Skills For Data Science

Data Sciences are more and more talked about. Knowing how to use them means making sure you place yourself in the category of profiles most sought after by recruiters. Math and statistics skills aren’t the only skills you’ll need, you’ll need to learn to code in Python, read visualization tables, and use SQL. Don’t be afraid, it’s much simpler than it looks, we’ll give you the keys to making Data Science child’s play and identifying the skills to develop to become a Data Scientist.

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Top 7 skills to be a Data Scientist

1. Know how to code in R or Python

Knowing how to code in Python will bring great added value to your CV. This programming language has become very popular in Data Science since many developers in Silicon Valley diverted its primary function and used it in data analysis. Libraries like Numpy, Matplotlib, and Pandas have sprung up and have now become essential when using Python.

2. Statistics

Statistics are the fundamentals on which machine learning is built. To be a Data Scientist, you don’t need to have a master’s degree in statistics, but you need to know the basics. This involves knowing how to construct an average, a median, a standard deviation but also understanding how to construct a confidence interval and interpret a p-value.

3. Machine Learning

Machine Learning is the ability of an algorithm to use existing data to build prediction models without having to code each step of the calculation. For example, Machine Learning will make it possible to predict whether an individual will buy a product based on certain characteristics intrinsic to his behavior. Machine learning skills are learned naturally with learning Python (or R if you are new to the language). Being able to build this kind of model is essential for a Data Scientist.

4. Manage databases in SQL

The first challenge presented by Big Data is data management and analysis. This skill has become essential to become a Data Scientist. SQL will help you manage structured relational databases, which you will use in Frameworks like Hadoop or Spark, commonly used in Big Data.

5. Data Mining

Data Mining is the ability to explore different data sources and identify those that will provide the right information to solve your problem. In digital companies, a lot of this data comes from the web, which is why having a background in Web Analytics and knowing how to use tools like Google Analytics or Optimizely to do A / B testing is a plus. However, the web is not the only source of data available; companies can also use CRM databases like Salesforce. The key is to be able to understand this data and know how to extract it in order to be able to analyze it. Here is a detailed article to better understand how Google Analytics works, article recommended by the community.

6. Data Cleaning

This is one of the most daunting phases in a Data Scientist’s job, yet the most important. There is no point in analyzing data that is corrupted. Cleaning up data includes knowing how to deal with missing data and ensuring that all data is of the correct type, for example, that a number is considered a number and not text. Although it is possible to clean your data with Python, Excel is also a very good tool to manage this phase.

7. Data Visualization

Knowing how to analyze data is good, but then you have to be able to communicate the results to an audience. In Data Sciences, it is essential to make the figures speak in a visual way in order to make your work accessible to a wider audience. Tableau is the most popular tool in this field but there are others like Chartio or Periscope Data which, unlike Tableau, also include the possibility of using Python and SQL. Data Sciences evolve very quickly with Machine Learning, Big Data, and even Blockchain.