So you want to be a data scientist. It’s one of the highest-paying jobs on the market and has been dubbed by the Harvard Business Review as the “sexiest job of the 21st century”.

You know that data science involves mathematics, programming, and statistics. You also understand that it helps businesses make smarter decisions by deploying more complex models than either data analytics or business intelligence.

But you may have found a problem when preparing for job interviews: there’s no single activity called data science!

Some companies view data scientists as high-powered data analysts, while others look for people with strong ETL and data engineering skills. So how do you prepare for a role with huge differences in job responsibilities or skills?

Despite its broadness, there are several defining characteristics of data science. Like business intelligence or data analytics, it’s a role that uses data to help businesses make smarter decisions. However, data science has clear advantages to both you and the organization you are working for when compared to its counterparts:

• Data scientists get paid more. The average base salary for data scientists is almost 50% higher than for data analysts.
• Modeling techniques are more complex. Data science requires more profound algorithmic & statistical knowledge than data analytics.
• Data scientists decide what new data to search for. Sometimes, they are even required to mine it themselves. In general, data analytics only uses existing data from their database.
• Data scientists tend to have a more predictive approach, for which they leverage machine-learning techniques. Data analytics and business intelligence tend to only find patterns and trends within past data.
• Data scientists can be challenged to prove causality - trying to find deeper relationships between variables rather than simply establishing statistical correlation.

What This Course is About:

This course serves as an introduction to the Data Science Learning Path - which has all the content you need to ace the data science interview.

Preparing for a data science job interview presents two challenges: one is the complexity of the topics and questions ahead, and the other one is the diversity and broadness of topics. There’s a lot within the scope of what they could ask you in an interview.

This introductory course will help you overcome these challenges by showing you:

• An overview of what different data science interview questions look like.
• A few recommendations on how to prepare for a data science interview.
• Tips on how to make the most out of this data science learning path when approaching data science.