Data Science is the most hyped-up topic of the modern era. It has vast opportunities and a huge job market. Mastering Python turns out to be one of the significant aspects of becoming a successful Data Scientist.
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.
So, Data Science is a pretty humungous field. But luckily, a simple programming language like Python can be useful for mastering crucial coding areas of Data Science problems and solving complex computations.
This article will cover the ultimate ten-step procedure to completely master Python programming and use it effectively to solve Data Science Projects with ease and high efficiencies.
The article aims to give the viewers a concise guide and starting point to boost the confidence of future data science aspirants to pursue the field and achieve high feats. I have elaborated most of the sections of this article in detail to make sure the readers gain precise knowledge and information.
Note: In this article, we will be going in-depth into some of the essential topics that I feel. The starting five points will correlate to how you can become efficient in Python. Starting from the sixth point, I will be diving deeper into how you can use Python for Data Science. If you already well-versed with Python, and you want to learn how to use Python for Data Science strictly, you can feel free to jump ahead to that section. However, I would highly recommend reading the entire article to clear any confusion and help gain a better understanding.
1. Learning Python
Python is an object-oriented, high-level programming language that was released way back in 1991. Python is highly interpretable and efficient. Python is versatile, and thanks to its resourcefulness, it is a suitable fit for Data Science. I initially started 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. Despite having some flaws, like being considered a "slow" language, Python is still one of the best languages for AI and machine learning. Although there are a variety of other languages such as Julia, Golang, etc., which might be quite competitive against Python in the future years, the latter remains the better choice at this point.
The main reasons for the popularity of Python for Data Science despite other languages like R is as follows –
- As mentioned previously, Python is a simple language and is overall consistent.
- The rapid increase in popularity in comparison to other programming languages makes it a suitable pick for beginner-level programmers.
- Has extensive resources concerning a wide range of libraries and frameworks for supporting Data Science.
- Versatility and platform independence, which means Python can import essential modules built in other programming languages as well.
- It has a great community with continuous updates. The Python community, in general, is filled with amazing people, with constant updates made to improve Python.
To get started with Python, you can download it from here.
2. Understand The Basics
Understanding the basics of the Python programming language is undoubtedly the most important aspect to master Python. There are many key concepts like keywords and identifiers, variables, iterative statements like "for" loop, "while" loop, the comment lines, control statements, and so much more.
It would make this article too big if we try to cover most of the topics mentioned previously. So, we will aim to cover some of the more crucial topics in this section. I will make sure to write a separate article that will cover a complete roadmap to Python in the future. Let us analyze some of the basic concepts to know about Python.
Strings can be defined in single quotes ‘ ‘ or double quotes " ". Strings are an immutable sequence of characters. Computers do not deal with characters. Instead, they deal with numbers, especially in binary. Even though you may see characters on your screen, internally it is stored and manipulated as a combination of 0’s and 1’s.
This conversion of character to a number is called encoding, and the reverse process is decoding. American Standard Code for Information Interchange (ASCII) and Unicode are some of the popular encoding used. In Python, the string is a sequence of Unicode characters. The usual formatting technique that is used by strings for the encoding is the UTF-8 standard which is represented with bytes.
Functions are a block of code that is written in a program so that they can be recalled multiple times. The main utility of a function is so that it can be repeatedly called numerous times in the same program, and you don’t need to write the same codes over and over again. However, you can also use it to provide more structure and an overall better look to your programs.
Functions are defined using the keyword ‘def,’ which can be called with defined or undefined parameters. When you call the particular the particular function, then whatever the value is to be returned is interpreted by the python compiler.
Data Structures are a collection of data elements that are structured in some way. There are many built-in data structures in python. Let us explore each of these individually. Let us quickly go through some of the data structures offered by Python.
1. Lists –
A list is a mutable ordered sequence of elements. Mutable means that the list can be modified or changed. Lists are enclosed within Square Brackets ‘[ ]’.
2. Dictionary –
A dictionary is an unordered collection of items. Unlike lists and other data structures like tuples or sets, the dictionary data structure has a pair of elements referred to as key and value. The dict() function can be used to assign a dictionary to a variable.
3. Tuples –
The tuple data structure is similar to the list data structure where you can define a tuple with a fixed number of elements. The only difference is that tuples are immutable. This prevents any modification of the elements within a tuple as more elements can’t be appended or removed from the specific tuple that is created.
4. Sets –
A set is a collection of unordered elements. These elements are not indexed as well. Sets can be defined by using the set function or by using the curly braces ‘{}’ with only one element.
