This blog discusses the essential prerequisites needed to pursue a career in data science in 2024. It covers recommended educational qualifications, core technical skills like programming, statistics, machine learning and databases. The blog helps readers assess their preparation and identify any gaps to focus on.
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If you’re hoping to get your foot through the door of the Data Science industry, then read on to learn about the career.
Fundamentals of Data Science
Data science is a field that blends the multiple disciplines of machine learning, algorithms, data inference, programming, mathematics, and statistics to extract useful information from raw data and solve complex problems.
The market for big data analytics is expected to reach a whopping $103 billion by 2023. Big data analytics involves analyzing large amounts of data to find important patterns and insights that can help businesses make smarter decisions. It’s a powerful tool that helps companies understand customer behavior, predict future trends, and improve their operations. As more and more data becomes available and technology keeps advancing, the demand for big data analytics continues to grow. Businesses across various industries are realizing their potential and investing in analytics solutions to gain a competitive edge.
Data Scientists are superheroes who make sense of unstructured, messy, raw data from sources that don’t fit into databases neatly, such as emails, social media feeds, and smart devices. They collect, cleanse and organize data.
The main use of Data Science is making decisions and predictions, using machine learning, prescriptive analytics, and predictive causal analysis. It is the field that helps businesses, not just in recognizing their markets and improving their decision-making, but also in getting them closer to their customers.
Over the years, data science has evolved as the most promising career option for skilled professionals. Data Science has already been declared The Hottest Profession of the 21st Century. Let’s see what makes it so popular and why Data Science is an intelligent option for the future.
Why Opt Data Science?
Here are a few reasons why you can consider pursuing a career in data science.
Increasing Demand
As data-driven decision-making is becoming more and more popular with time, each company, be it big or small, seeks professionals who can analyze and understand raw data, and help the company utilize it efficiently.
As per Searchbusinessanalytics.techtarget.com, year after year, the demand for data scientists is booming and will only increase. According to Linkedin, there has been a 29% increase in demand for data scientists, on average, and a 344% increase since the year 2013.
As per a report by McKinsey & Company, by 2018, the United States of America had around 140 – 180 thousand fewer Data Scientists and around 1.5 million fewer Data Analysts and managers than it needs.
According to a report by TowardsDataScience.com, there is a Major Shortage of Data Scientists in India. By August 2020, about 93,000 positions in Data Science were vacant in India.
Such a high demand and the extreme lack of skilled professionals make it a good field to gain expertise and pursue a career.
High Salaries
According to Glassdoor.com, the Average Salary of a Data Scientist in India is ₹9,16,500 per annum. This is significantly higher, compared to the Average Software Developer Salary in India, around ₹5,00,000, which is the chosen career by most Computer Science graduates.
As per Indeed.com, Data Scientist’s Salary in the United States, on average, is $119,353 per annum and the average salary in the United Kingdom, Canada, France, and Australia are £52,137, C$79,313, €44,730, and AU$92,157 per annum, respectively.
Evolving Field
Data Science is a dynamically developing subject due to the huge and ever-increasing amount of data in the world, as well as the growing demand for data scientists.
You will have exciting opportunities to work on emerging technologies like Machine Learning and Artificial Intelligence, as well as rapidly growing technologies such as Edge Computing, Blockchain, and Serverless Computing if you pursue a career in Data Science.
If the increasing demands of Data Scientists and the good pay scale have convinced you to pursue a career in Data Science.
Now, you must be wondering what are the prerequisites for Data Science and where to start. So let’s discuss that next.
Data Science Prerequisites
Data Science, just as the name suggests, is all about data. So the very first, and most important prerequisite to learn Data Science is one’s love for data, their understanding of it, and their ability to deal with data.
Data Scientists can be seen as big data wranglers. They analyze huge sets of data, both structured and unstructured. They combine Computer Science, along with mathematics and statistics, and process, analyze, and model data, to interpret meaningful results.
To do this they need knowledge of a wide variety of disciplines. These Data Science Prerequisites can mainly be categorized into two types.
- Technical Data Science Prerequisites
- Non-Technical Data Science Prerequisites
“I have quantitative problem-solving skills but don’t have a degree in statistics. Can I learn Data Science?”
“I have a good understanding of Excel and statistical analysis. Can I become a Data Scientist?”
“I am fairly good at programming, and love numbers, but don’t have a masters’ degree. Can I pursue a career in Data Science?”
Such questions might be popping into your head, so let’s address them.
Educational Requirements
If you have graduated in disciplines like computer science, information technology, mathematics, statistics, engineering, or any other related field, you already fulfilled the minimum educational requirement to pursue a career in Data Science. Additionally, an interest in statistical analysis and programming will help you master the skills needed to become a Data Scientist.
The most common disciplines of study for data scientists are Statistics and Mathematics, (32%), Economics (21%), Computer Science (19%), and Engineering (16%). A degree in these fields will help you learn the skills that are needed and will enable you to transition into the Data Science industry easily.
