6 Technical & Soft Skills for Every Data Scientist

A data scientist should possess a diverse range of both technical and soft skills. Although the relevance of certain skills might vary based on the specific field a data scientist operates in, most skills remain beneficial irrespective of the position. Acquiring proficiency in these diverse skills can aid in pursuing a career as a data scientist or enhancing one's array of experiences.

The following are some of the more common technical and soft skills for a data scientist to have:

Technical Skills:

  • Data Visualization
  • Programming/Software
  • Statistics/Mathematics

Soft Skills:

  • Communication
  • Business Sense
  • Problem-Solving with Data

Data Science Technical Skills

A data scientist needs a bunch of skills, but their technical know-how sets them apart. They have to get comfy with lots of technical skills and special tools. Different companies use different tools and languages, but all data scientists need a core set of tech skills that work for many problems. These core skills are crucial for every data scientist.

Data scientists use programming to do cool stuff like machine learning, AI, and data mining. They should get the hang of the math and stats behind these tricks to know when to use them. Besides the basics, data scientists should know the popular programming languages and tools used for these jobs. They also need to understand software engineering to put these languages and tools together.

1. Data Visualization

Knowing how to show data visually is a big deal for a data scientist. People understand patterns better when they see them. Data visualization does two key things: helps data scientists spot patterns in data and lets them tell a story with data. Both these things are super important in data science.

Stuff like scatter plots and histograms are vital for exploring data. Without visualizing data, it's hard to know where to start. And making sense of data only matters if you can share that sense with others. To do that, you need to present data in nice and useful pictures. The skill of telling stories with data needs data scientists to use data visualization creatively to explain their ideas. Without these tools, data science might not work well in bringing about change.

There are many tools for data visualization: most programming languages offer ways to show data. For example, Python uses Matplotlib and pandas. JavaScript has the D3.js tool. R has ggplot2 and more. Tableau is a platform for making data look good from lots of different places.

2. Programming/Software

Data scientists use loads of programming languages and software to handle, clean, analyze, and show data in smart ways. Even though new tools keep popping up, a few are always useful in the ever-changing world of data science. Here are six important tools that new data scientists should learn to get good at programming and software:
  • R: R was mostly for academics, but now big companies like social networks, banks, and media firms use it for stats, showing data, and predicting things. It's free and has been around for a long time, so there are lots of extra things you can add to it (called CRAN) for different data jobs.
  • Python: Python wasn't made for data stuff at first. But now it has the pandas library that helps store and work with data quickly. Big shots like Bank of America and Facebook use Python for data science. Python is friendly, fast, and easy to learn because it's been around for a while for general coding.
  • Tableau: Tableau from Seattle helps out data science tools like R and Python. It might not be great for cleaning data or making big changes, but it's awesome for playing with and showing data in cool ways. Tableau makes it easy to look at data with fun and interactive screens.
  • Hadoop: Hadoop is a free software system that splits up big data for lots of computers to work on together. It's good because it's flexible, powerful, and won't mess up if a computer breaks. It's run by the Apache Software Foundation and has tools like the Hadoop Distributed File System and a way to use the MapReduce way of programming.
  • SQL: SQL is a language for managing data in databases. There are different kinds, like MySQL, SQLite, and PostgreSQL. You can do a lot of the same things with SQL that you can do with R, Python, or even Excel. But writing your own SQL code can be quicker and make scripts you can use again.
  • Apache Spark: Like Hadoop, Spark splits up big data among many computers. But Spark is faster because it keeps data in the computer's memory. It's better than Hadoop’s MapReduce way, but it still needs Hadoop's File System.

3. Statistics/Mathematics

Computers do most of the hard math in data science these days, but a data scientist still needs to know which math test to use and what the results mean. They should know some calculus and algebra, which are the basis for lots of data tricks. Knowing stats helps them see what a method can do, what it can't, and what it assumes. A data scientist needs to know what things have to be true for a test to work.

Data scientists don’t just use fancy stuff like neural networks to learn things. Even basic things like drawing a line on a chart and understanding it are important first steps in data science.
Math things like logs and exponents come up a lot in real-world data. Knowing the basics and more advanced stats helps data scientists understand data better.

Though computers do most of the math work, understanding how they do it is still important. Data scientists have to know what to ask computers and how to get answers. Computer stuff is a lot like math, so knowing math helps data scientists write better and accurate code.

Data Scientist Soft Skills

Data science needs a mix of skills. It's a mix of science, math, computer science, business, and talking. Data scientists need different skills to work with numbers and to change decisions with data.

Because data scientists work with data to change how people make choices, they need to explain numbers to people who don't get the tech stuff. They have to make data stories that people understand and find interesting.

You can look at data scientist skills in different ways. Mitchell Sanders wrote a blog about it on Data Science Central. Looking at this can help you understand what it takes to be a data scientist. But Dave Holtz on the Udacity blog says that the job of a “data scientist” can be very different, asking for different skills. Holtz's blog breaks down four types of data scientist jobs and which skills are most important for each.

1. Communication

One big skill for data scientists is talking to people. To be good at data science, people need to understand the data. Data scientists help turn hard data into things people get. Even though cleaning, working on, and understanding data is a big deal in data science, this work is useless without talking about it in an easy way.

Talking well needs a few things. It starts with showing data in a good way. People understand data better when it looks good. This is key for both looking at data and explaining it to others.

2. Business Sense

Data science can be used in lots of fields. Each field has its own goals, data, and rules. To do a good job, data scientists should understand the business they're working in.

Knowing the business is key for effective data science. Data scientists need to know the field they're working in before they can understand data. Though some things, like profit and goals, are the same in all fields, lots of important stuff is different. This special data makes up the industry’s business smarts, which helps know where a business is at and how it got there.

Each field’s goals, needs, and rules decide what a data scientist does. Without knowing how an industry works, it’s hard to find good info or suggest helpful stuff.

A data scientist is best when they really get the business they're helping. Even though data can show things, it doesn't show everything. Data scientists with the same job title can have very different tasks depending on the field they're in. To be good, a data scientist should know the industry they're in.

3. Problem-Solving with Data

Using data to solve problems is key in data science. By using a smart way to find and explain problems, decisions can be easier. In data science, there are tons of ways to look at data. Knowing how to pick the best one is a big job for data scientists. Data science helps find problems and uses data to fix them.

A data scientist knows how to fix problems smartly. They look at what matters, ask the right questions, and pick the best ways to fix things, getting help from other people at the right times. They also pick the right data science tricks to solve the problem.

A data scientist’s job is to use raw data and make sense of it. This needs more than just understanding stats and machines. They also need to know about the problem they're working on, the info they have, and what they want to do.

Data science isn't easy. There are lots of ways to look at stuff, and it's easy to get stuck. A smart way to solve data problems helps data scientists keep track of what they're doing. Things like Six Sigma are good tools to help data scientists and teams fix real data problems.


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