Data Science for Beginners: 2023 - 2024 Complete Roadmap
Tags: datascience, sql, ai, python
Published: 2023-10-01 10:50:52

## **Introduction**
When starting a career as a Data Scientist, following the correct guide and/or roadmap can save a lot of time when learning. Googling random stuff online and deciding what to learn first or what not to learn can be daunting. So that you can save time while learning Data Science, I have curated a simple roadmap below.

1. **Get to Know the Basics**
Before you learn anything, it is important to understand what the subject entails from the surface. You can achieve this by skimming through books, articles, or blog posts. With respect to Data Science, you would need to understand what the field involves. For example, you may check out definitions, applications of Data Science, skills required in the industry, what Data Scientist do on a normal day and the tools they use to get work done. You may also want to understand the various roles within the Data Science field such as a Data Analyst, Data Engineer, Machine Learning Engineers, Analytical Engineers and so on. 

2. **Learn a Programming Language**
Data Science is a multi-disciplinary field that combines knowledge from more than one field. Notably, Data Science leverages domain knowledge, statistics, and programming. Domain knowledge refers to the background knowledge relating to the field where the techniques of Data Science are being used or applied. The need for domain knowledge is essential as Data Science can be applied in various fields to provide solutions. Statistics is at the core of data science. This is because, Data Scientists collect and analyze large amounts of data and on top of that, they are expected to provide reports and findings. To achieve this, they often leverage a myriad of statistical methods (e.g., mean, medians, linear regressions, statistical inferences, etc.). Data Scientists often use programming languages to quickly enable them analyze and extract value from data. Common programming languages used by Data Scientists include Python and R. Higher preference is however given to Python programming language due to it versatility, ease of learning, and due to the fact that it is backed by a large community of developers. Since Python has a huge community support, it has many libraries and tools that are used by Data Scientists today for data analysis, visualization, and developing machine learning models. Below are some of the recommendation you may consider while learning Python.
{% embed https://youtu.be/rfscVS0vtbw?si=GbP22qY4NLqNgYFp %}

3. **Learn SQL**
The work of Data Scientists is to extract value from data. In most cases, this data is stored in databases. Therefore, Data Scientist are expected to gain skills for working with databases the entry point of which is to learn Structured Query Language (SQL). When collecting or analyzing data, Data Scientist often find it easier to work with SQL especially when dealing with structured data. With SQL skills, working with structured data, querying databases, and preparing data for experiment becomes easier. SQL can easily work with programming languages (particularly Python) using libraries or adapters for your preferred database. Below is a resource you may consider for learning SQL.
{% embed https://youtu.be/HXV3zeQKqGY?si=tB-r7BxvJkIR6iK7 %}

4. **Learn Big Data Technologies**
Data Scientists may be prompted to work with large datasets that may be too large or complex to be handled using traditional data processing software tools. This is where Big Data Technologies come in handy. These technologies are a set of software tools that Data Scientists to manage large datasets and transform them into actionable insights. Some of these tools include; Apache Spark, Apache Hadoop, MapReduce, and Tableau. 

5. **Practice, Practice, and Practice**
To be a Data Scientist, you will need to practice your craft and know your tools as with any career in order to gain experience. The trick of becoming an experienced Data Scientist is to do as many project as possible. The more you practice, the more you become experienced. There are a tons of Data Science projects you may consider doing in platforms such as Kaggle. You may also want to participate in Data Science competitions or participating in solving real-world problems within Data Science communities. 

The above roadmap is not the perfect of all. There are a ton of other things you may need to do in order to excel in your Data Science career. Some extra skills that you may consider learning include; cloud technologies (such as AWS, Azure, or Google Cloud), Machine Learning, Communication, and storytelling.