Last Updated on: September 23, 2022
Understanding, comprehending, analyzing, and mining data is one of the biggest and highly valued tasks in today’s world.
The best way to master these is to learn to read and write codes, in other words, learn the art of coding. Coding or programming is very powerful in the age of data. Almost all the businesses are on the lookout for data, from within the company and also from external sources. They need this data to understand their company and their performance better and thereby gain insights to improve.
An example for a simple Binary Search:
def binary_search(arr, value, offset=0)
mid = (arr.length) / 2
if value < arr[mid] binary_search(arr[0…mid], value, offset) elsif value > arr[mid]
binary_search(arr[(mid + 1)..-1], value, offset + mid + 1)
return offset + mid
Learning to code, though seems difficult at the onset, could be learned effectively and efficiently through any of the best courses available. You could check out the Data Science course from GeekLurn.
This article discusses basic coding techniques and logic to learn to gradually master data science and thereby use it to develop and enhance any business and industrial segment in the world.
Table of Contents
Why Object-Oriented Programming Matters for Data Science
Computational Thinking – Algorithms
Algorithms are logically sequenced computational instructions that help to execute the program it corresponds to. They can also be understood as a source of successive guides given to the computer to carry out a task or an action.
There are 2 main parts of an algorithm: the input and the output. The algorithm takes in the input, works on it, and executes the action, and finally provided the output. Algorithms are used widely in Itsector pertaining to any business domain. This helps carries out lengthy, huge, and tedious calculations. In some cases, this helps take critical and quick business decisions as well.
As a simple example, the long division method we follow in Mathematics is a classic example of an algorithm. There are inputs, the step-by-step instructions to follow to get the answer, and thereby output, which is the final answer.
Understand the Building Blocks – Data Constants and Variables
Constructing an algorithm is very much similar to constructing a building from scratch. There have to be some building blocks that help with the complete and foolproof construction of an algorithm. These are the data variables and constants.
Variables are the elements that can hold different values at different points in time. These play a crucial role in calculation purposes where there would be a need to assign different values at different points in time.
The Trick to Pattern Formation and Repetition
A pattern is a sequence of data that repeats itself periodically. This repetition results in a logical flow of ideas as well. Another use of repetition codes is to repeat the message several times. This can be utilized as a powerful error-correcting methodology in coding.
Pattern formation is also immensely helpful. It can help visualize the code to a great extent, thereby making it easy to work on and predict the results as well. The whole coding environment could also be analyzed using the same method. This also helps to create shapes, curves, and related patterns to analyze the data that is being fed into the system or algorithm.
Handling Decision Points or Choices
An intricate part of coding is implementing choices in them. These are called Decision Nodes in the programming jargon. These nodes are ital as they execute all decisions or choices that need to be included in the program, in its algorithm.
Sample Python code of Decision Tree classifier:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
X = iris.data[:, 2:]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
clf_tree = DecisionTreeClassifier(criterion=’gini’, max_depth=4, random_state=1)
This part of programming also helps us handle all permutations and combinations of decisions possible with respect to the data fed into the algorithm, thereby giving us a range of possible answers based on the varied scenario.
Improper implementation of the same could lead to incorrect output and readings and program crashes at the worst. This is where this decision part of coding gets serious and must be handled with great care.
Debugging and Testing
Client requirements are gathered, the coding is done and the program is developed. Now, it is not possible to directly release the same to the client.
An important process called Debugging is necessary. Debugging helps to check every code of the algorithms, to verify and make sure it gives the output that is actually required. Though this is a bit of a time-consuming process, the importance of this is immense.
To understand this in detail, let is have a look at the below simple code to generate a code dump:
using namespace std;
int divint(int, int);
int x = 5, y = 2;
cout << divint(x, y);
x =3; y = 0;
cout << divint(x, y);
int divint(int a, int b)
return a / b;
Now, to debug the same, the program has to be compiled with -g option.
