Data Science for Beginners: 2023 - 2024: Complete Roadmap
Published: 2023-10-10 10:56:22

## Why Data Science

Data science has become a rapidly growing field in the tech industry. Nearly every organization requires the input of data scientists to help process and generate insights from the available data. The demand for data scientists is also rising due to the advent of big data being used in such organizations. Therefore, it is vital to understand how professionals can keep up with organizational needs and learn data science skills. This article will explain the complete data science roadmap a beginner can use to master and become an experienced data scientist.

**What you should learn:**

### Applied Statistics and Mathematics
Since data science involves data analysis to obtain meaningful info, learning statistics, and mathematical concepts is essential. The basic mathematical concepts include linear algebra, probability, and calculus. Linear algebra will be helpful when applying linear equations, vectors, matrices, operations, sets, logarithms, exponential functions, eigenvalues, eigenvectors, and Principal Component Analysis (PCA) to reduce vector dimensionality. Additionally, the probability will be helpful when calculating the likelihood of events and applications in the Bayes Theorem. Calculus is also a fundamental mathematical concept in determining the characteristics of derivatives and integral functions. 

The statistical phase of data science is also essential as it helps in data exploration and understanding data characteristics. For example, descriptive statistics helps in understanding essential data characteristics. The measures of descriptive statistics include mean, median, mode, central tendency, range, standard deviation, variance, and correlation. Similarly, inferential statistics is vital in gaining more insights from descriptive statistics. The main applications of inferential statistics include parameter estimation and hypothesis testing.

### Programming
Programming skills are essential in data science as data scientists use computer programs and programming languages to manipulate data. Although there are different programming languages to choose from, it is also crucial to understand the various programming concepts. These concepts include data structures, control structures, and Object-Oriented Programming (OOP) concepts. Furthermore, an aspiring data scientist should learn about Python and R, the two most powerful open-source languages in data science. Learning about these languages should involve familiarizing with the syntax and associated libraries in data science. Likewise, learning SQL is necessary for querying data from different databases.

### Integrated development environment (IDE)
The IDE is a software offering development features. There are different IDEs, each with unique characteristics. However, a data scientist must choose an IDE that fits their needs carefully and with a user-friendly interface. Examples of IDEs include JupyterLab, Spyder, Atom, R Studio, and Visual Studio Code.

### Data Wrangling (Exploratory Data Analysis)
Since data science relies on data, it is essential to learn the different steps in cleaning and preparing the data for further analysis. These actions may include changing data types, removing inconsistent values, and imputing missing values. Similarly, it is crucial to learn how to perform EDA to uncover hidden patterns in data before further analysis. 

### Machine Learning
Machine learning is also crucial in learning data science as it separates data analysts and data scientists. Machine learning uses AI algorithms to train data and draw inferences from the data. Some important ML algorithms for data scientists include linear and Logistic Regression, Support Vector Machine, Random Forest, kNN, and XGBoost.

## What Next
The above skills will be necessary for a beginner data scientist who wants to grasp data science basics. While this roadmap provides the crucial steps of becoming a data scientist, it is best to track the learning process and be aware of the changes and needs of the industry to align the skills with business requirements.