Become a Data Scientist in 2021

The demand for data analysis has exploded. Here’s how to create a shift to data science

In 2017, an IBM study found that 90% of all data in the world at that time was created in the past two years. In today’s increasingly digitized world, it is clear that big data has become a highly valued resource. In addition, the amount of data increases exponentially. Indeed, in the past year alone, it has been estimated that every person on the planet has generated 1.7 megabytes of data per second.

With data from a variety of sources – digital media, web services, business applications, and IoT-connected machines – big data analytics is used to prevent money laundering, optimize disease management, streamline construction projects , forecast weather conditions, optimize manufacturing production and anticipate what customers want next.

Today’s industries and governments increasingly depend on data to anticipate trends, understand behaviors, streamline market or public engagement, define and visualize strategies, scale operations, predict outages. machines, make better and smarter decisions, and more.

This boom in data analytics means that the demand for data scientists has exploded.

Make sense of data

Organizations around the world need data scientists to make sense of the huge pools of data they create and collect. In 2019, LinkedIn co-founder Allen Blue estimated that the demand for data science-based jobs in industries like education, marketing and manufacturing alone had increased 15 to 20 times in just three years.

Staying relevant in today’s fast-paced world means businesses and governments must constantly innovate, making very informed – and fair – decisions about which products and services to invest in. That’s why turning data into valuable and actionable information has become a mission. critical.

However, while organizations collect and store tons of information in their databases, a much larger amount of data to process is unstructured in nature. In other words, it comes in many different shapes, sizes and shapes, which makes it difficult to manage and analyze. This is where data scientists come in.

The problem is, there aren’t enough data scientists for everyone. Which makes entering data science one of the most exciting career options available today.

What it takes to organize a career change

In recent years, the focus has been on encouraging more people to pursue higher education in STEM subjects. But the expertise to become a data scientist requires education to develop skills that blend both art and science – STEAM (science, engineering, art and math). In other words, data scientists need a unique blend of creative, academic and technical skills: you are an investigator, a coder, a scientist, a mathematician and a storyteller!

Organizations now recognize that a wide range of individuals have the right thought processes to work in data science. Besides people with a background in computer science and analysis such as statistics and engineering, this includes people with a background in physics, chemistry and biology. Big tech companies have also found that liberal arts graduates do very well in data science roles as well.

With organizations eager to retrain people and retrain the data science workforce, let’s take a look at the five critical steps that will need to be mastered in order to move into a career in data science.

1) Start with science: math skills

The ability to learn and understand machine learning techniques is the key to data science. To do this, you will need to be good at math. Statistics reign supreme in data science, especially probability theory, so gaining an understanding of these areas at a fundamental level will be essential.

Linear algebra forms the basis of machine learning algorithms, like those used in Spotify’s song recommendations, so it will be important to master the basics. Likewise, aspiring data scientists will need a solid grasp of computational skills in order to understand how machine learning neural networks use backpropagation to learn new patterns.

Finally, data scientists will need to confidently manage such things as functions, variables, equations, and graphs as foundational skills, while more complex and broad scientific knowledge, such as the binomial theorem and its properties are also important.

2) take advantage of the right tools

Today’s data scientists need to be confident that they are using a range of data analysis tools. Start by mastering the best basic tools before diving into exploring other relevant tools that might be useful for more specialized data analysis or data visualization challenges.

The main basic tools in use today include Power BI, which has a drag-and-drop interface that makes it an easy-to-use data visualization tool. Tableau, which supports creating simple and attractive models for everyone to understand the data. Finally, familiarize yourself with AWS and learn to work with it; this tool makes it easy to create visualizations and perform statistical analysis in-the-moment to quickly generate business insights from data.

3) Build muscle: acquire the state of mind of a coder

The sooner you can start learning and mastering programming languages, the better. Python, R and SQL are considered the tools of the trade and data scientists use them regularly.

One of the most commonly used programming languages ​​today, Python is ideal for those new to data science. Offering an intuitive and easy-to-learn syntax that makes it a popular choice for beginners and professionals alike, Python is the preferred language in machine learning, deep learning, AI, and others. fields of data science.

Prized for its manageable statistical analysis and programming, R provides large sets of libraries and frameworks that make it ideal for developing machine learning algorithms and building statistical models. Any business that wants a large collection of their data to be analyzed and visualized will look for developers who are proficient in R.

Finally, used for updating, querying and manipulating databases, SQL is the lingua franca of data analysis and the programming language used to “talk” with relational databases. Easier to learn than general programming knowledge, it is easy to master SQL in a matter of months.

4) never stop learning: find a mentor

Finding the right professional mentor will get you to your data science goals in years, not decades. While acquiring the basic skills will help you embark on the path to entry-level positions where you can learn quickly on the job, guidance from someone who really knows what they’re doing will help you capitalize on your strengths and identify future skills to develop. .

A good mentor will help you navigate work situations, provide constructive criticism, point you to technical resources and expertise, and share different perspectives on solutions. If you don’t have good in-person mentors, support your continued development by seeking virtual mentorship.

5) tell the rich story of data

Business and leadership skills separate the best data scientists from the rest. In addition to knowing their own field and the industry in which they work, data scientists must also communicate effectively with non-technical people across the organization.

To tell the rich story of data, people working in data science need to develop skills in communication, collaboration, and proactive problem solving in relation to specific business cases. Ultimately, it is this integration of technical know-how with soft skills that makes a complete data scientist who is respected as a trusted advisor to the business.

With big data now a force to be recognized, industry leaders are eager to harness its many benefits. Today’s data scientists deploy their technical and creative skills to tackle the real problems facing organizations and becoming effective in their role depends on their ability to communicate the results of the data in a language that the uninitiated. will understand. More and more, this means becoming able to work in cross-functional teams in which everyone can deploy their individual talents to generate real value from an organization’s data.

Sean N. Ayres