Data science is the hottest tech field available today. The demand for Data Science job listings has seen a steady uptick over the last few years. According to fortune, the statistics say that the hirings for AI specialists have grown by 74% over the last 4 years. Data Science is regarded as the "Hottest" job of the present generation.
The demand for skilled Data Scientists is growing faster, like never before. Requirements and open positions for experts in the sub-fields of AI like machine learning, deep learning, computer vision, statistics, and natural language processing are surging each day.
In this article, we will cover all the significant aspects that you need to know to master data science and succeed as a data scientist by creating numerous fabulous projects.
I have made a small list of the table of contents for this article that will help you gather a sense of understanding of the things we will encounter.
Table Of Contents:
- Mathematics
- Programming
- Data Mining
- Data Visualizations
- Machine Learning
- Deep Learning
- Other Essential Branches
- Conclusion
If you are a beginner, I would recommend you read all these things, but if you feel confident, then feel free to skip to the sections you are most interested in.
1. Mathematics

Mathematics I find is one of those subjects you either learn to love or end up loving to hate it. Some find math as an amazing subject while others find all these number’s thing kind of boring. Doesn’t matter which side of the spectrum you are on because math is fortunately or unfortunately one of the most fundamental requirements for Data Science.
Mathematics is an essential requirement for data science. Linear algebra, calculus, probability, and statistics are the most significant concepts that you need to know in order to conquer all the mathematical aspects of data science.
A high school understanding of the basics of these concepts would suffice for a beginner to enter into the universe of data science. However, if you are not too confident with these concepts or need a brief brushing, then I would highly recommend checking out reading some articles on TDS because they explain most concepts with simplicity and ease. YouTube videos are also a great alternative option to learn these concepts.
Mathematics is required for building predictive machine learning models, understanding probabilistic and deterministic approaches to solving Bayesian and other similar problems, understanding backpropagation in deep neural networks, analyzing gradient descent, and so much more.
2. Programming

There are about 700 coding languages that exist in the world of programming. Understanding the significance of each programming language and how they can impact the particular tasks we need to perform are paramount. One such programming language which is extensively used in data science is Python.
Python is an object-oriented, high-level programming language that was released way back in 1991. Python is highly interpretable and efficient. Simply put – Python is amazing. I initially started out with languages like C, C++, and Java. When I finally encountered python, I found it to be quite elegant, simple to learn, and easy to use.
Python is the best way for anyone, even people with no prior experience with programming or coding languages to get started with machine learning. In spite of having some flaws like being considered a "slow" language, python is still one of the best languages for AI and machine learning.
The main reasons why Python is so popular for machine learning despite other languages like R is as follows –
- As mentioned previously python is very simple and consistent.
- The rapid increase in popularity in comparison to other programming languages.
- Extensive resources with respect to a wide range of libraries and frameworks. We will discuss this in further detail in the next part of this series.
- Versatility and platform independence. This means python can import essential modules built in other programming languages as well.
- Great community and continuous updates. The python community in general is filled with amazing people and constant updates are made to improve python.
To get started with python, you can download it from here.
3. Data Mining

Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.
Google searching is obviously the best way to look for new resources. Kaggle offers some of the best data and datasets options available for each of the specific competitions that it holds. Sometimes very interesting datasets can also be found on GitHub as well.
If you are looking to do some natural language processing projects, then you can also make use of Wikipedia or other similar sites to extract data by web scraping.
The UCI Machine Learning Repository and Data.gov are other awesome websites that have the availability of a wide array of resourceful dataset options.
4. Data Visualizations

Visualizations are a significant aspect of any data science project.
In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual 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.
The role of exploratory data analysis in the field of data science and machine learning projects is to be able to get a detailed understanding of the data at hand.
Exploratory data analysis offers many plots and varieties to visualize and analyze the data available. It provides a brief understanding and idea of how to proceed further.
Matplotlib.pyplot and seaborn are the two best library modules for visualization and performing exploratory data analysis tasks. These allow you to plot many graphical structures that are going to be extremely helpful for analyzing your data.
5. Machine Learning

