Data Science for Beginners: 2023–2024 Complete Road Map.
Published: 2023-11-02 21:43:39


![Data Science Intro](https://cdn-images-1.medium.com/max/2000/1*PmnBVanRlOPtVkXcD9G4YQ.jpeg)

## Introduction

We have all heard people introduce themselves as data scientists, and you wonder, what do these guys do? Then you went online to look at job descriptions, and you realized that there are many different descriptions, so you gave up. Or this is your first search, and you want to know what exactly data science is. Here you will know what it all involves, and find a map to lead you from your noviceship to a complete baller.

## What is Data science?

Basically, Data science involves finding hidden insights, patterns and useful information from large amounts of data. Its like having a pile of puzzle pieces, then arranging them in order to reveal a figure that would make sense. This would involve a process, which you have to trust.

 1. data collection: where the large pile of puzzle pieces are sourced

 2. Data cleaning: The puzzle pieces that wouldn’t finding the desired figure are removed.

 3. Exploration: This would mean looking through the pieces to find unique pieces that will help solving the puzzle, for example, finding the corner piece

 4. Analysis: In an actual data science tasks, Mathematical formulas and computational algorithms are utilized to uncover patterns.

 5. Visualization: To make their findings easy to understand, data scientists often create visual representations of the data, such as graphs or interactive dashboards.

 6. Decision Making: The whole idea is to find insights and patterns to inform decisions, so at this stage, the decision is made, backed by data findings. You decide which puzzle piece to place where.

The puzzle analogy might not work well, but the point is finding actionable information from raw data.

I know what you’re thinking. You want to start learning, but you don’t know where to start. well, that’s why I’m here.

## The Roadmap

### Master the basics

 1. Math
    You tend to go back to the drawing board less when you get the basics right. The foundations of data science lie in Math and statistics. Understanding inferential and descriptive statistics and Math concepts like calculus and probability will go a long way to helping you grasp more complex data science concepts.

 2. Programming
    These math concepts will be presented to a computer in for, of code through something called a programming language. Python is the most prefered language for many reasons, among which is its extensive library, large support community and quite friendly to beginners.

### Specialize

One the basics are well mastered, start learning data related tasks. These include:

* **Data wrangling **: Involves collecting data from different sources, cleaning it and preprocessing.

* **Data Visualization**: Using Libraries like Matplotlib and Seaborn to visually represent the data.

* **Machine Learning Basics**: Know how to make use of basic Supervised and unsupervised Machine Learning algorithms where predictive methods are needed. A famous Library for this is [Scikit-Learn](https://scikit-learn.org/stable/getting_started.html)

![I'm something of a data scientist myself](https://cdn-images-1.medium.com/max/2000/1*qcDnhKgAqVYbJ5_YR59dIA.jpeg)

### **Level Up**

At this level, the best way to learn is by working on actual data. There are many dataset repositories where you can find data to work with. Many prefer [Kaggle.com](http://kaggle.com), because it comes with an environment for running notebooks and a lot of resources, including other peoples’ code which you can learn from.

### Advanced Data Analysis

At this point, your data analysis is at a whole new level. You can unravel mysteries from large datasets, applying statistical hypothesis testing and other dangerous words👌.

### Feature Engineering

From the data you have, you can select columns that help the goal, get rid of those that don’t or create new ones by combining or performing other operations on the existing data. This requires good knowledge of the data and often, some domain knowledge helps.

### Machine Learning

Explore more complex machine learning and their under-workings. This is where most of the calculus comes into play. Its easier to understand and apply algorithms like Logistic regression, Support Vector Machines or even Neural Nets.

Here, explore deeper into Scikit-Learn and into other Machine Learning libraries like Tensorflow or Pytorch. You could also explore Natural Language processing using the Natural Language Toolkit (NLTK).

![Advanced data scientist](https://cdn-images-1.medium.com/max/2000/1*hvKHYHOufM7-8061U2eqDQ.jpeg)

## Finally

Once you start, the only way is up. Build and deploy data projects. Join communities where you meet other people who have taken different paths to the same destination, learn from them. Create networks, know people everywhere, this is quite an understated way of getting opportunities and help when you're stuck

Ultimately, data science offers an incredible opportunity to make a meaningful impact in various industries, from healthcare to finance, and beyond. Whether you aspire to be a data scientist, data analyst, machine learning engineer, or specialize in a specific domain, your dedication to mastering this field will open doors to a world of possibilities.

So, embrace the data science journey, adapt to new challenges, and never stop learning. Your future as a data science professional is full of promise, innovation, and the potential to shape a data-driven world.
>  ps. This last 2 paragraphs are generated, as a demonstration of what can be achieved.
>  Happy Learning🥳🥳🥳