As a Data Scientist, You should be familiar with multivariate calculus and linear algebra.
Knowledge level- Advanced
Resources to learn Mathematics
- Mathematics for Data Science Specialization
- Data Science Math Skills
- Introduction to Calculus
- Khan Academy
You should have Statistics knowledge that includes statistical tests, distributions, and maximum likelihood estimators.
Knowledge level- Advanced
Resources to Learn Statistics
- Statistics with R Specialization
- Statistics with Python Specialization
- Data Science: Statistics and Machine Learning Specialization
Programming knowledge is a must-have skill for a Data Scientist. As a beginner, start with Python. Once you are comfortable with Python, then learn R programming.
Knowledge level- Advanced
Resources to learn Programming
- Python for Everybody Specialization
- Python for Absolute Beginners!
- One Month Python
- Python 3 Programming Specialization
- R Programming
- Data Science: Foundations using R Specialization
You should have knowledge of how to store and manage your data in a database. That’s why you should have an understanding of SQL.
Knowledge level- Basic
Resources to learn SQL
You shouldn’t need to know how machine learning algorithms work. But You should have basic knowledge of Machine Learning Algorithms.
These are some important algorithms of ML, you can learn- principal component analysis, neural networks, support vector machines, decision tree, logistic regression, and k-means clustering.
Knowledge level- Basic
Resources to learn Machine Learning
As a Data Scientist, you have to showcase your findings in a visual form, so that stakeholders can understand it properly. That’s why the knowledge of Data Visualization is important. And for that, you should be familiar with data visualization tools like ggplot, matplotlib, Seaborn, and D3.js.
You should have knowledge of various Reporting tools like Tableau and power bi.
Knowledge level- Advanced
Resources to learn Data Visualization
- Data Visualization with Python
- Data Visualization with Tableau Specialization
- Tableau 2020 Certified Associate Exam Guide A-Z (w Datasets)
You should have a good understanding of Big Data concepts like Hadoop, Hive, PIG, Spark, etc.
Knowledge level- Basic to some Advanced level
Resources to learn Big Data
Now you have enough data science skills. Now it’s time to check your ability. Join Driven Data Competitions and Kaggle and challenge yourself.
Grow your data science skills by doing some Projects. It’s time to start working on some Real-World projects. Projects are most important in order to get a job as a Data Scientist.
The more projects you will do, the more in-depth understanding of Data you will grasp. Projects will also provide more privilege to your Resume.
Join data science communities, and stay up to date with the latest news related to data science.
The next step is build Strong Resume. Your Resume is the first impression for any recruiters. No matter how skilled you are, but if your resume is not attractive, sorry you will not get an interview call. That’s why you shouldn’t ignore your Resume.
If you want that your resume will get more privilege than others, then you should keep these things in mind:
- The template of your resume should be classic.
- Avoid templates with so many graphics. It gives a bad impression to the recruiter.
- Don’t hesitate about white spaces. That means don’t try to fill the full page with text.
- Leave some white space that looks clean.
- Don’t write a long text like a story. It should be precise and simple.
- Mention only the most important Data Science Projects. Don’t mention very basic projects.
- After finalizing your resume, you need to check for grammar and spelling mistakes. You can check it on Grammarly.
Now you are ready to apply for Data Science Jobs ✔