Machine Learning is the ability of a program to learn and improve its efficiency automatically without being explicitly programmed to do so. This means that given a training set you can train the machine learning model and it will understand how a model exactly works. Upon being tested on a test set, validation set, or any other unseen data, the model will still be able to evaluate the particular task.
Let us understand this with a simple example. Assume we have a dataset of 30,000 emails out of which some are classified as spam and some are classified as not spam. The machine learning model will be trained on the dataset. Once the training process is complete, we can test it with a mail that was not included in our training dataset. The machine learning model can make predictions on the following input and classify it correctly if the input e-mail is spam or not.
There are three main types of machine learning methods. We will discuss each of these methods. I will then state a few examples and applications for each of these methods.
1. Supervised Learning –
This is the method of training the model with specifically labeled datasets. The datasets can either be a binary classification or multi-class classification. These datasets will have labeled data specifying the correct and incorrect options or a range of options. The model is pre-trained with supervision i.e. with the help of these labeled data.
2. Unsupervised Learning –
Unsupervised learning is the training of the model on an unlabeled dataset. This means the model is given no prior information. It trains itself by the grouping of similar characteristics and patterns together. An example of unsupervised learning can be the categorizing of dogs and cats. The data given to us will be an unlabeled dataset with images of dogs and cats. The unsupervised algorithm will find similarities in patterns and group dogs and cats separately without the specification of the type of data.
3. Reinforcement Learning –
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
6. Deep Learning

Deep learning is a sub-field of machine learning which works on concepts of artificial neural networks to perform specific tasks. Artificial neural networks withdraw inspiration from the human brain.
However, it is paramount to note that they do not function theoretically like our brains, not even close! They are named as artificial neural networks as they can complete precise tasks while achieving a desirable accuracy without being explicitly programmed with any specific rules.
The main reason for the failure of AI a few decades ago was due to the lack of data and computation power. However, this has changed significantly for the past few years. The abundance of data is surging every day because big tech giants and multi-national companies are investing in this data. The computational power is also no longer such a big issue due to powerful graphics processing units (GPUs).
I will cover deep learning more specifically in my other posts, so stayed tuned for those upcoming articles.
7. Other Essential Branches

Let us look briefly into the other topics that are required to master data science starting as a beginner. These concepts will be extremely helpful for creating unique and awesome projects. Without further ado, let us look at them.
1] Computer Vision
Computer Vision is a field of Artificial Intelligence that deals with images and pictures to solve real-life visual problems. The ability of the computer to recognize, understand and identify digital images or videos to automate tasks is the main goal that computer vision tasks seek to accomplish and perform successfully.
Humans have no problem to identify the objects and the surroundings around them. However, it is not so easy for computers to identify and distinguish the various patterns, visuals, images, and objects in the environment.
The reason for this difficulty arises because the interpretability of the human brain and eyes differ from computers, which interpret most of the outputs in either 0’s or 1’s i.e. in binary.
The images are often times converted in arrays of three dimensions consisting of the colors red, blue, green. They have a range of values that can be computed from 0 to 255 and using this conventional means of arrays, we can write code exclusive to identify and recognize images.
You can learn more about Computer Vision from the following link:
OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!
2] Natural Language Processing
Natural Language Processing is one branch of Data Science where you can deal with languages and speech communications. You can develop projects to have a semantic understanding of the humans who are trying to interact with each other.
This is is the principle of working of most language predictive models such as the next word predictions or autocorrect. Natural language processing has a humungous scope and provides a wide array of choices to develop intelligent smart AI for high-level projects.
One such example used by both big and small companies is a chatbot that can provide human-level interaction to most audiences and viewers entering a website.
3] Robotics
Robotics and artificial intelligence have a humungous scope in the future. The integration of data science projects along with robots has tremendous potential for enforcing top-notch product manufacturing in industries with very little human effort.
Apart from this, robotics and data science can be exclusively used to achieve human-level performance on many pre-programmed tasks. The advancements in IoT and the community are also highly beneficial for the integration of AI in robotics to develop smart and effective devices.
Conclusion:

With the meteoric rise of data science and artificial intelligence, this is perhaps the best time for anyone to invest their time in understanding the depth and quality of these subjects. There are vast opportunities waiting out there for everyone due to the increasing demand and popularity for these fields each day.
I hope this article was able to connect with the audience on the essentials required for the mastery of data science. Since Data Science is an enormous field, some amount of time is required to master all these skills mentioned in this post. But it is totally worth all your time if you are interested in this subject!
Let me know what are your thoughts on the future of data science, and feel free to hit me up with any queries regarding this article. I will try to reply to them at the latest!
Check out some of my other articles that you might enjoy reading!
Solutions To Interview Questions On Pattern Programming!
Understanding ReLU: The Most Popular Activation Function in 5 Minutes!
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!