
"No great marketing decisions have ever been made on qualitative data." – John Sculley
Data Science is a humungous field, and it is easy to get lost in the wide array of topics that are presented by the field. However, if you are just a beginner who wants to dwell deep into the subject of Data Science, it makes you wonder what are the essential concepts you should focus on so that you can have an equivalently better grasp of this area.
If you want to learn everything from scratch and set up a perfect schedule to learn everything you want ever, then make sure to check out the following article to master Data Science in 12 months while learning and gaining further knowledge using the 12 mentioned steps.
However, if you don’t have a lot of time and just want to complete a quick crash course, then this article will be the perfect choice for you to focus on the most essential aspects of Data Science as fast as possible. The main objective of the article is to focus on the crisp topics that are considered highly valuable in the field of Data Science.
Let us get started and understand the most significant topics in Data Science that you must focus on to learn more and gain further knowledge as fast as you can. The outlines of these concepts can be considered as listed in the various sections of this article.
Data

Without data, there cannot be Data Science. Data Science consists of crucial aspects of data mining, data preparation, data manipulation, data visualizations, and other aspects related to data. The formal definition of Data Science is as follows –
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, Machine Learning, and big data.
Data is considered one of the most crucial resources of the modern era. Tech giants and major companies are investing heavily to obtain the best possible resources they can. The reason for this aim is to achieve the desired customer satisfaction rate and analyze their behavioral patterns with the help of the collected data.
If you are a beginner starting with the process of learning data science, your approach towards data must be to collect and process as much information as you can from the obtained resources. Learn to identify the most relevant data that will be suitable for your particular project.
Once you are able to obtain the essential data, your task is to consider the useful information from it and pre-process the additional unnecessary requirements. Collect and prepare your data. The best tools for helping you with this process is usually the pandas library module along with a module for scrapping off irrelevant data like the regular expressions (re) module.
I would highly recommend checking out the following article on NLP made simpler with four regular expression commands. The link is provided below.
Natural Language Processing Made Simpler with 4 Basic Regular Expression Operators!
Programming

Programming happens to be one of the most essential topics you need to focus on to build Data Science projects. That part is quite obvious but how can you develop an effective understanding of programming faster if you have no idea about it or if you are just a beginner looking to learn more about coding.
Well, the idea is quite simple. Python is one of the easiest programming languages for beginner coding enthusiasts to learn and explore. The best results about Python can be obtained by working effectively on the subject and it can be mastered at an intermediate level within the span of one month.
A quick ten step procedural guide I would highly recommend checking out for beginners to master Python for Data Science can be found from the following link. Follow the mentioned steps in their respective order to achieve the best possible results.
The other requirement for Data Science is an adequate amount of SQL. Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.
A simple basic understanding of SQL is usually sufficient to start working on decent Data Science projects. With an intermediate level understanding of the Python programming language that can be acquired with the help of amazing resources of the internet, you can start working on your Data Science projects.
The more essential aspect of programming that you must focus on is the ability to use the numerous Data Science library modules that are present in the Python development environment. You can explore these tools as they will help you to accomplish a lot of unique tasks.
Visualization Techniques

To simplify your Data Science projects, your main focus must be on using a variety of visualization techniques to grasp the useful information that you have in the data that is available to you. The procedural retrieval of this useful information can be attributed to an important step termed as Exploratory Data Analysis (EDA). The formal definition can be stated as follows –
In statistics, Exploratory Data Analysis is an approach to analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
Visualization holds high importance in Data Sciences due to its ability to figure out the right or wrong data for a particular task. While analyzing the data, you can figure out patterns and numerous other elements that will help you to encounter complicated Data Science projects more effectively and efficiently.
Python programming is highly beneficial for this purpose as it allows the users to utilize some of its best visualization tools in matplotlib and seaborn for performing analysis on numerous datasets. When you are able to have a better grasp on these library modules, you can develop a greater intuition behind the analysis procedure.
Trying out a variety of plots like bar graphs, pie charts, histograms, scatter plots, and other such visualization techniques will help you gauge a better understanding of the problem statement and your approach to solve it. You will not only understand the data, but you will also be able to interpret the steps you must follow to crack the project.
Math

