Data Science for Beginners📊: 2023-2024 Complete Roadmap✅
Tags: beginners, datascience, luxdatanerds, luxdev
Published: 2023-10-02 21:50:47


Are you new to **Data Science?** Do you want to build a career along the field of data? This article is certainly for you.  

![Data Science](https://uwo.ca/sci/datascience/img/Placeholders/Data%20Science%20Slide.jpg)  
>" **Data Science** can be described as a field of study that involves manipulating and analyzing raw data or complex data sets using statistical computing methods and machine learning techniques to draw insights in order to make data-driven decisions."  
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Data Science is one of the fast growing fields of the 21st Century. Data science has rapidly evolved in the recent era due to increased amounts of data in big companies and the corporate world which must be analyzed in order to provide data-driven business decisions.  
The field is immensely lucrative with a good pay even for the entry level personnel. Individuals who work in this field must be able to work with Big Data in order to provide solutions to the ever changing technology and the business sector.
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The increasing value for data scientists lies within the endless need of businesses to harness large amounts of data in order to come up with a viable present and future solutions. Therefore, data science provides the conduit between raw data and business insights. It allows for the manipulation of large raw meaningless data stored in databases to extract meaningful value.
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Data science is therefore one of the key drivers of today's economies as most of the steps being taken by government's and organizations to combat modern problems rely on keen analysis of data. Data Science can therefore be used to predict the future occurrences and trends. 

However, for one to really have a thriving career in this field, it must be instigated by passion for problem-solving rather than the money motive. One must be able to commit themselves into learning. There are numerous online courses, tutorials and YouTube videos where one can be able gain enough knowledge on data science.  
There are also various bootcamps where one can kickstart their their science career. For in instance, bootcamps organized by **[Lux_Academy](https://www.youtube.com/results?search_query=lux+academy)** would be a good starting point   

## **Data Science Essentials📚** 
Any beginner wishing to venture into the field of data science must first equip themselves with the elementary building concepts that are inexpendable as far as data is concerned.  

Data Science is an increasingly dynamic field, therefore ensuring a progressive learning approach for a beginner is highly recommended. Data Science is a multidisciplinary sector made up of a triplet domain:  

>1. Mathematics
>2. Statistics
>3. Programming  

It is important to note that these three domains are correlated and intertwined. Therefore understanding the three and having a concrete knowledge is very vital. 
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##  **Mathematics**
Mathematics in data science helps one to choose appropriate procedures and diagnose problems
appropriately. It is one of the core building blocks of data science due to the fact that data comes in unusual formats. Some key components of maths one should pay close attention to include:  
>a) [Probability](https://www.w3schools.com/ai/ai_probability.asp)  
>b) [Calculus](https://www.w3schools.com/ai/ai_statistics.asp)  
>c) [Linear Algebra](https://www.w3schools.com/ai/ai_algebra.asp) 

### a) Probability
**[Probability](https://www.javatpoint.com/probability)** is the likelihood of an occurrence of an event. Data science uses the various types of distributions such as **normal distribution, Bernoulli distribution* and *uniform distribution** to predict the likelihood of an occurrence.

### b) Calculus
**[Calculus](https://www.khanacademy.org/math/calculus-1)** in mathematics can be simply defined as the study of instantaneous or continuous the rate of change. **Optimization** and **integration** are important areas in Calculus that are very key in data science.

### c) Linear Algebra
**[Linear Algebra](https://www.khanacademy.org/math/linear-algebra)** is a mathematical field concerned with linear equations. **Vectors** represent data points while **scalars** represent numerical values in data science. However, there are a whole lot of Linear Algebra such as matrices, mappings and many more which are covered in Data Science.

## **Statistics**
Statistical concepts such as hypothesis testing, p-values, mean and average are key to data science. **Descriptive statistics** helps one to describe the characteristics of a dataset, making it easier for one to understand and interpret data while **inferential Statistics** is used for making estimates about a population and hypothesis testing.

## **Programming**
I would be obliged to say that programming is the most important cornerstone concept of data science.  
Whichever the programming language of choice, you must be able to have a solid knowledge of the language's variables, loops, functions and datatypes.  
The ability to work with databases relies on a strong background of programming. There has always been a debate about which language should one learn or which language should precede the other. Is it **Python, R or SQL?**
There are various languages to work with various types of datasets and databases but i will only outline the major languages that cut across the field. Different companies also prefer different languages which lays responsibility for one to be conversant with the key languages.

### - [Python](https://www.python.org/about/gettingstarted/)
Python is essential in data science due to it's flexibility and the availability of its huge libraries which enables data scientists to easily work with data. Python's syntax is also easy to grasp. The key Python libraries for Data Science include **[Pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/) and [NumPy](https://numpy.org/)**. Python consists of many libraries in addition to the three.   
You should be able to understand basic python like its syntax and various data structures such as lists, tuples and lists.  
## **[R](https://www.geeksforgeeks.org/r-programming-for-data-science/)**  
R is an open-source language which provides a wide range of statistical and graphical techniques for exploring, analyzing, and visualizing data. R offers powerful data manipulation and transformation capabilities, allowing users to clean, reshape, and prepare data for analysis.  
The **"dplyr" and "tidyr"** packages, for example, are popular tools for data wrangling.  R provides a variety of machine learning libraries and packages e.g. **caret, randomForest, xgboost** that allow data scientists to build and evaluate predictive models.


### - [SQL](https://www.w3schools.com/sql/)
**SQL** or **[Structured Query Language](https://www.w3schools.com/sql/#gsc.tab=0)** is a query language that is used for interacting with relational databases.  
You may be asking yourself now **what is a database or what is to query?**
A **database** is just a collection or related data which are organized in tables and columns. A phonebook for instance is a database because it has a collection of all your contacts which have similar characteristics like the phone number and a name, very simple.  
To **query** is to simply issue a request or a command to the database for a specific information. Therefore, **SQL** is used to interact with databases through a **[Database Management System (RDMS)](https://www.tutorialspoint.com/dbms/index.htm)**  
The main purpose of a RDMS is to perform the **CRUD** operations **(Create, Read/Retrieve, Update and Delete)** data on a database.
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## Machine Learning  
**[Machine learning](https://www.simplilearn.com/tutorials/machine-learning-tutorial)** is one of the fields of Artificial Intelligence which primarily involves development of models and algorithms that enable machines to learn, adapt and communicate with each other.  
In the field of data science, machine learning is a valuable tool that enables extraction of patterns and insights from datasets. There are various types of machine learning such as **supervised, unsupervised and deep learning.**
### Data Visualization
After performing the various operations on data, effective visualization is essential in communicating the insights.  Various data visualization tools such as **[Tableau](https://www.tutorialspoint.com/tableau/index.htm)** enable one to provide data visualizations that are easily understood by everyone.
 One must also be able to present the insights in a fluent and appealing way to the respective audience. Therefore, data science does not entirely rely on the technical skills of working with data.  
Communication skills is key for a data scientist as insights must be relayed with utmost fluency.  
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Finally, it is very crucial for one to build up their **portifolio** while learning. One should commit themselves to building projects and writing articles about data science. These articles and projects go a long way on increasing your probability of securing a career in a data related field.  
**[Github](https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners)** is a good starting point where you can create an account and include all your articles and projects in repositories. One can also use **[Kaggle](https://www.kaggle.com/)** where you can interact with fellow data scientists and get access to large amounts of data sets.