With all these basic concepts covered, we can now move on to the next section of the article.
3. Understanding Advanced Topics And Conceptualize These Essential Concepts
Now that you have a basic idea of the working of the various important aspects of Python as discussed in the previous sections of the article, it is equally significant to understand some slightly advanced topics in Python and conceptualize these essential concepts.
By the following statement, I mean that you need to have a visual perception and intuition of how some of the advanced concepts work. The advanced topics can be dealing with anonymous functions, topics like args and kwargs, classes, list comprehensions, and a few more significant concepts related to Python.
However, we will look at each of these individual topics separately in future articles. In this article, we will only briefly look into the topic of list comprehensions. Two links are also provided at the end of this section to help you gain a better understanding of other advanced concepts.
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.
To understand the concept of list comprehensions with the help of a simple example, let us consider the below code block.

In the above code block, the list with the name "squares" was created. Using an iterative "for" loop and the append function, we were able to calculate the squares of the numbers ranging from one to ten. However, the same problem can be solved in a single line with the help of list comprehensions as shown below.

The above code block shows the representation of how a list comprehension can simplify a code of a few lines to just a single line while getting the exact same output faster. The only issue with list comprehensions is sometimes it could be hard for the reader to understand the code.
By using the approach of nested list comprehensions, you can also solve more complex tasks. Overall, list comprehensions prove to be extremely useful for solving tasks and computations while consuming lesser space and time complexities for general problems.
In this article, we will not be covering any of the advanced topics as each of them are quite complex to cover in a few minutes, and they deserve separate articles of their own. However, I have provided links to two useful articles that you might want to consider to learn slightly more advanced topics. At the end of this article, I have also provided a link to master lists, which will be helpful to understand various aspects of lists in further detail.
Understanding Advanced Functions In Python With Codes And Examples!
Simplifying args And kwargs For Functions With Codes and Examples!
4. Code Continuously
While learning any programming language, consistency in coding should be maintained. It becomes an extremely essential factor to keep in touch with the language and learn new things. The more mistakes you make, the more you can learn, thanks to the wonderful resources available on the internet.
Python is a remarkable programming language. It is simple to use and provides efficient results. There is a wide range of options available for the integrated development environments (IDE) such as Pycharm, visual studio code, Jupyter notebooks, etc. Even the simple Python IDLE is a phenomenal interpretable environment for beginners.
No matter which development environment you choose, there are some errors you might encounter. These errors and issues occur mainly due to misjudgments, lack of in-depth knowledge on the particular topic, or just a silly blunder that could happen to anybody.
Hence, it becomes essential to continuously keep coding. Don’t be scared to get your hands dirty and consistently work on self-improvement to become better at programming.
There are a lot of websites to improve your coding as well as participate in competitions like HackerRank, which you should consider. Involving in the community is helpful to consistently learn more from fellow data science enthusiasts.
Participating in Hackathons or competitions, regardless of what place you finish, will boost your confidence and help you to develop a programming mindset to encounter more complex problems in the future.
5. Work On Some Cool Projects
Now that we have had a brief understanding of the Python language and the power it has to create various new projects, it is extremely important to incorporate these coding skills to the next level by practical implementations of many cool projects.
The best way to gain a better understanding of any programming language, especially a language like Python, is to keep coding and make sure you utilize your newly learned skills in the form of the project.
There are two major benefits to creating a variety of new projects with Python. The first helpful thing is quite obvious. It is the power of gaining knowledge and learning a newer concept better. While working on a project, you spend time extensively researching and attaining more information and an overall greater intuitive understanding.
Secondly, the projects you have implemented can also be a fantastic way to showcase your skills to a wide range of audiences as well as advertise these projects in your resume or portfolio. Hence, building new projects in Python is a win-win scenario. You gain knowledge, have fun, and get to display your projects to show your accomplishments in the field.
The link provided below should be an awesome way for you to get started with Python projects. In the article below, I have discussed the five best project ideas for viewers of various ranges of difficulty. Feel free to check it out and implement it on your own to develop a stronger understanding of Python.
5 Best Python Project Ideas With Full Code Snippets And Useful Links!
6. Understanding Why Python For Data Science
Python’s specialty is in its ability to perform visualization tasks, exploratory data analysis, and of course, in the field of Artificial Intelligence in the aspects of machine learning, deep learning, and neural networks.
Python provides great functionality to deal with mathematics, statistics, and scientific function. It also grants access to some of the best libraries to deal with data science applications. We will discuss more on this particular point in the next section of the article.