Mathematics is the single essential aspect of Data Science that you need to have some knowledge of to understand numerous concepts intuitively. Having a strong basic understanding of Math will help you to figure out the conceptual proficiency behind machine learning algorithms and other concepts related to Data Science.
In math, if you are just getting started with Data Science, the main objectives for you to master must be the three fields of linear algebra, probability and statistics, and calculus. In linear algebra, try to develop an intuition behind vectors, Euclidian distances, dot products, and other such similar topics to understand numerous machine learning aspects easily.
Probability and Statistics are important topics to understand graphical information intuitively and always the procedural understanding behind the machine learning algorithms like Naive Bayes algorithm based on the Bayes theorem. While Integral calculus might not require immediate attention, you should focus on differential calculus to grasp the process of backpropagation and other similar concepts more quickly.
For learning more about the best free resources for learning math, you can either check out the following link or read the next section containing the best resources to learn math.
Best Resources To Learn Math:
- *Khan Academy ():** Best place for you to start off with learning basic mathematical concepts like linear algebra, calculus, and probability and statistics.
- *3Blue1Brown ():** One of the best YouTube channels to check out some brilliant in-depth explanations and intuitively understand concepts much better.
- Books: If you are more into reading books, then do a quick Google search to check out some outstanding resources for the best free e-books to learn Mathematics. You should prefer separate books for each specific topic for better practice.
Machine Learning

Machine learning is an exceptional topic in Data Science and one of the main ways you can construct and build your models to complete the objectives and tasks that you are planning to accomplish. Machine learning can be termed as a computer program that will learn over time i.e. gain experience by performing a particular task and learning it after many epochs.
Machine learning models are the main way to solve your Data Science projects. Once you finished analyzing the dataset, and you have figured out the best methods and approaches using numerous visualization techniques, and you have finished preparing the data, your machine learning model is now ready to be built.
During the construction of the machine learning model, your primary objective is to solve the particular task while achieving decent accuracy and loss results. The scikit-learn library module in Python is highly recommended to all the beginners of Data Science who don’t have any experience with machine learning algorithms.
Understanding the working and orientation of these models is important for a Data Scientist. If you have the time, I would suggest you implement all these machine learning algorithms from scratch. However, if you don’t have enough time to work on these projects and you want to experiment with the subject as soon as possible, just try out a lot of these pre-built algorithms.
Once you have successfully finished training your model and you have managed to run a decent number of epochs, you should consider the deployment of these models. This step will ensure you complete your first data science project in a few days or a few weeks. You can focus on the essential parts of Data Science and build a lot of more useful projects.
Conclusion:

"Wisdom is not a product of schooling but of the lifelong attempt to acquire it." – Albert Einstein
While there are several aspects of Data Science that are crucial and require complete attention and focus to master the subject, there are a few specific topics that hold particularly more significance. With the help of these concepts, you can accomplish a wide range of tasks and hence, enabling you to achieve the desired results on your projects much faster.
Your objectives must be to acquire a wide range of data and study the data thoroughly. Apply a bunch of visualization techniques and accordingly determine the best machine learning algorithms that you can implement on your projects. Doing so will help you to achieve your desired results and in much less time than if you spent your time working out everything.
Don’t forget to focus on the essential aspects of programming, namely a programming language like Python to enable you to learn the basics of coding and develop unique projects. Learn some SQL for managing some databases for your Data Science projects. Don’t forget math because it is the core of Data Science and machine learning. It will help you gain a solid foundation and understanding of the subject.
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles that you might enjoy reading!
5 Reasons Why You Should Develop A Game With Python And AI!
Working With Object Storage And Jupyter Notebooks
Thank you all for sticking on till the end. I hope you guys enjoyed reading this article. I wish you all have a wonderful day ahead!