$g++ -g crash.cc -o crash
Floating point exception (core dumped)
# Gdb prints summary information and then the (gdb) prompt
Program received signal SIGFPE, Arithmetic exception.
0x08048681 in divint(int, int) (a=3, b=0) at crash.cc:21
21 return a / b;
# ‘r’ runs the program inside the debugger
# In this case the program crashed and gdb prints out some
# relevant information. In particular, it crashed trying
# to execute line 21 of crash.cc. The function parameters
# ‘a’ and ‘b’ had values 3 and 0 respectively.
# l is short for ‘list’. Useful for seeing the context of
# the crash, lists code lines near around 21 of crash.cc
#0 0x08048681 in divint(int, int) (a=3, b=0) at crash.cc:21
#1 0x08048654 in main () at crash.cc:13
# Equivalent to ‘bt’ or backtrace. Produces what is known
# as a ‘stack trace’. Read this as follows: The crash occurred
# in the function divint at line 21 of crash.cc. This, in turn,
# was called from the function main at line 13 of crash.cc
# Move from the default level ‘0’ of the stack trace up one level
# to level 1.
# list now lists the code lines near line 13 of crash.cc
(gdb) p x
# print the value of the local (to main) variable x
It is imperative to understand the how and why of debugging the code. It ultimately helps to polish the code and make sure it does not yield bad results or crash while running. Certain line tracing techniques are often used to carry out the same. These techniques are important and much necessary to know for a programmer.
Data Arrangement and Exploration
Data obtained to feed into the algorithm is almost always jumbled and messed up. There is no proper alignment if the same and reading this so-called Raw Data is a tedious process in itself.
This is where the Data Arrangement technique plays a huge role. Arranging data using proper methods helps in obtaining great clarity and understanding of the data. It also makes the resulting data easier to work on and dissect.
Arrays are an integral part of this process. A programmer must have in-depth knowledge as to why and how arrays could be used to represent data. This applies to both static and dynamic arrays.
The World of Functions, Queries, and Classes
Functions are a sequence of code that help execute a specific task. These are generally pre-defined in nature and could be utilized any number of times in a code depending on the need. Having said this, Functions could also be created in the code for temporary use. It all comes to what is required in the programming.
Queries are instances that help test ideas, explore patterns, and see connections between themes, topics, people, and places that exist in the project or program.
Classes hold a set of data together that needs to function as a unit. So, data could be segregated using this technique. This also makes it easier to view and visualize data to identify and predict the process flow and the outcome.
Important Tools to Master Data Science Coding
The best way to implement the theory on coding and programming is to use tools to analyze and work on them. Some of them are:
- Apache Spark
Best Programming Languages to Learn for Data Science?
Learning to code is an essential step for anyone looking to make a career in data science. For this, you need to know the best programming languages for data science. The field of data science involves the use of mathematical and statistical techniques to manipulate, analyze, and extract information from data. It encompasses many domains, like machine learning, deep learning, network analysis, geospatial analysis, and natural language processing, that require the knowledge of programming languages to interact with computers. Some important data science programming languages are:
This general-purpose and powerful programming language is concise and easy to read, which is what makes it the most popular among data science programming languages. Widely used in web development, machine learning, and data science, Python is known for its rich ecosystem of libraries. The language can be used for data preprocessing, visualization, and even statistical analysis for the deployment of deep learning and machine learning models. Some of the most used libraries for data science purposes are:
- NumPy – This popular package offers an extensive collection of advanced mathematical functions.
- Pandas – This data science library is used for performing all kinds of database manipulations.
- Matplotlib – This is a standard Python library for data visualization.
- Scikit-learn – This Python library is used for developing machine learning algorithms.
- TensorFlow – This library, developed by Google, offers a computational framework for developing algorithms for machine and deep learning.
- Keras – This open-source library is designed to train neural networks with high performance.
An easy way to learn Python is to join GeekLurn’s online programs for data science.
