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understanding the fundamentals and their intermediate aspects. Most often, the aspiring Data Scientist makes a mistake by diving directly into the high-level data science and AI libraries instead of solidifying the basics of the language. That’s why it is crucial for the developers to enhance the basic features of any programming language before jumping into any specialization. Therefore, this series of Python: Understanding in 2 minutes is dedicated to aspiring data scientists who want to take the “next step” in Python after learning the basics. We shall cover the medium-level topics in this series. In this tutorial, the represents arguments and keyword arguments in Python.

What exactly are **args and **kwargs?

Let’s start by understanding what *args and **kwargs represent in Python.

You must have frequently seen such things in Python.

def function_name(*args, *kwargs): # body

Confused with these notations? Don’t worry. We all have been there.

First of all, let’s begin by understanding that it is not mandatory to write the words args and kwargs. You can go with anything unless you have the asterisk (*) sign. The asterisk sign basically takes a variable number of arguments.

The official doc’s saying

From the Python documentation on what does ** (double star/asterisk) and * (star/asterisk) do for parameters?

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax “*identifier” is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax “**identifier” is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

Confused? Let’s start with a simple example.

What is *args doing?

*args allows you to pass the desired number of arguments to the function. Args generally means arguments in this case. Let’s see an example.

def demo(*args): print(args)

Call the function

demo("Humpty", "Dumpty") # call with two arguments

Output:

('Humpty', 'Dumpty')

Call the function again, this time with 6 arguments

demo("Humpty", "Dumpty", "Sat", "On", "A", "Wall") # call with two arguments

Thus, regardless of the number of arguments passed, *args is showing you the result. Doesn’t matter if you pass (“Humpty”, “Dumpty”) or (“Humpty”, “Dumpty”, “Sat”, “On”, “A”, “Wall”). , *args will handle that for you. Note: As mentioned, you can write anything and not just args. Let’s try *whatever.

def demo(*whatever):     print(whatever)

Call the function

demo("Humpty", "Dumpty", "Sat", "On", "The", "Wall")

Output:

('Humpty', 'Dumpty', 'Sat', 'On', 'The', 'Wall')

And that’s perfectly fine!

Let’s write a function that sums up as many inputs as we want.



Call the function

sum(1,2,3,4,5)

Output:

15

Call again. This time with more arguments.

sum(1,2,3,4,5,6,7,8,9,10)

Output:

55

Doesn’t matter if you sum 1 to 5 or 1 to 10, Python will calculate the result for you irrespective of the number of parameters. This is the beauty of *args.

The case with **kwargs

Now, what about **kwargs? Well, they are not much different. The term Kwargs generally represents keyword arguments, suggesting that this format uses keyword-based Python dictionaries. Let’s try an example.

def demo(**kwargs):     print(kwargs)

Call the function

demo(name="Humpty", location="Wall")

Output:

{'name': 'Humpty', 'location': 'Wall'}

**kwargs stands for keyword arguments. The only difference from args is that it uses keywords and returns the values in the form of a dictionary. Now, let’s write a normal function and pass arguments through args and kwargs.

def list_numbers(first, second, third):     print("First number", first)     print("Second number", second)     print("Third number", third)

Call the function

args = ("One","Two","Three") list_numbers(*args)

Output:

First number One Second number Two Third number Three

Another shot!

kwargs = {"third": "Three", "second": "Two", "first": "One"} list_numbers(**kwargs)

Output:

First number One Second number Two Third number Three Conclusion: About the Author:

Hi there! My name is Akash and I’ve been working as a Python developer for over 4 years now. In the course of my career, I began as a Junior Python Developer at Nepal’s biggest Job portal site. Later, I was involved in Data Science and research at Nepal’s first ride-sharing company, Tootle. Currently, I’ve been actively involved in Data Science as well as Web Development with Django.

You can find my other projects on:

Connect me on LinkedIn

End Notes:

Thanks for reading!

