Data Science for Beginners: 2023–2024 Complete Roadmap.
Tags: python, datascience, beginners, programming
Published: 2023-10-02 08:20:25

In a recent EastAfrican datascience bootcamp, a speaker asked attendants in a google meeting, what had initially piqued their interest in data science. One lady raised their hand and answered, ”Uhm..because data is the new gold.” Was she right? Yes! A huge yes. 
Data Science is such a driving force behind a majority of innovations in the world today due to its ability to extract valuable insights from datasets and make informed decisions that aid in problem solving. Furthermore, according to a Business Havard Review, the role ‘Data Scientist’ is the ‘The Sexiest Job in the 21st century’.
It is also a field that continues to evolve everyday and one must keep up with the latest news and trends. That is why a roadmap is important.

![An image showing various terms in data science](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ktvbx0fxbei7j26p8yi5.jpg)
## What is Data Science? 
It is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information.

Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in:

1. Domain Knowledge:- Take for instance you want to be one in the bank sector knowing about finance, insurance , credit risks e.t.c will be important information when drawing conclusions and tackling problems.

2. Mathematical Skills:- Linear Algebra, Calculus, probability and Statistics help us in understanding algorithms and perform data analysis.
3. Communication skills:- It includes both written and verbal communication. Data Science involves some form of communication of project findings.
## The Roadmap

![A graphical representation of the roadmap](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/knu3b2lmqusow0whcsn1.png)
## 1. Mathematics
In data science, statistics is essential. It provides one with mathematical ideas necessary for conducting hypothesis testing and data analysis, as well as for making decisions based on the given data. Some significant applications of statistics in data science are listed below:
- Data scientists can quickly summarize and define a dataset’s characteristics by using descriptive statistics. Included in this are statistics like mean (average), median (middle value), mode (most frequent value), variance (spread), and standard deviation (average deviation from the mean). For a basic understanding of the data, descriptive statistics are helpful.
- Making assumptions or projections about a population based on a sample of data is known as inferential statistics. Regression analysis, confidence intervals, and hypothesis testing are some of the methods used in this.

Probability theory is crucial in data science for modeling uncertainty and randomness. It’s used in various applications, such as Bayesian statistics for machine learning. Others : Linear Algebra, Vector Calculus
## 2. Programming Skills
The most recommended languages are R and Python. While R is not mandatory, it is valuable for statisticians.

In python, familiarize yourself with numpy, pandas, matplotlib, visualization and analysis. Object oriented and procedure oriented python exercises will improve your mastery. Python offers several built-in data structures like lists and tuples that allow you to store and manipulate data efficiently.
## 3. Structured Query Language(SQL)
It plays a crucial role in data science, particularly in the context of working with structured data stored in relational databases. It is useful in :

1. Data Cleaning

2. Data Retrieval

3.Data Aggregation
Can be found [here](https://www.kaggle.com/learn/intro-to-sql).

## 4. Data Visualization
Understand the principles of data visualization and storytelling with data. Tools like Matplotlib, Seaborn, and Tableau can help you create compelling visuals.
## 5.  Machine learning concepts
One at least needs to understand basic algorithms of Supervised and Unsupervised Learning. There are multiple libraries available in Python and R for implementing these algorithms. [Kaggle Machine learning](https://www.kaggle.com/learn) courses are very helpful here.
## 6. Practice pratice practice!
In the process of doing this, create repositories for your work and track your progress. Collaborate on projects, network with like-minded people, share your insights on social media and engage in discussions.

Don't forget to refer to a graphical roadmap above .
All the best!