Updated March 21, 2023
Introduction to Data Scientist Skills
Data science is a buzzword for all the job hunters in the market. It has inspired many that the number of online platforms to teach data science outnumbered other computer skills. So what skills are needed to become an efficient data scientist? Is the knowledge of given data sufficient, or do I have to learn something new? I know a few statistics and excel; will that be okay to be a data scientist? I am perfect at programming languages and will be a great data scientist! So let’s check out what skills are essential for a data scientist.
Important Data Scientist Skills
Below are the essential skills for Data scientists:
I was very good at solving statistics and probability problems during my school days which I missed in my software world. The world of statistics is fantastic. Okay, at least for like-minded people and me. So what could bring me back to statistics other than Data Science? Believe me, folks, statistics are essential for analyzing this vast data pool. Statistics itself means the collection, interpretation, and analysis of data. This explains why statistics are necessary for this field. Prediction of future data is as important as the analysis of data. Knowledge of the basics of statistics and probability is essential for predicting the behavior of data.
I hated programming more than anything because learning C, C++, and others were complicated as I didn’t understand their logic. As a blessing, I came across the Python language created by Guido Van Rossum. It’s easy to enter print (‘Hello World!’) and get the output. In other languages, we have to write three lines to get ‘Hello World’ printed. All the built-in functions are easy to learn and understand. Data types like lists, tuples, dictionaries, and others are easy to grasp and remember. There is a saying that if we know python, there is no going back to other languages, as this is super easy. We have many libraries for data analysis and model building in python,n like Numpy, pandas, matplotlib, etc. All these libraries help in building a good model for the data. Jupyter notebook is good for doing data analysis problems.
Ross Ihaka and Robert Gentleman developed r. R has statistical, graphical, and machine-learning methods similar to python. However, the visual representation of R is better when compared to python. R’s data types include character, numeric, integer, complex, and logical. If python is so good, then why R? R is good for communication and programming as well. If you are new to programming, learning the R language is better. R is mainly used for data analysis, while python is considered the general-purpose programming language. Hence, it is beneficial to know both languages. Who knows, you may become a master in both! Also, both are free to download and use in Windows, macOS, and Linux.
When my boss asked me whether I knew Excel, I was like, who doesn’t know it? But seriously, guys, there is much more to learn in excel. Statistics and probability functions are built-in in excel; deep knowledge of excel is essential to make it easy to compute the data. Graphs can be drawn; what-if analysis can be done, pivot tables to extract data, and many more options in excel, which makes a different world. Isn’t it amazing to think that excel is still being used as an unavoidable tool in data science? Charts and formulae help to formulate data and to see data differently. This helps in the visualization of data. Excel can also be used as an optimization tool.
To get data from the database and to work with the data, SQL or Structured Query Language is very much needed. SQL is used to create a table without physically seeing it, reading data from it, or updating it. The most used commands are select, insert and update. SQL has a standard for its commands. We can call it exactly a Structured language for the database. SQL is case-insensitive, unlike python and R.
Excel is a program, while SQL is a database programming language. SQL Server is a database management system,m while excel is used for data analysis and calculation. Knowledge of both is equally important to become a skillful data scientist.
#4. Communication Skills
You are a master in python, and making the graphical interpretation after data analysis doesn’t make a data scientist unless you don’t know how to communicate the findings you have done in data. Communication between the team members you have worked with and the audience is significant. When data scientist interviews are done, the interviewer looks for good communication skills, which add up as a weight for the job. Creating stories from data is not an easy task. The audience can be from different areas: technical and non-technical people. Engaging everyone in a single presentation is tiring as well as enjoyable. A data scientist should be a good storyteller.
Creativity is important in data science. You may find it challenging to find an outcome from the data given even after applying all the analyses you know. Here it would be best if you used your creative thinking to predict what is possible and what is not. It can help in producing good results for your interpretation. A data scientist should always be curious to know what can happen with the data given. Also, data scientists should work with all the people in the company to see the flow of data. Data scientists can’t work alone. Linear Algebra, calculus,s and Numerical Analysis are important math topics for a data scientist. Mastering all these can make you a great data scientist. But update the knowledge base and be curious to learn something new always. It may be hard to remember everything if you start your career in data science. But hard work pays off, and you will love playing with data.
This has been a guide to Data Scientist Skills. Here we have discussed the introduction to data scientist skills and the essential types of data scientist skills. You can also go through our other suggested articles to learn more –