I am also planning to start The Data Science Blog on my Github page. I will try to include how real companies have been working in the field of Data Science, how to excel in Data Science and/or tech interviews, and other useful content related to Python and general programming. Feel free to check them once in a while.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

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10 Essential Python Libraries For Data Science In 2023

The 10 essential python libraries for data science in 2023, the area of data science and data analytics

The 10 essential python libraries for data science in 2023 in the area of data science and data analytics. Aside from its simplicity of use and broad applications, Python has an incredibly supportive community with millions of potential answers to any problems you may encounter. Python can be used for a variety of uses including server, interface, machine learning, data science, middleware, artificial intelligence, even arithmetic, and deep learning.

List of Python Libraries for Data Science in 2023:

1.  NumPy: Machinery has seen the universe through the lens of multi-dimensional arrays, just as we do in terms of sights, scents, tastes, and touch. As humans, we can only see and sense three dimensions (X-Axis, Y-Axis, and Z-Axis). Multi-dimensional arrays reflect the ability of machines to process and grasp numerous dimensions.

Applications:

NumPy is used to keep RAM usage to a minimum

In Python, it is used as an option for arrays and lists, and it works well with multi-dimensional arrays

NumPy is used in situations where quicker runtime performance is required

2. SciPy: SciPy is an accessible science and technological computing package. It includes tools for optimization, interpolation, integration, eigenvalue, statistics, linear algebra, and multi-dimensional picture processing, among other things. Interesting fact: SciPy is built on NumPy.

Applications:

Mathematics! SciPy is used for study and science computation jobs involving mathematical operations such as algebra, calculus, difference equations, and signal processing.

3. Theano: Theano is a Python package based on NumPy that allows you to manipulate and analyze mathematical expressions, particularly matrix-valued expressions.

Computer Vision: Theano is used in computer vision for tasks such as handwritten recognition and patchy coding.

Intensive Learning: Theano, widely regarded as the Grandfather of Python programs, was one of the first to make use of GPU optimization.

4. Pandas: Possibly the most popular program among Data Analysts worldwide. Pandas is a software library that deals with data structures and offer data manipulation and analysis features.

Recommendation Systems: Websites such as Netflix and Spotify use Pandas in the background to efficiently handle huge amounts of data.

Natural Language Processing (NLP): With the aid of tools like Pandas and Scikit Learn, it is now easier to build NLP models that can be used in a variety of applications.

5. Matplotlib: Matplotlib is a Python tool that helps with data analysis and plotting to create static, animated, and live displays.

Matplotlib is useful in data visualization because it can generate a large number of early plots for big datasets.

Given that NumPy is used in the server, matplotlib is heavily used with numerous third-party modules to achieve the quickest outcomes.

6. Plotly: Perhaps is Python’s finest charting and graphing program. Plotly allows users to create low-code applications for building, scaling and deploying data apps in Python.  

Plotly can be used to create an enterprise-grade interface with the dash in the backdrop in a variety of ways.

7. Sea Born: We talked about how matplotlib has a low-level interface. Seaborn is a high-level interface developed on top of matplotlib that provides useful statistical diagrams and draws appealing visualizations.

Seaborn is used in a variety of IDEs to visualize data in a visually appealing manner

8. Ggplot: Ggplot is an abbreviation for Graphics Grammar. Ggplot is a tool designed with R in mind. It is available in Python as part of the plotline module.

A fantastic package for creating fast graphics, regardless of how layered the source data is

9.  Altair: Altair is a declarative statistical visualization tool built on the Vega visualization language.

Altair is used to autonomously display graphs for data sets with fewer than 5,000 rows in a variety of methods

10. Autoviz: A collection can be automatically visualized using Autoviz.

AutoViz can be used to better comprehend data in a variety of areas·       

The Demand For Data Science Jobs In 2023

Data science is one of the hottest industries these days, given the massive amounts of data flowing into companies of all sizes and across all sectors.

From product sales to material usage and employee work hours, among others, the variety of data is immense, and this data could be mined for valuable insights to guide the strategic choices of companies.

Data science enthusiasts had a great year in 2023. The data science industry could do even better in 2023, touching global revenues of USD 274 billion (2024: USD 189 billion) and growing at 13.1% annually.

