Data Science is a hot topic these days! It’s a very lucrative field with lots of research and innovation. It also doesn’t hurt that Data Scientists are paid very well! So, it’s no shock that switching your career from IT to Data Science can improve your prospects a lot. This field will teach you cutting edge technologies that will help you in reskilling yourself and provide an edge in this competitive job market. And it doesn’t hurt that Data Science is also a fascinating subject with many branches like data visualization, image recognition, machine learning, etc.
So this article tells you all about switching your career from IT to Data Science. It teaches you the various skills essential in Data Science and also shows you the methods by which you can acquire them. You will also get to know the networking you need to do before you can get a job in Data Science and become a full-fledged Data Scientist!
Learn Data Science Skills
While switching your career to Data Science, it’s important that you learn the basic skills. These allow you to create a roadmap you can follow, especially if you are currently in another job and want to switch. After learning these basics, you can continue as you think best and acquire more in-depth knowledge. So, here are some basic skills in Data Science.
1. Statistical Skills
Statistical skills are the bread and butter of a data scientist. So you should be familiar with at least the basics of Statistical Analysis including statistical tests, distributions, linear regression, probability theory, maximum likelihood estimators, etc. You should also understand which statistical techniques are a valid approach for a given data problem and which ones aren’t. Some of the analytical tools that are popular for this are SAS, Hadoop, Spark, Hive, Pig, etc.
Python and R are the most popular programming languages for Data Science. Python is used because of its capacity for statistical analysis and its easy readability. Python also has rich libraries and various packages for machine learning, data visualization, data analysis, etc. that make it suited for data science. R is also another popular programming language for Data Science. It makes problem-solving very easy with the help of packages like Ggplot2, Esquisse, etc.
It is important to learn the basic Machine Learning algorithms like Linear Regression, Logistic Regression, K-means Clustering, Decision Tree, K Nearest Neighbor, etc. Most of the Machine Learning algorithms can be implemented using R or Python libraries so you don’t need to be an expert on them. What you need expertise on is the ability to understand which algorithm is required based on the type of data you have and the task you are trying to automate.
4. Cloud Services
It’s also an extra edge for you if you have some understanding of Cloud Services since most companies are also moving big data and analytics applications on the cloud. So it’s important if you can understand these cloud services as a Data Scientist a little more deeply so that you can perform data analytics effectively. This knowledge about deploying your models and code to the cloud will make you stand apart from the crowd as most companies are moving towards moving their databases to the cloud.
Deep Learning is a subset of Machine Learning that is normally used for more complex applications like Image Recognition, Natural Language Processing, etc. Hence, it is not necessary to know for the routine and basic Data Science applications that involve structured or tabular data. But now, Image Recognition, Natural Language Processing, etc. are becoming more and more popular even in normal Machine Learning applications which means you should know at least the basics of Deep Learning.
SQL is a fundamental aspect of Data Science that you should be deeply familiar with. You should have the ability to write and execute complex queries in SQL that will help in carrying out analytical functions and changing the database as required. SQL is also essential in providing deep insights into a database depending on your query. So learning SQL will help you in understanding relational databases and add another step to your journey in becoming a Data Scientist.
7. Communication Skills
This is a common skill that is also very helpful in becoming a Data Scientist. That’s because you need to be able to translate your data findings into quantified insights for a non-technical team to aide in the decision-making. If you are not good at communication, you won’t be able to do your job effectively even if you understand the data better than anyone else. This also involves data storytelling wherein you should be able to present your data in a storytelling format with concrete results and values so that other people can understand what you are saying.
Ways to Learn Data Science In Depth
Now that you know enough about the Data Science skills, you can focus on the different mediums you can use in learning them in depth. Since you are switching your career, there are multiple ways of getting the necessary knowledge to apply for Data Science jobs. Let’s see these now.
1. University Education
If you want to be completely and formally prepared for a career in Data Science, then a University Education is the way to go. An education from a top university will be very helpful in providing you a platform to apply for Data Science jobs, especially since you are switching your career. So a degree will provide some credibility that you know Data Science and are industry-ready. However, one drawback of getting a University Education is that it is insanely expensive. Chances are that you already went to university for your current career and the cost of a fresh university education could finish your savings or even put you into debt. So only opt for a University Education if you can afford it and you think you need more formal support to switch your career.
2. Online Courses
In case you don’t want to go to university again, you can always opt for Data Science online courses. These allow you to learn Data Science on your schedule along with your current job and then switch when you are ready. Another advantage is that these courses are very cheap compared to formal degrees and can be completed in a self-paced environment. However, you need to be extremely focused and dedicated to successfully learn Data Science from online courses. Some of the popular courses include:
- Data Science Specialization by John Hopkins University (Coursera)
- Applied Data Science with Python Specialization by University of Michigan (Coursera)
- Applied Data Science Professional Certificate by IBM (Coursera), etc.
Practice Data Science Skills by Working on Projects
Projects are a great way to demonstrate your skills in Data Science. They adequately demonstrate your talents even though you are from a completely different background and just switching to Data Science. And another plus point is that projects are very interesting as they provide the opportunity to analyze a data set to find the correlations between the data and obtain unique insights. There are many dataset sources where you can download and use data sets for free. These include Kaggle, Data.gov, Google Cloud Public Datasets, Global Health Observatory, etc. Some of the popular projects that you can try on Kaggle if you are just a beginner include the Titanic Survival Project, the Personality Prediction Project, Loan Prediction Project, etc.
Apply for Internships and Jobs in Data Science by Networking
After you are ready to work professionally in Data Science, it’s best to start applying for jobs and internships. LinkedIn is your best friend here! It’s a great way to build your professional network and gain more connections. Recruiters also check out your LinkedIn profile as it serves as a digital resume highlighting your skills, experiences, and education. You might even miss out on some internship or job opportunities if you don’t have a LinkedIn account or if it’s not regularly updated. And if you have a professional network on LinkedIn, you might even get some internship opportunities or direct job offers there! Some of the other online portals where you can apply for Data Science jobs include GeeksforGeeks, ai-jobs.net, Amazon jobs, Analytics jobs, Analytics Vidhya, etc.
After that, it’s just a matter of acing the job interviews to get started in your new career as a Data Scientist. One of the important things to remember is that the most important thing for a Data Scientist is the ability to think critically and analyze the questions in a structured manner. This is what you will be tested for in your interview rather than knowledge of any particular language or technology as those can be learned, and they may even change with time.
So after learning the basic skills required in Data Science like Statistics, Programming, Machine Learning, Cloud, Deep Learning, SQL, and Communication skills – you need to decide whether you want to enroll in a formal college degree or self-study Data Science using online resources. After that, you can apply for jobs and internships until you get your first job offer and move further in your goal of becoming a Data Scientist. All the best!