While having a bachelor’s degree is the minimum requirement, most companies give preference to candidates with higher educational qualifications, at least a master’s degree, as per the US Bureau of Labor Statistics.
Most Data scientists are highly educated. According to KDnuggets, amongst every 100 data scientists, 88 of them have at least a Master’s degree and 46 of them have PhDs. A strong educational background is very essential to gain the deep knowledge required to be a successful Data Scientist. Moreover, many data scientists also take up online training and courses, to keep up with the latest technologies and skills.
Once you have the educational qualifications, the next and one of the most important Prerequisites for Data Science is the skills that are needed. So let’s learn about them.
Technical Skills Required
SQL Databases
In the year 2016, CrowdFlower performed a study. After analyzing almost 3500 Data Science job descriptions posted on LinkedIn, they created a list of the top 21 skills that appear in job descriptions most often.
To nobody’s surprise, SQL topped that list. It was found to be the most important of all Data Science Prerequisites and was a requirement in 57% of the job postings.
SQL is a programming language used to manage and query data that is held in a relational database management system. It is used to read, retrieve or update data, insert new data, or delete existing data. It also helps in transforming database structures and carrying out analytical functions.
Companies expect candidates to be able to write complex SQL queries, in order to get insights from data. SQL helps you access data and work on it. It is very concise when it comes to commands, and hence, reduces the amount of programming you need to do and saves a lot of time. It gives a better understanding of relational databases.
In the same survey by CrowdFlower, the next most required Data Science skill was Hadoop, cited in 49% of job descriptions. Even though it is not a strict requirement always, it is still one of the Prerequisites for Data Science heavily preferred by employers.
As a data scientist, you will encounter situations where the amount of data that you have exceeds the memory of your system.
In this case, you would need to send that data to different servers. This is where Hadoop’s role comes into play. Hadoop can be used to quickly convey data to various points in the system. It can also be used for exploring data, filtering, sampling, and summarizing it.
Python Programming
After Hadoop, Python was the 3rd most required Data Science skill, cited in 39% of the job listings. It is also the most popular programming language among data scientists these days.
Python is very versatile and can be used in almost all the processes in Data Science. Be it data mining or running embedded systems, python can do everything, and because of this, 40% of the people that participated in a survey by O’Reilly said that they used Python most often.
Pandas, a Python library, is used for data analysis and can do anything from plotting data with histograms to importing data from spreadsheets. Python can take data in various formats and import SQL tables to your code easily. The Python packages you need to master are Numpy, Matplotlib, PyTorch, Pandas, Scikit-Learn, and Seaborn in Python.
R Programming
Following Python, the next skill in the list was R Programming, mentioned in 32% of the job postings. R is a language specifically designed for Data Science. It can be used to solve any Data Science related problem that you might encounter. It is the most popular language among Data Scientists.
In fact, 43% of data scientists prefer to use R for solving statistical problems. It is one of the most important Data Science Prerequisites. However, the learning curve is steep. It is difficult to master, especially if you already have expertise in any other programming language.
R can implement ML algorithms to give us a vast variety of statistical and graphical techniques like time-series analysis, clustering, classical statistical tests, etc. It is used for calculations and data manipulation. Tidyverse, Ggplot2, Stringr, Dplyr, and Caret are some of the things to master in R.
Machine Learning and Artificial Intelligence –
ML helps in analyzing large amounts of data using algorithms. Using Machine Learning, major parts of a data scientist’s job can be automated.
Only a small percentage of Data Scientists are proficient with advanced machine learning techniques like adversarial learning, neural networks, reinforcement learning, Outlier Detection, Time Series, etc.
The most skilled data scientists are highly familiar with advanced machine learning techniques such as recommendation engines and Natural Language Processing.
If you want to stand out from the crowd and be one at the top tier, knowledge of machine learning techniques such as logistic regression, supervised machine learning, decision trees, Survival Analysis, Computer Vision, etc., is a must.
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Mathematics and Statistics
Mathematics is one of the very popular prerequisites for data science. Probability and Statistics are used for data imputation, visualization of features, feature transformation, model evaluation, dimensionality reduction, feature engineering and data preprocessing.
Multivariable Calculus is used to build machine learning models. For model evaluation, data preprocessing and data transformation, we use linear algebra. A matrix is used to represent a data set.
There is no defined syllabus for what you need to learn in mathematics and statistics but here are a few topics you should be familiar with –
- Mean, Median, Mode, Variance, Standard Deviation, and Percentiles
- Bayes Theorem And Probability Distribution (Normal, Poisson, and Binomial)
- Covariance Matrix and Correlation Coefficient
- Mean Square Error and R2 Score
- Statistical tests like p-value, hypothesis Testing, and chi-square
- Multivariate Functions, Cost Functions, and Maxima and Minima of a Function
- Step Function, Sigmoid Function, Logit function, and Rectified Linear Unit
- Vectors and Matrices; Transpose, Inverse, and Determinant of a matrix
- Dot Product, Cross Product, Eigenvalues, and Eigenvectors
Apache Spark
Just like Hadoop, Apache Spark is also a big data computation framework. The difference between the two is that Spark is comparatively faster.
