Is Data Science Hard?

Introduction

Data Scientists have been in demand for the past few years and with organizations investing more in nurturing Data Science solutions to be data-driven in their decision-making processes, this demand is expected to be there for the next few years as well. Whether you are a student or an experienced professional, building a career as a Data Scientist could be a smart move as this job offers a promising career path and high salaries.

As per LinkedIn job reports, the Data Science industry is expected to grow from 37.9 billion USD in $2019$ to 230 billion USD by $2026$.

To become a Data Scientist, you would be required to learn and master a certain set of technical and interpersonal skills. Among aspiring Data Scientists, one question is very common: Data Science easy or hard?

Well, due to the involvement of a lot of technical skills to become a Data Scientist such as programming, statistics, etc., learning Data Science could be more challenging than other fields in technology.

Also, Data Science is a vast field and in the beginning, it might feel overwhelming to grasp all the fundamentals of it. But with hard work, focus, a strong learning roadmap, and thorough interview preparation, you will realize that it is just another field and not hard to learn the skills required to get into Data Science.

This article intends to answer questions such as whether Data Science is difficult, or is Data Science harder than software engineering, etc.

Is Learning Data Science Worth It?

Data Scientists are in demand across the world and industries. Data Scientist has already been regarded as the sexiest job of the 21st century by Harvard Business Review. And, for three years in a row, it has been named the number 1 job in the US by Glassdoor. Based on a survey by Monster jobs in India, $96$% of the companies are looking to hire professionals to fill Big Data Analytics roles by $2023$. Demand for Data Scientists is also expected to grow as we are set to generate more and more data with the arrival of the Internet of Things (IoT), and businesses become more reliant on valuable insights derived from this data for their success and growth. This also has been suggested by various reports. E.g., The U.S. Bureau of Labor Statistics has estimated a 22 percent growth in data science jobs during $2020-2030$ which is substantially higher than the $7.7$ percent growth for other kinds of occupations.

Also, Data Scientists are one of the highest-paid professionals across industries. Though, the salary of a Data Scientist depends on multiple factors such as years of experience, education, skillset, company, and location. Some companies also pay higher to Data Scientists having specialized skills such as Computer Vision, Natural Language Processing, etc. In the USA, a Data Scientist earns around 120K USD on average, and the average salary for Senior Data Scientist comes to about 145K USD. In India, the salary for a Data Scientist ranges from ₹ 4.5 Lakhs to ₹ 25.0 Lakhs, with an average annual compensation of ₹ 10.5 Lakhs. If we factor in experience, the average salary of a Data Scientist having $1-4$ years of experience comes to around ₹ 4.8 LPA, while Senior Data Scientists take home a salary of ₹ 20 LPA on average.

The above statistics reflect that it is indeed an excellent time to become a Data Scientist as it offers a promising career path along with high salaries.

How Hard is It to Get into Data Science?

Let’s have a quick look at the skillset required to become a Data Scientist. To get your first job as a Data Scientist, You must be proficient in the set of technical and interpersonal skills mentioned below :

• Statistics and Mathematics
• Programming Languages such as Python, R, SQL, etc.
• Machine Learning, Deep Learning, etc.
• Data Visualization
• Big Data Frameworks such as Spark, Hadoop, etc.
• Business Acumen/Domain Expertise
• Communication and Storytelling Skills

Now, looking at these skills might make you feel that getting into Data Science is a hard job. Data Science is a vast field, and it can feel challenging if you are trying to learn all the skills in one go. But with discipline, focus, and a learning roadmap, you can learn and master all the required skills mentioned above. Scaler's Data Science Course can help you learn and master all the skills required to become a Data Scientist. Also, these course includes hands-on experience with real-life projects, which is an added advantage when interviewing for the job of a Data Scientist.

As it is evident that if you have acquired and learned the right set of technical and interpersonal skills required to become a Data Scientist, it is not at all difficult to get your first job in Data Science. In fact, in the Data Scientist profile, currently, there is a big skill gap and talent shortage across industries. There are many open positions where organizations are looking for Data Scientists who can examine their data to derive valuable insights by applying various data science techniques that can drive their decision-making process.

Also, It is not mandatory to have an advanced degree such as a master's or Ph.D. to get an entry-level job in the Data Science field. As to the California University of Pennsylvania, around $61$% of the jobs in Data Science have a bachelor’s degree as the minimum requirement.

Another thing you should keep in mind is that this demand is only set to grow in the future as organizations become more and more reliant on the power of data. As per a survey by NewVatange Partners in $2021$, $91.7$ percent of the organizations have increased their investment in Big Data and Data Science initiatives.

Do Data Scientists Code?

The short answer to this question is, yes. Data Scientists spend most of their time coding or programming to implement various steps involved in a Data Science project. Data Scientists must have a sound understanding of various programming languages such as Python, SQL, R, etc. Below are a few instances in a Data Science project where Data Scientists are required to code for their implementation :

• Data collection from a variety of sources using SQL, Web Scraping, APIs, etc.
• Data cleaning and preparation by discarding irrelevant values, imputing missing fields, handling outliers, etc.
• Performing Exploratory Data Analysis (EDA) by applying various statistical and visualization methods to discover underlying patterns and trends in the data.
• Feature Engineering to derive and compute the most relevant features that can further improve the accuracy of the Machine Learning models.
• Build, develop, and evaluate Machine Learning based on predictive or prescriptive models.
• Deployment of the developed models into production.

