Do 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 9 months.
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 offer an excellent course on Data Science With R & Python that covers these topics.
Let’s look at the key skills that one Data Scientist has under their belt.
Descriptive Stats
The descriptive statistics used for data exploration is the first step in data analysis. Like central tendency, visualizing data, normal and sampling distribution.
Inferential Stats
Inferential statistics are used to draw conclusions from a sample of data about the population. Hypothesis testing, correlation, and regression fall under this.
R
One needs to get into programming stuff. Begin with a popular language for data analysis R. A deep understanding of the various functionalities and features of R is needed if you want to succeed.
Python
Start looking at another popular programming language Python. Begin from the basics of Python to more advanced stuff by using popular Python packages for data science – NumPy, Pandas and Scikit-Learn.
Machine Learning
Start with understanding Supervised and Unsupervised Learning. Then look at techniques for supervised learning like Linear and Logistic Regression with a look at unsupervised learning algorithms like KMeans Clustering and other important topics like Naive Bayes and Time Series Analysis.
Tableau
Learning a popular reporting tool is necessary so, go for Tableau and how to integrate Tableau with R/Python.
Project and Case Study
After you’ve gained all the knowledge, get your hands dirty with a real-world case study, which you can also add to your portfolio.
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 using Datathon. That may be analytics, visualization, computer vision, or anything else. Once you have used your knowledge of data science to solve any problem, no matter how big or small, make sure you push your code to a public repository like GitHub.
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 follow groups as well as people related to the field on social media. Members of the meetup 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 , , 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 globally also serves the same purpose, but there’s more to that. In addition, other people also get to know you, your strengths, and your 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. Join Data Science Foundation where you can collaborate with other aspiring data scientists and work on projects of common interest.
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 by attending Resume Preparation Classes and when you get called up for an interview, you can nail it.
So, best of luck with your upcoming journey in data science and keep attending our online and offline batches. And those who are looking forward to beginning this journey we are here to help you guys, just call us – 99033 76367