The fourth industrial revolution will further accelerate the demand for data science professionals by creating millions of new data science jobs in the coming years.

The Demand for Data Science Jobs in 2023

This massive sprawl will bring:

Job opportunities for data scientists, data engineers, data analysts, analytics specialists, consultants, insights analysts, analytics consultants, and more

Lower costs, higher efficiencies, uncovering new markets, and an edge over competitors in the market

The prospects for the data science industry are bright. Every day, our world generates 2.5 quintillion bytes of data. This could add up to 5.2 zettabytes by 2025, which must all be analyzed.

Also read: What Is Forex Trade? 5 Untold Forex Trading Benefits + Expert Tips For Higher Forex Profit

The following trends were seen in big data in 2023

Big data analysis got a great push from artificial intelligence (AI) and machine learning (ML).

Acquisitions in the data space moved to core products instead of only adding features incrementally.

Only those companies received funding who innovated more and generated more value than others.

Producing use cases for big data became simple with frameworks for automation.

A large number of data science jobs points to the increasing demand for big data and analytics professionals, further driving the need for distinctive skill sets.

A report from the Royal Society, an independent scientific academy of the UK and the Commonwealth, said that the demand for workers with data skills grew by 231% over the past five years.

Globally, according to IBM, around 28% of the total jobs in 2023 will be data science jobs.

The US Bureau of Labor Statistics (BLS) believes these jobs will grow by at least 19% by 2026.

The data science industry has been hiring an exponentially larger number of professionals every year, making it very popular. About 400,000 new jobs came up during 2023-2024, in particular for administrators, analysts, architects, and engineers.

Along with this growth in job openings, average salary levels for professionals in big data and analytics went up in the same period by 28%.

But there is a serious shortage of the relevant skill sets, which could well disrupt the disruption dream! According to ‘The Quant Crunch’ report from IBM, machine learning, big data, and data science skills are the most challenging to recruit for, and can potentially create the greatest disruption if not filled.

The current skill development rate for data science professionals stands at a slackened 14%.

On average, a data science job remains vacant for 45 days due to a talent crunch.

In a 2023 survey of 3,000 technology leaders, KPMG found that the “Data Analytics Expert” role was the hardest to fill.

With the ever-widening demand and supply gap, this period is more than likely to extend much further. Already, according to LinkedIn, the total number of big data professionals is 2,186,308 worldwide. Against it, approximately 660,528 job postings were made in 2023.

Looking ahead, the demand for big data and data science skills is likely to continue to rise in 2023. Information technology and services could be picking up 44% of the professionals hired in this period (2024: 36%).

A smaller part, though significant, will go to sectors such as financial services, recruitment, and software.

Data Science And Its Growing Importance

Introduction to Data Science and Its Growing Importance

Data Science and Its Growing Importance – An interdisciplinary field, data science deals with processes and systems that extract knowledge or insights from large amounts of data.

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Data extracted can be either structured or unstructured. It is a continuation of data analysis fields like data mining, statistics, and predictive analysis.

A vast field, it uses many theories and techniques that are a part of other fields like information science, mathematics, statics, chemometrics, and computer science.

Some data science methods include probability models, machine learning, signal processing, data mining, statistical learning, database, data engineering, visualization, pattern recognition and learning, uncertainty modeling, and computer programming.

It is not restricted to big data, a big field, because big data solutions focus on organizing and pre-processing rather than analyzing the data.

Also, machine learning has enhanced data science’s growth and importance in the last few years.

What is the origin of Data Science?

Over the years, it has become integral to many industries, like agriculture, marketing optimization, risk management, fraud detection, marketing analytics, and public policy.

It tries to resolve many issues within individual sectors and the economy by using data preparation, statistics, predictive modeling, and machine learning.

It emphasizes the use of general methods without changing their application, irrespective of the domain. This approach differs from traditional statistics and focuses on providing solutions specific to particular sectors or domains.

The traditional methods depend on providing solutions tailored to each problem rather than applying the standard solution.

Today, it has far-reaching implications in many fields, both academic and applied research domains like machine translation, speech recognition, and digital economy on the one hand, and fields like healthcare, social science, and medical informatics on the other hand.

