Must-Have Data Science Skills - Part1

Data Science Skill #1: Fundamentals of Data Science

As a newcomer in data science, I did what everyone around me did – started applying machine learning techniques like linear regression and SVM without even understanding the basics. I believe it’s all a fault of the generic “Build your machine learning model in 5 Lines of code” but this is miles away from reality.

The first and foremost important skill you require is to understand the fundamentals of data science, machine learning, and artificial intelligence as a whole. Understand topics like –

  1. Difference between machine learning and deep learning
  2. Difference between data science, business analytics, and data engineering
  3. Common tools and terminologies
  4. What is supervised and Unsupervised Learning
  5. Classification vs regression problems

Data Science Skill #2: Statistics and Probability

Statistics is the grammar of data science.

When you start learning writing sentences, you must be familiar with grammar to build the right sentences similarly statistics is an essential concept before you can produce high-quality models. Machine Learning starts out as statistics and then advances. Even the concept of linear regression is an age-old statistical analysis concept.

The knowledge of the concept of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Then come the various probability distributions, sample and population, CLT, skewness and kurtosis, inferential statistics – hypothesis testing, confidence intervals, and so on.

Statistics is a MUST concept to become a data scientist.

Data Science Skill #3: Programming knowledge

Machine Learning has seen a great jump only because of the boost in computing power. Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with it.

First of all, choose the programming language of your choice. Python, R, or Julia are to name a few and each has its own set of Pros and Cons. Python is a general-purpose programming language having multiple data science libraries along with rapid prototyping whereas R is a language for statistical analysis and visualization. Julia offers the best of both worlds and is faster.