So you want to become a data scientist? Is that because of the hype around this field lately? A lot of companies have realized the importance of data science in their business and are hiring professionals with job titles like Data Scientist, Data Analyst, Data Engineer, AI Engineer and Machine Learning Engineer. Is it because these are some of the top paying jobs in the world? These are valid reasons but once you get to know the power and applications of data science, I believe you’ll be more excited just to solve real-world problems. So, in this article, we’ll be looking at how to get your first data science job in 6 months.
If you’ve read this article on what data science is, you must be clear that pretty much anyone can get into data science. But then the question arises how and where to start? When you begin, it might seem there are just too many things to learn. But the good news is, you don’t need to learn everything to become a data scientist. Fundamentals, Statistics, Programming and Visualization are a must have. Machine Learning is also good to have, and the rest depends on which field you’d like to specialize in. We have an excellent course on Data Science With R & Python that covers these topics. An overview of this course is given below.
Course Roadmap
Week 1-2: Descriptive Stats
For the first week and the start of the second, you’ll be learning about descriptive statistics used for data exploration which is the first step in data analysis. You’ll learn about the central tendency, visualizing data, normal and sampling distribution.
Week 2-4: Inferential Stats
From week two, you’ll be learning about inferential statistics to draw conclusions from a sample of data about the population. You’ll learn about hypothesis testing, correlation, and regression up until week four.
Week 4-7: R
Starting from week four, we get into programming stuff. We begin with a popular language for data analysis R with a deep understanding of the various functionalities and features of R.
Week 8-11: Python
We start looking at another popular programming language in week 8 – Python. We go from the basics of Python to more advanced stuff by using popular Python packages for data science – NumPy, Pandas and Scikit-Learn.
Week 11-19: Machine Learning
Week 11 onwards we embark on a journey of Machine Learning. We start with understanding Supervised and Unsupervised Learning. We then look at techniques for supervised learning like Linear and Logistic Regression. We also look at unsupervised learning algorithms like KMeans Clustering and other important topics like Naive Bayes and Time Series Analysis. We spend much of our course here.
Week 19-20: Tableau
In the final weeks, we learn about a popular reporting tool Tableau and how to integrate Tableau with R/Python.
Week 21 and beyond
After you’ve gained all the knowledge, you’ll be getting your hands dirty with a real-world case study, which you can also add to your portfolio.
Applying your knowledge
Following the course will not only give you brilliant insights into the field from our expert tutors but also helps you keep track of your progress. It’s good that you have gained all these knowledge, but now you need to apply it as well if you’re serious about getting your first data science job. There are some extra steps you need to take. It is not necessary that these steps be taken after you’ve finished the course. In fact, I’d encourage you to take them as early as possible. This will help you gain more confidence as you learn. Below we list some of them:
Practice, practice and more practice
Practice is the key to everything and while you learn from our course, you also need to try things on your own. Find a subfield you’re interested in. That may be analytics, visualization, computer vision or anything else. Once you do, search for the relevant dataset from which you can practice. You can use Google’s latest dataset search engine for that purpose. Once you have used your knowledge on data science to solve any problem, no matter how big or small, make sure you push your code to a public repository like GitHub. This will allow you to incrementally create a portfolio that you can share to potential future employers.
Join data science groups/follow people on social media
The field of data science is constantly evolving and there’s always a new breakthrough going on. So, in order to remain up to date, I’d recommend you to follow groups as well as people related to the field on social media. Artificial Intelligence & Deep Learning is an excellent group on Facebook where people around the world share their research, articles, and problems. Members of the group range from beginners to experienced. They also share new job postings and paid projects every Saturday. You may also find local groups created by like-minded people on Facebook, Twitter, and LinkedIn.
Attend workshops and seminars
Following people/groups online is a great way to learn new things. Attending in-person workshops and seminars organized locally also serves the same purpose, but there’s more to that. In addition, other people also get to know you, your strengths and interests. It is a great way to grow your network and learn about new opportunities. You also get the feeling that you’re not alone in this. You can collaborate with other aspiring data scientists and work on projects of common interest. There’s a School of AI that frequently organize meet-ups. You can find about your city or nearby cities from here. Facebook events is also a good way to find about data science events happening near you.
Create an online/offline presence
Now that you’ve learned a few things on data science and know a handful of people who either work on or have a keen interest in the field, it is now important to create a brand for yourself. Start writing short articles/blogs on what you’ve learned. Don’t worry if you don’t know everything; don’t be afraid to express yourself. Share your articles on platforms such as LinkedIn, where by now you have a decent number of followers of common interest. Organize meet-ups yourself and take an initiative to talk about what you know in person. In this way, you market yourself and when some new opportunities come up, you definitely have an edge over fellow aspiring data scientists.
Apply for jobs
By now, you have a decent portfolio of work to showcase and you might already have one or more job offers. Maybe it’s not from a big company for now, but it’s definitely a step forward. Take it; it all adds up in your résumé. Also, don’t send out the same résumé to all potential employers. Be sure to highlight your skills that match the requirements in the job description. Because employers are seeking for individuals who can solve their problem and if you’ve got that skill, chances are you’ll be invited for an interview. When writing a cover letter, make sure to tell how you’re a good fit for the company. This will give the employers the impression that you’ve thoroughly read through the requirements and that you have an eye for detail.
Prepare for the interview
You should start preparing for an interview even when you’ve not been invited to one. Because then you’ll always be prepared for any and all opportunities that come your way. To be a data scientist, you need to have a strong command of mathematics, statistics, programming, and problem-solving techniques. Again, practice is the key here. Keep brushing up your skills and when you get called up for an interview, you can nail it.
Getting a data science job is not the end of the story, actually, it’s just the beginning. Because this field is constantly evolving, you need to keep learning and keep being updated. So the learning continues as long as you’re in the field. I’d encourage you to follow the rule that I live by – Learn, Share and Repeat.