It affects the growth and development of a brand by providing a lot of intelligence about consumers and campaigns through data mining and data analysis techniques.

This history can be traced over fifty years back and was used as a substitute for computer science in 1960 by Peter Naur.

In 1974, Peter published the Concise Survey of Computer Methods, where he used the term data science in his survey of contemporary data processing methods.

These methods were then used in several applications. Almost twenty-two years later, in 1996, the International Federation of Classification Societies met Kobe for their biennial conference. Data science was used for the first time in the conference title, Data Science, classification, and related methods. C.F. Jeff Wu 1997 gave an inaugural lecture on the topic in which he spoke about statistics being a form of data science.

The report mentions six areas that he thought formed the base of data science: multidisciplinary investigations, models, methods for data, pedagogy, computing with data, theory, and tool evaluation.

In the next year, in 2002, the International Council for Science: Committee on Data for Science and Technology started the publication of Data Science Journal, which focuses on issues related to data science like a description of data systems, their publication on the internet, application, and legal issues.

In January 2003, Columbia University also began the publication of the Journal of Data Science, a platform for data workers to share their opinions and exchange ideas about the use and benefits of data science.

A journal that was devoted to applying statistical methods and qualitative research, this journal was a platform that provided data workers with a voice of their own in the field of data science.

In 2005, the National Science Board published Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century.

Their primary activity is to conduct creative inquiry and analysis so that data can be utilized properly and effectively by organizations across all sectors.

The growing importance of data science has, in turn, led to the growth and importance of data scientists. These data scientists professionals are now integral to brands, businesses, public agencies, and non-profit organizations.

These data scientists work tirelessly to make sense of a large amount of data and discover relevant patterns and designs to be effectively utilized to realize future goals and objectives.

This means that data scientists are gaining prime importance, and understanding data properly is reflected in their rising salaries.

According to a recent study by McKinsey Global Institute, there is a shortage of analytical and managerial talent, especially as they need to make sense of the large amount of data available in the world.

This is one of the most pressing challenges in current times. Further, this report estimates that by 2023, there will be a requirement of four to five million data analysts.

There is also a need for close to one million managers and analysts who can help consume big data results to help organizations reach their goals using resources strategically and helpfully.

Why is data science so important?

Over the past few years, it has come a long way. That is why they are integral to understanding many industries’ working, however complex and intricate.

Here are ten reasons why it will always remain an integral part of the culture and economy of the global world:

It helps brands to understand their customers in a much enhanced and empowered manner. Customers are the soul and base of any brand and have a great role in their success and failure. With data science, brands can connect with their customers in a personalized manner, thereby ensuring better brand power and engagement.

One of the reasons why it is gaining so much attention is because it allows brands to communicate their story in such an engaging and powerful manner. When brands and companies comprehensively utilize this data, they can share their story with their target audience, creating better brand connections. After all, nothing connects with consumers like an effective and powerful story that can inculcate all human emotions.

Big Data is a new field that is constantly growing and evolving. With so many tools being developed, big data is almost regularly helping brands and organizations solve complex problems in IT, human resources, and resource management effectively and strategically. This means effective use of resources, both material and non-material.

One of the most important aspects of data science is that its findings and results can be applied to almost any sector, like travel, healthcare, and education. Understanding the implications of data science can go a long way in helping sectors analyze their challenges and address them effectively.

It is accessible to almost all sectors. A large amount of data is available today, and utilizing them properly can spell success and failure for brands and organizations. Properly utilizing data will hold the key to achieving goals for brands, especially in the coming times.

That being said, it is taking on a big and prime role in brands’ functioning and growth process. Being a data scientist is a prime position for any person as they have the big task of managing data and providing solutions for their problems, both within and outside the organization.

Link new and different data to offer products that meet the aspirations and goals of their target customers

Use señor data to detect weather conditions and reroute supply chains.

Uncover frauds and anomalies in the market

Advance the speed at which data sets can be accessed and integrated

Identify the best and most innovative way to use the internet so that brands can comprehensively use opportunities.