Hadoop reads data from the disk and writes it back to the disk, whereas Spark catches its computations in the memory of the system, making it faster than Hadoop. It is one of the most popular data science prerequisites worldwide.
Spark is designed for Data Science specifically, to run complicated algorithms faster. It helps you save time when you’re processing a big sea of data. In addition to that, it also helps Data Scientists handle large, unstructured, and complex data sets.
Spark has made it possible to prevent data loss. Its strength lies in the platform which easily carries out projects and its speed.
Data Visualization
Data Visualization is a very important Prerequisite for Data Science. In simple words, data visualization is a representation of data visually, through graphs and charts. A data scientist should be able to represent data graphically, using charts, graphs, maps, etc.
Visualization is very important to make sense of the large amount of data generated each day.
There are multiple components in data visualization –
- Data Component – The first step in visualizing data is understanding the type of data, for example, it could be continuous data, discrete data, or categorical data.
- Geometric Component – This means deciding what kind of visualization will best suit your data- histograms, bar plots, heatmaps, scatter plots, pair plots, line graphs, etc.
- Mapping Component – In this component, you decide what variable you should use as the x-variable (independent variable) and y-variable (dependent variable). This is especially important for multi-dimensional datasets.
- Labels Component – This has the axes labels, legends, titles, font size, etc.
- Scale Component – Here you decide which scale you will be using- log scale, linear scale, etc.
- Ethical Component – This is to make sure that the visualization you have done, tells the true story, and doesn’t mislead the audience.
Excel and Tableau
Excel and Tableau are another two very important Data Science Prerequisites. Both these Data Science tools are very important to understand, manipulate, analyze, and visualize data.
Excel is used when there are a lot of manipulations and computations that have to be done on the data. Tableau is used when you need to gather all the data in one place and display it using powerful visualizations on the dashboard.
A combination of both can be used where all the major calculations can be done in Excel and then the final data set can be imported to Tableau for further processing, analysis, and getting more insights.
While most of us are already familiar with the basics of Excel, to use Tableau to its fullest, you would need some training.
Now that we’ve learned about the Technical Data Science Prerequisites, let’s shift our focus to the analytical, Interpersonal, and non-technical Data Science Prerequisites.
Interpersonal and Analytical Skills
Like every other field, just technical skills are not sufficient to make it big in the Data Science industry. Having an analytical mind, problem-solving abilities, and interpersonal skills is very important. Let’s go over some such skills that are needed to pursue a career in Data Science.
Communication skills
Data Scientists should be good communicators, to easily, fluently, and clearly translate technical findings to the other non-technical teams like Sales, Operations, or Marketing Departments. They must be able to provide meaningful insights, hence enabling the business to make wiser decisions.
This also includes the ability to create a storyline around data, to make it easier for anyone to understand. The focus needs to be on the results that were found and the impact they can have, not on the data that was analyzed.
Teamwork
The ability to work in team settings efficiently is very important for Data Scientists. They can’t work as lone wolves and have to work with product managers, designers, software developers, and company executives.
They have to come up with solutions to create better products, create data pipelines and develop strategies. They need to work with everyone, from all departments to clients, to generate better business solutions.
Business Strategy
Data Scientists should understand businesses, and the problems that are faced, and should have the ability to provide solutions by conducting analyses. This helps them use data in a way that is helpful to the organization.
One of the most important Data Science Prerequisites is to understand the industry that you’re working in and the problems that your company wants to solve. You should be able to understand how the problem might affect the business.
To solve the problems, you must know how businesses operate, so your efforts can be directed in the right direction.
Intellectual curiosity
In the words of Albert Einstein, “I have no special talent. I am only passionately curious.”
As per Burtch Work’s Tips for Hiring Data Scientists, Frank Lo listed Intellectual Curiosity as the number one intangible for Data Science. Curiosity is the desire to gain more knowledge, and learn more things.
According to a study by Harvard Business Review, Data Scientists spend about 80% of their total time discovering and analyzing data. For a field evolving as quickly as Data Science, you need to learn to ask questions and keep up with the pace.
To succeed in the data science field, you need to be curious. For instance, you might not find much insight in the data initially, but curiosity will make you find more insights and answers.
It is very important to understand why some people do good in the data science field and others do not, even after training and mentoring. Data Scientists should be all-rounders.
They don’t just need to have a knowledge of programming and managing databases, but should also be great communicators, and have a natural curiosity for the world around them and this will make them eligible for Data Science.
Now that you’ve learned about not just technical, but also the Non-Technical Data Science Prerequisites, Do you see a Data Science Career for yourself?