different Database Versions and Types such as;

• MYSQL
• Oracle
• Cassandra

6. Data Engineering

Master data engineering skills to clean and process a massive amount of data to avoid missing values
Gain Data Engineering Skills such as;

• Data Processing
• Data Wrangling
• SQL

Take Away: Build atleast 2 Projects

7. Machine Learning

Learn and implement machine learning algorithms to create predictive models.

• Supervised learning
• Unsupervised learning
• Reinforcement learning

8. Deep Learning

Get a thorough understanding of Deep Learning and its algorithms to work with vast volumes of unstructured data.
Focus on;

• TensorFlow
• Artificial Neural Network
• Deep Belief Network
• Generative Adversarial Network

9.Big Data

Get an added advantage by learning Hadoop and Spark to easily store, process and manipulate data.

10. Data Visualization

Master different data visualization tools to build interactive plots and dashboards to derive business insights.
Learn the following;

• Tableau
• Excel
• QlikView
• Power BI

Take away: Build atleast 2 projects

11. Build a Portfolio

A Portfolio is a collection of projects that a professional has worked on. Data Science portfolio can help you showcase your skills and credibility, and also helps you highlight your strengths and abilities.
The best part about building a data science portfolio is that there is no shortage of datasets that you can use to get started.
Get a good platform to host your portfolio. Example, GitHub.

12.Personal attributes

• Good Communication Skills
• Good Story telling
• Persuasion skills

13.Get Certified

Gives you better chances of being hired.

14. Apply for Jobs

Apply for Junior Roles as Data Scientist or any data Science Career Path of your choice depending on your abilities.
Happy Learning, Cheers!