Everything you need to know to start a career in Data Science

If the idea to turn data into action sounds interesting, then data science may be the right career path for you! 

The numbers just confirm it. The job market for data science professionals expanded by 35% this year. When growing at this speed, imagine what will happen in just a few years.

The good news is that the supply of data professionals can’t keep up with the demand. This means that now more than ever, it’s the perfect time to enter this rapidly-growing job market, and in this article, you will see how.


What is data science?

Data science combines statistics, probability, and machine learning with domain knowledge to produce insights from data. 

These insights can vary from predicting trends, outcomes, patterns, and so on. They affect almost everything we encounter, like shopping recommendations, curated playlists, detecting diseases, etc. 

Moreover, data scientists are at the forefront of developing chatbots and self-driving cars. How cool is that?


What are the top data science job roles?

1. Data Scientist

  • Recognizes relevant data sources;
  • Collects both structured and unstructured data;
  • Enhances the data collection process.
  • Sources missing data;
  • Organizes data in usable formats;
  • Develops machine learning algorithms;
  • Develops predictive models.


2. Data Engineers

  • Analyzes and organizes raw data;
  • Develops pipelines and data systems;
  • Predicts trends and patterns;
  • Adjusts data for prescriptive and predictive modeling;
  • Creates prototypes and algorithms.


3. Machine Learning Engineers

  • Designs Machine Learning systems;
  • Researches and implements ML tools and algorithms;
  • Selects suitable data sets and data representation methods;
  • Acknowledges diversity in data distribution that can affect model performance;
  • Verifies the quality of the data.


These are just the main titles, but there are so much more job roles that go under a different title, such as:

  • Business intelligence (BI) Analysts;
  • Data Storyteller;
  • Systems Analysts;
  • NLP Engineers;
  • Data Architects;
  • Deep Learning Specialists.


What data science languages should I know?

You probably know that hundreds of different programming languages have their own applications in data science. 

Don’t panic! 

To get started, you need to learn just 3 of the most popular programming languages data professionals use.


1. Python

This is the favored language among programmers across every discipline due to its versatility and readability. 

Python’s broad range of powerful libraries and packages make it easier to model and calculate every application of data science.


2. R

This is a statistical programming language. It includes variable types, data structures, and tools like analysis and visualization. 

R can perform a variety of functions like linear regressions and t-tests. You can also use it with RStudio to quickly inspect its output.


3. SQL

SQL is great to query and edit the data stored in your databases. Data professionals use SQL to extract data from a database before analyzing it with R or Python. 

This is an incredibly versatile language. Its syntax for basic queries is similar to other relational databases like PostgreSQL, SQLite, and MySQL. 

Along with the 3 languages listed above, there are other less-known programming languages data scientists use. 

Which language you will need to know is mainly determined by your company goals. So, always be open to learning new languages if needed!


What are the main data science skills you need?

Understanding how to code is just half the battle. 

Here are the primary skills you should have (if not, hurry up to achieve them to make yourself more competent on the job market).


  • Critical thinking

Turning vast amounts of raw data into something useful can be challenging, right? Your ability for critical thinking can land you a hand!

Before you start transforming raw data, you need to know what problems you need to solve and what types of data you will need to resolve them.


  • Data manipulation and analysis

Once you have the problem in mind, collecting and organizing relevant data is the next step.

This is the part where data scientists use tools like SQL or APIs to extract relevant data from larger datasets. Next, they apply languages like Python or R to explore the data and visualize it.


  • Great communication

In any job role, excellent communication skills are always a great asset! Since data scientists work closely with non-technical teams, they need to effectively communicate with managers, stakeholders, and executives and present their findings in easily understood terms.

While these are the essential skills that can form the foundation for your data science career, there are many more. 

But don’t worry. It’s just a matter of time when you will obtain other skills, depending on your company’s requirements.


How to start your career in data science today?

The road towards a data science career is long, but now more than ever, it’s easier to get there. 

Now that you understand the foundations you need to have to land a career in data science, the next step is to get you there. 

Whether you are already familiar with programming languages or have no clue what data science is about, but you are eager to learn and practice, we’ve got you covered! 


The enrollment for the next Data Science Bootcamp is in progress! 

Make an appointment with our student success manager for more details about the program!

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