If you decide to learn data science just for the sake of landing a good job, you may lose steam midway unless you are passionate enough about the domain, have a clear idea of what you want to do, and a roadmap that would let you achieve your goals.
If you love the wide variety of data that gets generated today and want to learn how it can help you take real-world, data-driven actions, data science is the field you should consider to get into.
But before answering how long it would take you to learn data science to land a good job, it’s important to know where you are in your journey and what skills you need to acquire to meet your career goals.
You may often come across professionals working in the field of data science, who suggest a lot of different things and paths to take. It’s true that you can’t learn it all in a day or even within just a few months.
But don’t get overwhelmed when someone tells you that you need to learn it all — right from the fundamentals to programming, machine learning, statistics, database technologies, and several other domain-specific technologies.
It all depends on how quick a learner you are, your background (like whether you have a Mathematics/Statistics background, or have worked in the IT industry), and the time and effort you are ready to put into learning and mastering data science.
So, before you decide to learn data science, let’s delve deeper to check what the job titles are that you can aim to land once you have finished your course.
In the field of data science, three job profiles that are often touted as the “big three” are Data Analyst, Data Engineer, and Data Scientist. Let’s take a look at the job responsibilities and skills required for each.
Though some may call it an “entry-level” position in the domain of data science, not all data analysts are junior. The salaries you get too can vary widely based on your experience and the nature of the job you do. No wonder why it features highly on the list of many who learn data science.
Primarily, your job as a data analyst would include looking at industry or company data and analyzing it to find insights that can answer business questions and help in making business-driven decisions.
An instance could be where you are asked to analyze sales data from a current marketing campaign to evaluate its efficiency and spot strengths and weaknesses. The task would involve getting access to the data, probably cleaning it, and executing some statistical analysis to answer related business questions followed by visualizing and communicating these results to other teams in the company (and even to those in the management) so that they can act upon it.
Over time, you may need to work with different teams within a company. So, you may help the company CEO to find reasons into what the company did right (or wrong) in its expansion plans by using data one month, while the next month, you could be dealing with marketing analytics. Unlike data scientists who often find interesting trends on their own and predict future results, your job will typically involve mining useful insights from data and answering business questions that are given to you.
Though your job specifics may vary from position to position, the skills you need to handle the job of a data analyst successfully include:
- Intermediate data science programming in either R or Python along with the use of popular packages
- Data cleaning
- Intermediate SQL queries
- Probability and statistics
- Data visualization
Additionally, you should have good communication skills to convey complicated data analysis with clarity and in an easy-to-understand manner to people having no programming or statistics background.
When you consider the career prospects as a data analyst after you learn data science, you will have a fairly open-ended career path as you will get to work in a wide range of positions. Many professionals in this field continue building their data science skills, usually with an emphasis on machine learning, to make their transition into the role of a data scientist easier. You may even work toward becoming a data engineer in case you are more interested in data infrastructure, software development, etc. Thus, taking up the post of a data analyst after you learn data science could be a prudent move.
This job profile involves a lot more programming and software development skills while needing less statistical analysis skills. When you work as a data engineer with a data team, it would be your responsibility to create data pipelines to get the latest marketing, sales, and revenue data to data scientists and data analysts speedily and in a usable format. You are also likely to be responsible for creating and maintaining the infrastructure required for storing and accessing past data quickly.
The skills that you will need, in general, for this position are:
- Advanced programming skills (possibly in Python) for working with massive datasets and creating data pipelines
- Advanced SQL skills (and possibly knowledge of Postgres)
When you consider your career prospects, you can draw upon your skills and continued experience to move into other software development specialties. You may even have the potential of moving into management roles as the leader of the data engineering team.
This is often touted as the most coveted job with a fat pay packet, which is why many who learn data science have their eyes set on becoming a data scientist.
Though your job would involve doing several things, which are the same things done by data analysts (such as obtaining, cleaning, and visualizing data), you would also usually set up machine learning models to make precise predictions about the future by using past data.
When you learn data science and take up the job of a data scientist after finishing your course, you will often enjoy more freedom than other job profiles to chase your own ideas and experiment to locate remarkable trends and patterns in the data that the management might not have even given a thought to.
As a data scientist, you will need the skills of a data analyst along with the following:
- A rock-solid understanding of both unsupervised and supervised machine learning methods
- Programming skills in Python or R (and preferably, familiarity with other tools such as Apache Spark)
- A strong base in statistics and the capability of assessing statistical models
When you consider your career prospects, you can start work as a junior data scientist, and then rise to become a senior data scientist or decide to specialize further in the field of machine learning to become a machine learning engineer. Either of these career paths would bring a significant pay raise your way, which explains why many who decide to learn data science aim to become data scientist, often as a stepping stone to transition into other high-paying jobs. You may even contemplate roles with a bend toward management like chief data officer, lead data scientist, etc.
Here’s a curriculum roadmap to learn data science and start your career in this field:
- Python Programming: Since this is a fundamental skill that you will need, get to know the syntax of Python. In your quest to learn data science, focus on understanding how you can run a python program in different ways.
- Linear Algebra and Statistics: When you decide to learn data science, this would be a precondition for data analysis and machine learning. If you already possess a solid understanding in these fields, you can spend just a week or two to brush up on the key concepts. Remember to emphasize especially on descriptive statistics because the ability to understand a data set is a skill that’s extremely valuable. When your primary aim to learn data science is to get a good job, you surely need to get robust knowledge of linear algebra and statistics.
- Pandas, Numpy, and Matplotlib: You will need to learn ways to manipulate, load, and visualize data. In your road to learn data science, mastery of these libraries will be vital to the success of your personal projects.
- Machine Learning (ML): You will need to learn the theory and application of ML (machine learning) algorithms at first and then apply the concepts that you have learned to real-world data.
- Production Systems: after you learn data science and are job-ready, you will have to take on real-world data and convert it into action. To handle this task successfully and comfortably, you will need to be trained in ways of using a business’ computational resources to acquire, convert, and process data.
While undergraduate and master’s courses in colleges and universities often taken 2–3 years to teach you all the above, many say you can learn them in about 6 months by dedicating around 6–7 hours every day. If you already know the fundamentals, you may even opt for bootcamps that will get you job-ready within just a few weeks.