Data science can have huge implications in retail and beyond, such as recreating the personal touch of local shopkeepers.

This shopkeeper was able to meet the customer’s needs in a personalized manner. With time, however, this personalized attention got lost in the emergence and growth of supermarkets.

However, data analytics can help brands to create this personal connection with their customers. Using this, brands must develop a better and deeper understanding of how customers use their products.

This means competitive retailers will have to understand better how customers use their products. Efficiency means that retailers will have to match the right product to the right customer, even though both these objects are constantly evolving.

What is the future of data science and data scientist?

Data science can have far-reaching implications beyond retail. These include healthcare, energy, and education. Because these fields are constantly evolving, their importance is also rapidly increasing. Healthcare needs to balance discovering new drugs with improving patient care. With data-driven solutions, healthcare can take patient care and satisfaction to the next level.

The healthcare industry is constantly evolving, and it can help them create better care for patients at all stages. Another field that can truly benefit from data science is education. Smartphones and laptops in education can create better opportunities for constructive learning and knowledge enhancement.

Another example of how it can help society is through its application and use of energy. The energy sector is today on the cusp of radical change and transformation. From oil to gas to renewable energy, we need to find new and innovative ways to use energy.

It can help us meet the increasing demand and sustainable future challenges while ensuring the best solutions. Data scientists will have to develop a wide range of solutions to meet challenges across all sectors.

This is not an easy task, so they need the resources and systems that will help them achieve this goal. Data scientists must use high-end tools to create cross-sector solutions and become creative thinkers.

All in all, data scientists are the future of the world today. Data scientists will be integral to addressing global challenges with far-reaching impacts.

Developing the skill and creativity of data scientists worldwide can transform people’s experience of life, products, and services.

Recommended Articles

This has been a guide to Data Science and Its Growing Importance. Here we have discussed the basic concept, origin, importance, and future demand of data science and a data scientist. You may look at the following articles to learn more –

Importance Of Internships In Data Science

Introduction

Internships and apprenticeships are two of the most popular methods of learning on the job and gaining crucial skills. The concept of an apprentice started dying out once masters of various sciences and crafts were replaced by employers. Eventually, internships took over from the 20th century as a standardised approach for gaining domain experience.

Internships are officially commissioned by the company and also feature documentation or certificates that justify an intern’s time at the company. Internships are essential for gaining hands-on experience and are one of the best methods of adapting to job roles that you like. For instance, if you complete an internship as a Data Scientist or a data analyst, then you will become prepared to function in any of the respective professions with ease. Internships also make other companies more likely to hire you, assuming that you are already trained in the job role.

Another factor we must take into consideration is the tools and technologies that are associated with sectors such as Data Science. There are many software and methodologies which are domain-specific and are not necessarily covered in degree programmes. Thus, individuals can use the time during their internship to acquire these necessary skills. Yes, there are online courses that teach these skills but an internship allows companies to believe that you do not need any further technical training (unless a company is using different technologies). There are programmes such as Data Science Immersive Bootcamp that can provide you with real-world job training or internships as well as teach you all the necessary skills. 

There are also various soft skills that internships help you pick up. Internships are the best approach for gaining experience for freshers. Even if you are an exceptional student, being an intern first is recommended as it is sometimes a requirement for top MNCs that are dealing with domains such as Data Science.

Benefits of Data Science Internships

Joining and then completing an internship in Data Science can help you in a lot of ways along your path to becoming a Data Scientist, a data analyst or a data engineer. Internships serve as proof of your accomplishments and your foundational abilities. Through your internship and the projects you have worked on, employers can find out your capabilities and how well you fit inside a Data Science process or a pipeline. Also, without an internship, it is almost impossible to get jobs as a Data Scientist.

Let us check out some of the main reasons why internships are essential for Data Science jobs.

Crucial Skills

The best thing about internships is that interns are not expected to know much when they join and are able to learn while being on the job. Unlike degree programmes and courses, internship roles require interns to carry out many tasks that help them gain practical experience. This helps one acquire enough knowledge about all the necessary tools, technologies, techniques and methodologies.

