Top 8 Skills for Every Data Scientist

Top 8 Skills for Every Data Scientist

Top 8 Skills for Every Data Scientist: Programming Skills, Statistics, Machine Learning, Multivariable Calculus & Linear Algebra, Data Wrangling, Data Visualization & Communication, Software Engineering, Data Intuition.

As I attend university talks, the most common question I get asked is “What skills do I need to have?”

Answering the question “what skills do I need to have” can come with various answers depending on who you are talking to, what company you are looking at, and even what job position you are applying to. After many talks with college students on this topic, I wanted to sit down and discuss how I look at this question and the top 8 areas to consider when looking at skills.

  1. Developing a Business Problem
  2. Working with Big and Small Data Ingestion and Processing
  3. Understanding Data Cleaning and Preprocessing
  4. Working with Tools
  5. Drafting Visualizations, Reports, and Dashboards
  6. Understanding the Analytics Life Cycle
  7. Analytics to Production
  8. Embracing Research and Development (R&D)

1. Developing a Business Problem

As you look at what skills you need to work in data science, one key area is learning to develop or understand the business problem you are trying to solve. It is essential to understand the business justification for the work you will be doing and how the customer will utilize it. Often, we can get caught up in a cool idea, but we miss the business aspect. If no customer is asking for the work, no reason to run the analyses, then what are you doing? Understand how this work will be used and provide value to the customer. Developing skills in this area to understand business justification and value-add can help you continue to build data science projects and present your work to others.

2. Working with Big and Small Data Ingestion and Processing

As you look for a job in data, you will need to know how to ingest and process the data you are working on. The skill sets in this area may vary. Suppose you are looking more towards data engineering. In that case, you may find yourself developing databases, creating relationships between the data sources, and making data marts for people to come to get the data from you. Your skills need to be in how to create and maintain those data sources. If this is the case for you, focus on knowing different database types, how to use those databases, and how to create relationships within the data.

If you are looking to find a data science or data analyst job, you may be more focused on how to bring that data into your workspace. Do you need to connect to a database or use an API? Are you developing code that will interact with this data or software tools like Power BI and Tableau? Your skills in this area may vary depending on the type of role you are applying to. Still, it is good to have at least a basic understanding of how to interact with different data sources and ingest that data into your tools or environments. Knowledge of how this data gets ingested before you start your analyses is essential.

3. Understanding Data Cleaning and Preprocessing

Data cleaning and processing will also vary between jobs. In the first case of being a data engineer and creating the data sources, your cleaning and preprocessing may be more generic to the overall ingestion process. You may want to remove duplicate entries, clean up how the data sources are interconnected, and create a usable database that others can work with. In this position, you are not as focused on intense data cleaning techniques.

Now, what I mean by that is, if you are a scientist or analyst looking at the data, you may ingest a data engineers dataset and still need to clean and preprocess the data to work with what you are doing. You need skills in one-hot encoding, cleaning and handle text data, imputation, and making sure the data columns are the expected data types for what you are doing. Understanding different ways data cleaning and preprocessing happens and implementing them depending on your end use-case are valuable skills that you will often need as you work with the data. You should understand the main concepts of data cleaning and preprocessing relative to the job you are looking for.

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Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.