The wide variety of frameworks that are accessible through python modules and libraries help to solve complicated machine learning as well as deep learning problems.
It would not be wrong to say at this moment of time that the capabilities of python in the field of artificial intelligence and Data Science are significant and almost unmatched.
However, there are new emerging languages on the rise that could potentially pose a threat and be a massive competitor to python. We will discuss more on three such potential languages in a future article.
As of right now, python is a great Programming language to start off with your data science journey to solve complex machine learning and deep learning projects.
7. Studying The Basic Data Science Libraries
The main advantage of using Python to solve data science problems and perform visualizations is due to the abundance and availability of fantastic libraries and frameworks to solve tasks related to Data Science and Machine Learning.
In this part, we will discuss five of these significant library modules that are used in python for solving Data Science tasks. There are tons of frameworks available in python, but these five mentioned in this section of the article will cover the basic requirements to get started.
1. Pandas –
The Pandas module is an open-source library in python to create data frames, which is extremely useful for organizing the data. Pandas is used extensively in the field of data science, machine learning, and deep learning for the structured arrangement of the data.
The data frame created in pandas is a 2-dimensional representation of the data. After importing the Pandas library as pd, you can visualize the tabular data of your liking. An example of this is as shown below:

Overall, the Pandas module is a fantastic library for systematic viewing of the data, and it also allows a wide variety of operations that can be performed.
2. Matplotlib –
The Matplotlib module is one of the best tools for the visualization of the data frames or any other form of data. Matplotlib is used to visualize the data for exploratory data analysis in data science. It is extremely useful to understand the kind of data we are dealing with and to determine what is the next action that must be performed.
The library offers an extensive variety of visualization functions such as scatter plot, bar plot, histograms, pie chart, and many other similar functions. Import matplotlib.pyplot module as plt for performing visualization tasks using matplotlib. An example of these can be seen below –


The scatter plots and bar graph plotted using matplotlib is shown in the figures. An advantage of the module is that it is very simple to use and efficient at providing visualizations. It can also be combined with the seaborn library for a more visual and aesthetic appeal.
3. NumPy –
The NumPy library stands for Numerical Python. The numpy library is one of the best options for performing computations on matrix operations. It supports multi-dimensional arrays. An extensive amount of mathematical and logical operations can be performed on arrays. By converting lists into numpy arrays, we can perform computations like addition, subtraction, dot product, among many others.
The use cases of numpy are applicable in both computer vision and natural language processing projects. In computer vision, you can use numpy arrays for visualizing the RGB or grayscale images in a numpy array and converting them accordingly. In natural language processing projects, you usually prefer to convert the text data into the form of vectors and numbers for optimized computation. Import numpy as np, and you can convert the text data into categorical data, as shown below:
4. Scikit-learn –
The scikit-learn module is one of the best tools for machine learning and predictive data analysis. It offers a wide range of pre-built algorithms such as logistic regression, support vector machines (SVM’s), classification algorithms like K-means clustering, and a ton more operations. This is the best way for beginners to get started with machine learning algorithms because of the simple and efficient tools that this module grants access to.
It is open-source and commercially usable while granting accessibility to almost anyone. It is reusable and supported by libraries such as NumPy, SciPy, and Matplotlib. import the sklearn module to run the scikit-learn code. Below is a code example for splitting the dataset we have into the form of train and test or validation data. This is useful for training and evaluation of the models.
5. NLTK –
The NLTK library stands for the natural language toolkit platform, which is one of the best libraries for machine learning of natural language processing data. Natural Language Processing (NLP) is a branch of AI that helps computers to understand, interpret, and manipulate human language.
The NLTK library is very well suited for linguistic-based tasks. It offers a wide range of options for tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning. It allows the user to chunk the data into entities that can be grouped together to produce a more organized meaning. The library can be imported as nltk, and below is an example code for the tokenization of a sentence.

8. Implementing Them In Various Scenarios
To appreciate the true beauty of data science, you need to try out lots of projects. The tasks that can be achieved and the problems you can solve are absolutely fantastic. Theoretically understanding the intuition of machine learning concepts and math behind these concepts of data science is crucial.
The most interesting part of data science projects to me is building machine learning or deep learning models and making sure they work perfectly and feel good about it. Then, deploy those models built once they are meeting the appropriate requirements.
However, a large part of Data Science is actually dealing with the data at hand. Most of the data available naturally on the web is not clean. A lot of cleansing and pre-processing must be done for the extraction of useful data.