Formal education mostly covers foundational topics and not specialised tools and skills. Thus, one will always learn something new while being an intern. Generic curriculum from formal education generally features outdated technology while companies operating on the ground adapt to modern practices for meeting business requirements.

For example, you might have been taught Python for programming and Excel for foundational analytics during your education. However, the company you are working for requires you to use Microsoft Power BI, Azure and various libraries for Python such as Matplotlib. Nowadays, Power BI caters to various operational and strategic requirements of a company. By learning other technologies from your internship, you will seem more alluring to employers who are also using the same systems or tools.

Similarly, you can learn skills related to data pre-processing, data mining, data warehousing and tools associated with cloud computing, artificial intelligence and machine learning. 

Experience and Domain Knowledge

One can gain crucial experience with the help of internships. From the daily tasks during internships, interns can learn important domain information that will help them become better employees in the future. With enough exposure during your internship, you can even become a domain expert. For example, you might become excellent in noise removal or visualisation just by carrying out these job responsibilities during your internship.

With more experience and knowledge, you will also feel more confident, essentially reflecting your skills through high-quality work. Also, you will be adding value to the company you are working for, providing you with enough job satisfaction. Many companies have specialised training facilities and internal resources that interns can use for growing.

Employability

Freshers find it hard to get good jobs due to not having any experience. A lot of companies assume that it will take additional time and money to train freshers and thus, many of them prefer freshers who have completed an internship. Companies expect interns to already have domain knowledge and an understanding of how Data Science processes function. 

This enables people who have completed their internships to become more employable. Let us take an example where there are two candidates where one has completed an internship and the other has not. In this kind of situation, it is almost guaranteed that a company will choose a candidate who has completed an internship. Many internships also convert into full-time jobs upon the completion of the intern period.

Networking and Career Prospects

By joining an internship, you can grow your network and become acquainted with more people working in the field who wish to be involved in the future. This can help you learn the opinions of other professionals in the domain. You can also identify the future of the sector better with the help of other senior resources. Other professionals working in Data Science can help better guide you in terms of upskilling yourself and your career. For instance, domain experts might recommend a tool such as SAS to upskill yourself in.

For Data Science, growing your network is crucial to stay updated about the latest market trends and innovations in technology. There are also many great career prospects you might find out about after joining an internship. Many companies also refer their interns to their partners or to other organisations.

Conclusion

An internship is like a journey that allows you to gain skills without any compulsory cost. As a matter of fact, many internships even compensate interns, allowing them to learn while they earn. Finally, without an internship, it might be extremely hard to get a good data scientist job, if not impossible. Bootcamps such as Data Science Immersive Bootcamp by Analytics Vidhya can solve this problem by offering a 100% job guarantee. Read more about a comprehensive comparison among Data Science bootcamps vs degree vs online courses here. 

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Why Programming Is Essential For Data Science

Introduction

I graduated with a degree in Bachelor’s of Commerce from Delhi University and decided to pursue Data Science as a career. During the first 3 months of my learning journey where I was taught basic programming, I quickly jumped from there without paying any heed to practice. Call it my ignorance or the excitement to learn algorithms and build models, I regret that decision to this data.

The time I could have saved in long run by simply being good at basic programming could have been unfathomable.

And Yes! You heard it absolutely right. You do not need to have hardcore programming skills to be a data scientist. Being really good at the basic skills will help you in ways that might have skipped your thought.

So in this article, we are going to explore in detail the role programming plays in data science. If you are from a non-programming background transitioning to data science, search no more.

Table of Contents

Real-Life Scenarios

Kaggle competition code interpretation

Data Science Learning Journey

What aspect of Programming should you be Good at for Data Science?

Role of Programming in Data Science Life Cycle

Programming Languages for Data Science

Real-Life Scenarios

Let us go through a couple of real-life scenarios that data scientists go through, where good programming skills could have saved you a lot of your time.

Scene 1 – Kaggle Competitions

Suppose you are participating in a Kaggle Competition with a very large dataset and 30 days’ time to complete. Here your programming skills will not only determine whether you complete and submit your model, but the quality of your work will also be dependent on how good you are at your programming skills.

