9 Must have skills to become a Data Scientist.

Soft skills are underrated but don’t neglect them.

Data science is tough to break in. This multi-disciplinary field is mostly based on maths, stats, and programming. To be a successful data scientist you must have these 10 skills which will take you to the next level as data scientist.

Here we go.

1. Statistics

When you start writing a sentence you must be clear about the grammar concepts to build your sentences in a similar way is an essential concept for you to understand to build your high-quality models. The main advantage of statistics is that the information is presented in an organized way which will help us a lot.

  • Types of Analytics
  • Probability
  • Variability
  • Probability distribution
  • Regression Modeling

2. Calculus

Most machine learning models are built with several unknown variables. Knowledge of is significant for building a machine learning model.

  • Limits
  • Differentiation
  • Integration
  • Gradient Descent
  • Multi-Variate calculus

3. Linear Algebra

It is necessary to understand to step up into machine learning. With Linear Algebra, you can develop a better intuition for machine learning algorithms. Learning Linear Algebra would help you to choose the necessary parameters and develop a better model.

  • Matrices and Vectors
  • Matrix Operations
  • Matrix Inverse
  • Orthogonal Matrix
  • Applications of Linear Algebra within Data Science (SVD and PCA)

4. Programming

gives us a way to communicate with our machines. So, you would have a question for yourself. Do you need to become the best in programming? The answer is

Firstly you should choose a programming language. or are the popular programming languages learned by data scientists, each language has its own set of pros and cons. is a general-purpose programming language, and having multiple libraries with rapid prototyping makes it useful for data scientists. is a statistical analysis and visualization language.

Usually, everyone starts with Python as their primary language, because Python is found to be a more accessible language to perform machine learning tasks.

5. Data Manipulation and Analysis

which is also known as is the skill where you clean your data and transform it into a format that can be useful for analyzing it. Data Manipulation takes a lot of time but it will help you in taking better data-driven decisions. Data manipulation is done in areas such as

is the step where you understand a lot about your data. Data Analysis can be done by using the library in Python.

6. Data Visualization

is considered the fun part of machine learning. To start with data visualization one must be familiar with and move to advanced plots like etc. These plots will be very useful during exploratory data analysis.

Data visualization is where you can relate your bi-variate and multi-variate variables with colors. Data Visualization can be done in etc.

7. Machine Learning

For every data scientist, is the core skill to have. For example, if you want to predict the number of customers you will have in the next month by looking at the past month’s data, you will need to use machine learning algorithms.

You can start learning Machine Learning with simple algorithms like and climb up to other models like etc. It is really easy to remember the line of code for your machine learning algorithm which hardly takes only 3–4 lines of code but the most important thing is to know how they work.

8. Communication Skills

is a . Communication Skill here refers to the skill with which you communicate with your fellow mates with data. Effective communication is necessary for quite a few reasons.

  • Concentrating only on the sum and substance of thoughts.
  • Focusing on Outcomes and Values
  • Use empathy
  • Speak the language of the Business

9. Story-Telling Skills

The art of is a very critical skill for every data scientist. . Every data scientist should also be a storyteller because it brings in simplicity. Storytelling makes our data interesting also stories provoke thought and bring out some useful insights into our data. This also helps in understanding the logic behind every data and analysis.

With time data analytics are growing bigger and better. It is expanding the number of people generating insights, increasing the need for more data storytellers in the future. Therefore, data scientists should not only stick to numbers and their analytical skills rather they should train themselves to become good storytellers with the use of their data.

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