Most complex tasks require critical analysis and computational processing to obtain desirable outcomes. Persistence is extremely important in every scenario especially in the field of data science.
It is not uncommon in data science to get stuck on a problem that you are working on for a long time. The best part is data science has a brilliant community with very helpful people and lots of resources at your disposal for your benefit.
Stack Overflow, discord channels, YouTube videos, free online code camps, GitHub, towards data science, etc. are all helpful resources that are available for all of us to utilize and improve our skills.
9. Correlate To The Real World
In my opinion, this point is the most crucial one of all the listed points.
As mentioned in the previous point, it is awesome to implement your immense knowledge that you learn over long periods into some Data Science projects.
However, you also need to know how you can implement the following projects in a real-life practical scenario. Try to correlate your project ideas and see how they would work out in the real world. Don’t be afraid to get your hands dirty with some code and implement these projects on your own.
The best part about Artificial Intelligence and Data Science is the continuous evolution of these subjects each day. The improvements in technologies are rapidly increasing. It becomes significantly more important to stay updated on the latest trends and emerging developments that occur in the field of data science.
Researching is an integral part of any Data Science Project. It is crucial to have some knowledge or at least a brief idea of what are expansions occurring in the AI field.
Every task to be solved by a data scientist is unique in its own way, and these complex tasks have various solutions and hence, even the best ways to solve them will differ accordingly. Therefore, adaptability is an essential aspect of producing the best results.
Creative, critical, and analytical thinking are some of the most intriguing characteristics of a data scientist. The ability to think outside the box and implement innovative ideas is a necessary and requirement for a successful data scientist to perform. These attributes are some of the key aspects of performing outstandingly on an industry level.
Find a way to successfully implement your ideas in a way that it can be used in the real world and in real-life scenarios so that it can reach a wider audience as well as benefit a lot of people.
10. Keep Practicing
Data Science can sometimes be difficult, especially for a beginner trying to get started. You look at the potential topics in this field, and it could intimidate quite a few people.
The interesting part about data science, similar to programming, is with each mistake you make, you learn something new and what you did wrong, provided you find a solution by looking it up on the internet or cracking it by yourself. This feeling makes the overall experience even more satisfactory.
Don’t worry if you are not able to solve a machine learning or data science problem on your first try. That is completely fine as long as you remain persistent, find a solution, and understand the concepts better.
Also, if it makes you feel better, even experts in this field make mistakes and have to look up stuff for solving certain questions. This field is probably one of the only ones where you don’t have to mug up a lot of things as you can use Google for things you forget.
The field of artificial intelligence and data science is humungous. There is so much out there to be curious about and explore. There are lots of mathematical functionalities, in-depth theory on multiple aspects of machine learning and deep learning.
Practice becomes significantly to keep yourself updated with all the latest trends and process the on-going techniques in this tremendous field. There is a lot of scope in every aspect with continuous developments. So, keep coding and keep working on practical implementations!
Try to actively participate in competitions on websites. Kaggle is one such site that hosts some of the best data science, related competitions. Don’t worry about which place you finish. It does not matter much as long as you learn something new.
As discussed earlier, there are a lot of websites to improve your coding as well as participate in competitions like HackerRank, which you should consider. Involving in the community is helpful to consistently learn more from fellow data science enthusiasts.
Every model you construct and every project you complete in data science has a lot of room for improvement. It is always a good practice to consider alternatives and various other methods or improvements that you can make to achieve better results.
So, Keep Practicing!
Conclusion:

The article aims to provide a solid foundation and a concise guide to master Python for Data Science. To summarize the essential things stated in this article, it is crucial that beginner data scientists learn Python from scratch and understand the basics of Python with conceptual interpretations. Working on projects and continuing to code is crucial to stay familiar with the language.
Python is one of the best languages for solving complex data science problems. After a detailed understanding of python and its library frameworks for Data Science, keep up the practical implementations while trying to correlate its usefulness with the real world. And most importantly, keep practicing and evolving!
If you have any queries in any context of the various sections of this article, then feel free to let me know, and I will get back to you as soon as possible. Make sure you leave a comment below on the specific topic, and I will either give you a reply or write another article that aims to answer the required question.
Check out some of my other articles that you might enjoy reading!
10 Awesome Real-World Applications Of Data Science And AI
Mastering Python Lists For Programming!
IoT And AI: A Powerful Evolution For Future Generations!
3 Ways To Utilize The Power Of Artificial Intelligence For Your Marketing Today!
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!