Often, you need to learn, understand and implement some new code that is complex but efficient in cleaning such vast data. Now if you do not have the capability to understand the code syntax, you will either miss out on the deadline or you will only be able to do basic cleaning and create a below-par model which will not fetch you any medals.

Practicing is key when it comes to excelling in programming skills.

Scene 2 – Data Science Learning Journey

Now, if your programming skills are not good by this stage, there is a very high chance that you will not understand what each step meant and will definitely hinder your journey.

What aspect of Programming should you be Good at for Data Science?

As I said before, a person from a non-programming background transitioning to data science should be good at the basic tasks of programming. Let’s have a look at these tasks-

1. Constructing Conditional Statements

This is one of the easiest and the basic programming skills that a data scientist should know. This simple statement has immense applications when it comes to breaking our own and analyzing data.

A practical example of the use of conditional statements will be an HR trying to identify whether an employee is eligible for promotion or not based on his annual performance metric. Let’s say the benchmark score is 75. So the HR can easily use the conditional statement and segregate employees having a score of 75+ into the eligible for promotion category and else, not.

2. Looping Constructs

These lines of code help you command your language to perform a repetitive task without you manually typing the code every time a task has to be repeated.

For example, if you want to command your language to print “Larry is a good player” 1000 times, you simply use a looping construct (for loop to be precise) to print the statement 1000 times.

3. Functions

This is the most ignored yet the most important aspect of programming. Even though to perform various functions there are pre-defined libraries to solve the problem, in many situations you are required to define your own functions to efficiently perform the function.

For example, let’s say that in multiple steps of model building you are required to add a number(say – 5) and then multiply it with the result of the previous code line. Rather than repetitively writing multiple lines of code, you can simply pass the function in one line each time.

4. Data Structures

Data structures are constructs around which you do your programming. Different data structures help you store different types of data in a particular manner. Prominent data structures which you need to understand well include-

Dictionaries

List

Tuple

Set

5. Indexing Dataframe

Once you have the data imported to your programming language, you will be required to slice and check only a certain portion of the data. Or you will be required to index through data having a particular variable value.

For example, you work in a hospital and you need data of all patients currently at the 2nd stage of cancer.

Our Certified AI & ML BlackBelt Plus Program teaches you all the programming you need with the necessary 1:1 mentorship required at each stage.

Role of Programming in Data Science Life Cycle 1. Data Extraction

Once you identify the objective, you need to collect the relevant data. Either the data will have to be imported from your local system or you will have to retrieve it from the database of the organization. In both cases, you are required to code. And the programming skills required to extract data from a database are a tad bit technical than the former activity.

2. Data Cleaning

Clean data is an absolute must for your model to understand the rules of the data and create the best possible models. Identifying and imputing missing values, variable transformations, creating multiple loops, and defining functions are some of the common activities for which you will be required to code.

3. Data Visualization

Before you create models, a major effort is exerted in understanding each and every variable of the data. You will be required to individually visualize them to check distributions, plus you will also need to compare 2 variables to check if they have a relationship or not.

Furthermore, often you will need to make complex visualizations, and good programming skills go a long way.

Programming Languages for Data Science

With the world of data science progressing faster and faster, myriad programming languages have been developed. Let’s have a look at the most prominent ones. Some of the most prominent languages include-

Python

R

Julia

Java

C/C++

I recommend Python as the language to begin with. It is the most popular programming language in the data science community. From courses to data science competitions, a majority of activities in the data science domain happens around Python.

Python is a general-purpose, high-level interpreted language that has been growing rapidly in the applications of data science, web development, rapid application development. Its ease of use and learning has certainly made it very easy to adapt for beginners.

To learn about other languages and choose the right programming language for you, I recommend you go through the following article-

5 Popular Data Science Languages – Which One Should you Choose for your Career?

End Notes

I hope you understand how paramount the concept of programming is for a data scientist to be efficient in his tasks. Better programming skills will definitely provide the necessary edge that multi-disciplinary fields like data science requires.

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