Efficient Python Programming: The Top 10 List for Data Scientists

The contribution of a Data Scientist cannot be overstated since they utilise sophisticated tools and techniques to extract valuable insights from vast data sets. Tasks such as structuring data, formulation of machine learning models and more require attention to detail. Widely recognised Python is a top choice as it satisfies every requirement in this field. Recently, Forbes listed it among the top 10 technical skills that companies seek to recruit, to meet the high demand for its application in this domain.

Having knowledge of Python is an essential requirement to become a Data Scientist.

Businesses are gradually understanding the worth of data science and are continuously enhancing their approaches to capitalise on this technology. Python programming has gained immense popularity, primarily due to its simple, effective syntax and high speed, thereby accelerating work productivity. As evident from the example code snippet below:

“Hello Adam.

There is no content provided to rephrase. print "Hello Adam";

An alternative Java implementation, serving the same purpose, is presented below.

There is no content provided to rephrase.

class A {
public static void main(String args[]){
System.out.println("Hello Adam");

The extensive use of Python as a computer programming language is supported by the reasons provided below, thus attracting many data scientists. The popularity of Python is maintained by its numerous benefits, making it an essential tool for data scientists who must be well-versed in its functionality.

Outlined below are some recommendations to improve the effectiveness of your Python programming.

By strengthening essential programming concepts

Coding is a highly creative process, which can be refined through perseverance and commitment to produce efficient and practical code. A clear comprehension of programming fundamentals is imperative for creating effective solutions via computer programming. By attaining a strong grip on these basics, coding becomes a much simplified task.

Beginner’s Advice:

“Automating the Boring Stuff with Python” is an excellent source of information for those who are new to programming. As a beginner-friendly language, Python can be used to accomplish various mundane tasks such as filling out online forms, downloading content from the web, encrypting PDFs, and combining multiple documents. This guide is designed to help those who have never written any code before, allowing them to become comfortable with programming without feeling overwhelmed.

Tips for Experienced Programmers:

There are ample online resources available to aid you in expanding your professional expertise with the addition of Python programming to your skill set. To attain proficiency in Python, it is critical to thoroughly understand the following concepts.

  • The map function, which behaves like an iterator, operates on every element of the iterable by applying the specified function and yielding the corresponding outputs.
  • Lambda functions are unnamed functions, which may have multiple return values and required parameters numbering zero or more.
  • Itertools is a module comprising of iterator-based manipulations that yield more advanced iterators.
  • The objective of exception handling is to handle unforeseen software issues with efficiency and poise.
  • “Decorators” enable developers to modify the way a specific class or method functions.
  • The Collections module in Python offers a versatile collection of libraries for consolidating and accessing data in a range of containers.
  • The phrase “magic methods” alludes to methods that the class invokes whenever certain activities are carried out on the class.
  • A “generator” denotes a particular type of standard function that does not return a single outcome but rather generates iterators.
  • Regular expressions are strings of characters that can be used to find or identify specific strings or groups of strings.
  • Threading is a technique used to carry out multiple processes simultaneously.

To apply your acquired knowledge and establish a strong comprehension of basic programming concepts, I suggest the following:

Data Science Archives

Data scientists commonly use Python because of the extensive collection of specialised libraries that are at their disposal. These libraries cater to almost every requirement, from mathematical calculations and data exploration to data mining and more. Below are some of the most commonly used Python libraries for data science:

  • SciPy
  • NumPy
  • Scrappy
  • Pandas
  • BeautifulSoup
  • Matplotlib
  • Seaborn
  • Keras
  • PyTorch
  • PyCaret

Programmed tqdm for Loop

Creating code loops to manage large data sets can be challenging and time-intensive. However, tqdm offers a simple solution by incorporating a progress bar within the code. This progress bar provides valuable data, such as the current loop progress, time taken, iterations per second and other useful metrics. A progress bar like this can save developers a lot of time and effort.

If you want to provide a loop description, the “DESC” parameter will help you achieve that.

Type Indication

Type hinting is an essential feature for writing intricate Python scripts. By specifying argument types that a function can accept, developers can define the return type of a particular Python function, offering valuable clarity and understanding. Although not universally adopted, this practice is widely regarded as a hallmark of expert Python programming.

