DATA SCIENCE FOR BEGINNERS: 2023 – 2024 COMPLETE ROAD MAP
Published: 2023-10-04 15:41:20


Today, it is difficult to escape the noise around data science, data analytics, artificial intelligence, python, SQL and so on. And quite frankly, this buzz can be overwhelming especially if you are considering a career in data science. It that is how you feel, do not worry. This article will break things done for you coupled with a road map on how you can begin your career in this domain. 
First of all, let us clarify the difference between Data Science and Data Analytics. Data science and data analytics both involve working with data, but they differ in scope and objectives. 
Data science is broader, encompassing data collection, analysis, machine learning, and predictive modelling for complex, open-ended problems. It requires programming and algorithm development skills. 
Data analytics on the other hand is narrower, focusing on examining data to answer specific questions and provide immediate insights. Analysts use tools like Excel and SQL, emphasizing descriptive and diagnostic analysis. 
While their techniques may overlap, data science aims for deeper understanding and predictive modelling, while data analytics aims to provide actionable recommendations based on historical data for specific context

Now that you have a fundamental understanding of data science and data analytics, here is a step-by-step guide to help get started 

**Module 1: Introduction to Data Science**

•	Under this module try and get basic understanding of what data science is and its relevance in various industries.
•	Learn a programing language like python (recommended)
•	With python, learn the fundamental with focus on variables, data types, loops and basic functions 


**Module 2: Data Manipulation and visualization** 

For data manipulation learn;
•	Pandas (Python library for data manipulation).
•	Numpy
In addition, learn how to load, clean, and explore datasets
For data Visualization learn;
•	Study data visualization with libraries like Matplotlib and Seaborn.
•	Create basic plots and charts to visualize data


**Module 3: Mathematics, Statistics and Probability**

•	In this area you should learn descriptive statistics, central tendency, and variability.
•	Also explore concepts like mean, median, mode, variance, and standard deviation
•	Understand probability theory and common probability distributions.
•	Focus on normal, binomial, and Poisson distributions.
•	Study inferential statistics: hypothesis testing, confidence intervals.
•	Learn about descriptive statistics and exploratory data analysis (EDA).
•	Apply statistical concepts to analyse datasets.

**Module 4: Machine Learning Basics**

•	Understand supervised and unsupervised learning.
•	Learn about regression, classification, and clustering.
•	Get hands-on experience with Scikit-Learn for simple ML tasks

**Module 5: Data Management Systems**

In this module, learn SQL which stand for Structured Query Language. This is used to access and manipulated datasets in databases. In addition, learn database management systems like MySQL, Microsoft SQL Server etc 
Module 6 and beyond
•	Start building data science projects
•	Apply what you have learned to real-world problems.
•	Write articles or reports about your learning experiences and projects, which can help showcase your skills.

**Conclusion**
I hope this basic road map will help you get started in your career