Even if you are from a non-Computer Science field or a non-programming background, you can build your career in Data Science. Many Data Scientists started their careers without prior knowledge or experience in coding. You can start learning Python and R as programming languages as these have faster learning curves and provide all methods to perform the job of a Data Scientist. You should first understand the methods required to perform the basic Data Science job and gradually move on to learn advanced programming concepts. With focus and a consistent approach, you should be able to grasp programming fundamentals within a few weeks.

In the following sections, we will discuss what kind of languages are commonly used by Data Scientists.

Core Programming Languages for Data Science

Data Scientists use a variety of programming languages to implement various tasks in their day-to-day work. But there are some core programming languages that every Data Scientist must have a sound understanding of. The most used programming languages by Data Scientists include -

Python

• Python is the most popular and widely used programming language among Data Scientists. One of the main reasons for Python’s popularity in the Data Science community is because of its ease of use and simplified syntax which makes it easy to learn and adapt for people having no engineering background. Also, you can find a lot of open-source libraries along with online documentation for the implementation of various Data Science tasks such as Machine Learning, Deep Learning, Data Visualization, etc.
• A few of the most common Python libraries used by Data Scientists include:
  • Pandas :
    • It is the best available library when it comes to data manipulation and wrangling. Pandas have a lot of in-built functions to explore, visualize and analyze the data in many ways.
  • NumPy :
    • It is used frequently by Data Scientists to perform operations on large arrays and matrices. All of the operations in NumPy are vectorized methods that can enhance execution speed and performance.
  • SciPy :
    • It provides various functions and methods to perform any kind of inferential or descriptive statistical analysis of the data.
  • Matplotlib :
    • Matplotlib is a handy library that provides methods and functions to visualize data such as graphs, pie charts, plots, etc. You can even use the matplotlib library to customize every aspect of your figures and make them interactive.
  • Seaborn :
    • It is an advanced version of the matplotlib library that enables Data Scientists to plot complex visualization methods such as histograms, bar charts, heatmaps, density plots, etc with a few lines of code. Its syntax is much easier to use compared with matplotlib and provides aesthetically appealing figures.
  • Scikit-Learn (sklearn) :
    • It is the most popular Machine Learning Python library that provides a simple, optimized, and consistent implementation for a wide array of Machine Learning techniques.
    • It is an open-source library built upon NumPy, Matplotlib, and Scipy. Scikit-learn can be used to develop a variety of Machine Learning models but it lacks support when it comes to Deep Learning. It also provides other functions such as creating a dataset for a machine learning problem such as classification, regression, etc., normalizing the features, splitting the training and test data sets, etc.
  • TensorFlow :
    • Tensorflow was launched by Google and mainly focuses on implementing deep learning techniques. It supports CPU or GPU to train complex and deep neural network architectures.
    • To easily access and use the Tensorflow ML platform, Data Scientists use Keras as a programming interface. It's an open-source Python library that runs on top of TensorFlow. Using TensorFlow and Keras, you can train a wide variety of Deep Learning models such as Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, etc.

R

• After Python, R is the second most popular programming language used in the Data Science community. It was initially developed to solve the statistical problem but has now evolved into a complete Data Science ecosystem.
• Dpylr and readr are the most popular libraries to load the data and perform data augmentation and manipulation.
• You can use ggplot2 to plot the data using various visualization methods.

SQL

• SQL stands for Structured Query Language that is used by Data Scientists to query, update, and manage relational databases and extract data.
• It is a must-have skill for Data Scientists, as organizations have been storing most of their data in relational databases that require SQL for querying and collection of the data stored in it.

Other Programming Languages for Data Science

In addition to the core data programming languages Python, SQL, and R, other data science languages might be required for niche use cases. Understanding these programming languages will be a big plus when interviewing for the Data Scientist job.

Java

• Many big companies like Spotify, Uber, etc. use Java as a programming language along with Python to host their data science applications in production. Also, many Big Data frameworks such as Apache Spark, Kafka, Hadoop, Hive, Cassandra, etc. run on the JVM (Java Virtual Machine), and Data Scientists might require to deal with them depending on the organizations.
• Java is a highly scalable programming language. So developing complex functions is easier in Java compared to Python due to its scalability and excellent load-balancing features.

Scala

• Scala combines object-oriented and functional programming in one concise, high-level language. It was developed in $2004$ for the Java Virtual Machine (JVM), making it easy for this language to interact with the Java code.
• Scala is the ideal programming language when processing large amounts of data. Applications written on Scala can easily interact with the Java code or systems, making it useful for large-scale machine learning. Many of the Big Data Processing Frameworks, such as Spark, MLLib, etc., are written in Scala language. The reason Scala is used in these frameworks is because of its superior concurrency support, which is key in parallelizing a lot of the processing needed for large data sets.

Julia

• Julia is an emerging programming language that has recently gained popularity in the Data Science community. It is a high-level and general-purpose language that can be used to write code that is fast to execute and easy to implement for solving various scientific problems. It was built for scientific computing, machine learning, data mining, large-scale linear algebra, and distributed and parallel computing. Julia can match the speed of popular programming languages like C and C++ during Data Science operations.
• Julia provides packages such as CSV to load the data into Dataframes. It has other packages such as Plots, Statistics, etc. perform exploratory data analysis (EDA) on it.

MATLAB

• MATLAB is a numerical computing language developed by MathWorks which can be used for developing high-level mathematical solutions such as Fourier Transform, Signal Processing, Matrix Algebra, etc.
• MATLAB is not yet widely adopted in the Data Science community but it offers some advantages over other programming languages such as fewer lines of code, faster matrices operations, and the availability of rich ML libraries.