This open-source, domain-specific language is specially designed for data science. Highly suitable for statistical computing and graphics, R provides a wide variety of statistical and graphical techniques. R has a large community of users and a vast collection of specialized libraries for data analysis. Although one can work with R directly on the command line, there is an option to use a third-party interface called Rstudio that integrates various capabilities like data editor, data viewer, and debugger.
Among the notable libraries of R are:
- Tidyverse – This is a collection of data science packages including dplyr for data manipulation and ggplot2 for data visualization.
- Caret – This is for machine learning algorithms.
Learning R is a good choice for people interested in the field of data science. Do check out the courses offered by GeekLurn.
SQL (Structured Query Language) is a domain-specific language used by programmers to communicate with and extract or edit data from databases. This feature makes SQL a must-learn for data scientists. If you know SQL, you can work with different relational databases like SQLite, MySQL, and PostgreSQL. It is important to enhance your skills in such programming languages required for data science. This easy-to-learn language is quite versatile and goes well with both Python and R for use in data science applications.
Highly popular, Java is an open-source, object-oriented programming language that is the base of several technologies, software applications and websites. Java is known for its first-class performance and efficiency, with its virtual machines providing a solid framework for data tools like Hadoop, Spark, and Scala. The excellent performance of Java makes it suitable for developing ETL jobs, besides performing tasks that involve a high level of storage and complex processing.
This high-level, high-performance, dynamic programming language can be used to write any application. This relatively new language that was introduced in 2011 comes with many features that can be used for numerical analysis and computational science. The language is often referred to as the inheritor of Python but has a small community and fewer libraries in comparison.
Released in 2004, Scala is widely used for machine learning and big data applications. Designed as a clearer and less wordy alternative to Java, Scala’s interoperable features makes it perfect for distributed big data projects.
Two important languages that are very useful in computationally intensive data science jobs are C and C++. The two languages are quite fast and frequently used to write the core components of popular machine learning libraries like PyTorch and TensorFlow. But learning these languages is not as simple as other options. However, individuals who are well-versed with the fundamentals of programming can master these two quite fast.
Developed by Apple, Swift has been built with mobile devices in mind. The growing use of mobiles, wearables, and the Internet of Things (IoT) is expected to boost the demand for languages that can help in mobile app developments. Swift is compatible with TensorFlow besides being interoperable with Python. The language is now open-source and can be used with Linux systems too.
This easy-to-understand and flexible language was introduced by Google in 2009. The language has C-like syntax and layouts and is being used for machine learning applications.
Designed for numerical computing, MATLAB was launched in 1984. The language’s powerful tools for carrying out advanced mathematical and statistical operations make it a good candidate for data science. The proprietary nature of this language means you have to pay for using it for academic, personal, or business use.
SAS (Statistical Analytical System) is designed for business intelligence and advanced numerical computing applications. The software environment is fast losing its popularity to modern languages like Python and R because it can only be used if you have a license for the same.
How to Choose the Best Programming Language for Your Data Science Career Path?
A successful career in data science requires you to be proficient in not one but several programming languages because there is no best programming language for data science that can solve all the problems. It is best to begin with the most popular and widely used programming languages required for data science, like Python and R and then enhance your skill set by learning others. Some of the newer languages are gaining popularity based on the demand from the cloud, artificial reality, artificial intelligence, machine learning and deep learning segments. Certain languages complement different areas of data science. Several considerations like your data science environment, the organization in which you are working, and the platform framework will help you choose a specific language.
Answers to these questions will help you select a programming language required for your data science career path:
- How does your organization use data science and what are its objectives?
- What are your career interests?
- How much programming do you already know, and which languages are you already familiar with?
- What level of difficulty are you ready to tackle as a data scientist?
- How much do you wish to learn or enhance your skills?
Programming or coding is an intricate part of applying data science to various business areas.
Almost all the businesses are on the lookout for data, from within the company and also from external sources. They need this data to understand their company and their performance better and thereby gain insights to improve.Learning to code, though seems difficult at the onset, could be learned effectively and efficiently through any of the best courses available. You could check out the Data Science course from GeekLurn.