Data Science

Description

Data science is the exciting field that use statistics, computer science and business domain knowledge to uncover actionable insights into a specific business.  Although it has been around for many years, it has gained prominence due to increased computational power and software tools, including artificial intelligence and machine learning.  It can be applied to any industry or company with a large data set.  

The basic process includes the steps below.  The typical role for each step is shown in brackets.

1. Pull and aggregate data.  [Data Engineer]

2. Exploratory data analysis (EDA).  Probe the data to get a sense of what could be learned from it.  [Data Analyst]

3. Pre-process the data for your specific task by refining and transforming it.  [Data Scientist and Machine Learning Engineer]

4. Build models to do the predictive analysis.  [Data Scientist and Machine Learning Engineer]

5. Present the results in a useful way such that non-data scientists can understand it.  This is best done by creating a tool that can be used by all types of internal users.  [Data Analyst - same as step 2 above]

Something Cool

Data scientists help to predict the future for a business.

Types of Companies

• Consulting: These are the only pure data science companies from the perspective that they are agnostic to a specific industry or company.  They perform data science for a fee to help support a business.

• Any Company with Data: Yes, you are reading this correctly.  Any company with a large amount of data could have a need for data science.  They bigger the company and amount of data, the more likely the company is to have a data science team to gain specific predictive insights to the company and its industry.

Further Education & Credentials Required

• A college degree in data science, statistics or computer science.  A master's degree could be helpful but is not necessary.  Not all colleges and universities has a degree in data science.  In this case, you could focus on some combination of statistics, computer science, linear algebra and business analytics.

• Python is the most common computer software used in data science so it is important to become familiar with it.  More about Python: link.

• Side note: those with a degree in computer science typically go into software engineering as opposed to data science.

Entry Level Roles

• Data Analyst (Heads Down / Analytical): This role has specific business knowledge combined with data science skills to combine the two to gain insights into the business.  Alternative titles might be Business Data Analyst or Predictive Data Analyst.

• Data Scientist (Heads Down / Engineer): This role will be more technical than the Data Analyst.

• Machine Learning Engineer (Heads Down / Engineer): This role is more technical than the Data Scientist.  It required deep knowledge of statistics, linear algebra and software.

How To Learn More

• Talk to someone doing the role you want.  Check out my writings on The Gift of Asking for Help, Mentors and Networking 101 on The Search page to learn how to do this.

• Search YouTube using the term "data science project [industry]".  Example: Sports.  There are many videos that explain the process.  

  • Better understand your own skills and where you will be successful at Roles.

  • Check out my writings on various industries and departments at the Industries page.

  • Already know what you want to do but are looking for guidance on how to find a job within that industry?  Check out The Search for advice.