Kwargs and Args

Using Kwargs and Args can provide more accurate definitions for a function’s parameters.

  • The quantity of positional arguments is specified by args, which is frequently left undefined.
  • Kwargs indicate a variable number of keyword arguments.

To better comprehend the concept, let’s look at a real-life example. Let’s say you have a function that can accept an unknown number of directional paths, and each path consists of several files. There’s no way of knowing how many choices the user will make. To accommodate all of the function’s arguments, developers can use Keyword Arguments (Kwargs) and Arguments (Args).

Adding to Visual Studio Code

Python code editors offer developers various tools to create top-notch programs. One of the most popular and versatile options amongst them is Visual Studio Code (VScode). To optimise your experience with VScode, we recommend that you download and install additional extensions to significantly enhance your coding experience.

  • With “Path Intellisense,” file names can be automatically completed.
  • Pylance’s code-completion and parameter-suggestions capabilities enable faster program development.
  • Python Indent is a utility for properly indenting multi-line Python scripts.
  • Python Function Docstring Generation.

Attraction before Commitment

It’s usual for early versions of computer code to be ridden with errors and poorly formatted. Fixing each issue individually can be an extremely tedious task. Thankfully, free commit hooks can be highly advantageous in such instances. By running the “pre-commit run” command, automatic code formatting can be accomplished with minimal effort.

Tip: Don’t forget to stage the files (with git add) before running a pre-commit script to ensure they aren’t missed.

Interactive Data Visualizations that Integrate Basic Statistics

A comprehensive grasp of the theoretical as well as practical aspects of statistics is widely recognized as essential for success in the area of data science. This book highlights the challenges that can be addressed through statistical analysis, which is commonly referred to as the “lifeblood” of data science.

Listed below are some of the most basic concepts in statistics. Once you’ve grasped these basics, you can begin to apply them in Python.

  • Fundamental Probability Concepts
  • Comprehensive Examinations
  • Mean
  • Sampling
  • Median
  • Mode
  • Deviation from the Mean Value
  • Occurrence Patterns
  • Confidence Intervals
  • Hypothesis Testing Procedures

When it comes to statistical modeling in Python, the use of the statsmodels package is highly recommended. To learn how to make the most of Python for statistical modeling, it is advisable to visit statsmodels.org for in-depth resources and tutorials.

Data Visualization with Matplotlib

Matplotlib is a robust library that offers a wide range of options for creating popular graph types, including bar charts, histograms, line graphs, scatter plots, and box plots. Seaborn is an additional high-quality library that can produce similar results. It is not essential to be a Matplotlib expert, as modern businesses also use other tools, such as Qlik and Tableau, to make interactive visualizations.


Once you have gained a solid understanding of Python, the next step is to apply what you’ve learned. Below are some resources that may be useful in achieving this goal.

Do-It-Yourself Home Improvement

When choosing a topic related to real-world data science, it is vital to examine the structure, characteristics, and objectives of the dataset in question. By conducting a thorough evaluation of the data, one can gain a better understanding of the dataset and the insights it can offer.

By participating in this training, you will gain invaluable hands-on experience of the data science process in action, providing you with a practical understanding of the proper procedures for managing projects. Consequently, you will have the confidence to apply the knowledge and skills you have acquired to your future tasks.

Kaggle: Building Competitive Thinking Skills

Participating in competitions hosted by Kaggle can be an excellent way to gain experience and insights into a project. By following the tutorials provided, one can learn the project’s fundamentals and begin working with the dataset provided to achieve a specific objective.

Participating in these contests is advantageous as they offer an excellent opportunity to refine one’s skills, as tasks can be taken on gradually, starting from the simplest and progressing to more complex levels. Moreover, contest winners can expect to receive significant rewards.

To be a successful data scientist, it is unnecessary to stay up late learning the intricacies of coding. The more you practice and read code, the more comfortable it becomes. Becoming an expert in every programming language is not required; a solid understanding of well-organized code is sufficient. Memory leaks, Big O notation, and Python cryptography may not be relevant to data science; there are fewer Python topics that a data scientist needs to master to excel in their field.

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