7 Must-Have Skills for a Data Scientist

Data science has evolved over the years to become the most in-demand and promising career path.

Data scientists are present in the majority, if not all, organizations. Before we dive deeper, here are a few facts you need to understand about data science;

  • The term “data science” came into the limelight in 2008 after organizations realized they needed skilled professionals to analyze vast amounts of data.
  • Demand for data scientist increased by 28% in 2020
  • There are approximately 4,524 job openings for data scientists.
  • Data science ranked as the best job in America for years 2016, 2017, and 2018.

Data is expansive, and it’s everywhere. In the field of data, you’ll encounter terms such as cleaning, interpreting, mining, and analyzing data. Although often used interchangeably, they mean different sets of skills.

The applications in the data science field are endless.

There is a considerable shortage of skilled professionals in the data science field. Although there is a growth of jobs, it’s still scarce to handle massive data.

Must-Have Skills for a Data Scientist
Photo by Kevin Ku from Pexels

This article mentions seven skills and resources you need to become a successful data scientist.

1. Understand Basic Fundamentals

As a newcomer in the data science field, you may get tempted to do what everyone around you is doing. Don’t let enthusiasm override you. Start small and keep learning.

Don’t apply machine learning techniques such as SVM and linear regression without the requisite understanding. This mistake will make you depressed, frustrated, and in the blink of giving up.

The first task is to understand the fundamentals of data science. First, differentiate between deep learning and machine learning. Know the common terminologies and tools and the difference between regression and classification problems.

Can you define terms such as business analytics, data science, and data engineering?

Simple basics build a strong foundation.

2. Programming

Machine learning is a fundamental principle when it comes to data science. With programming knowledge, you’re able to communicate with machines. Surprisingly, you don’t need to be perfect in programming. All you need is to be comfortable with it.

Be sure to pick a programming language of your choice- R, Julia, or Python are some of the languages. R is the language of visualization and statistical analysis, while Pythion is a multipurpose language in programming.

On the other hand, Julia is faster to learn and the best in both worlds.

But generally, Python is the most preferred since it’s easy to learn tasks.

3. Probability and Statistics

If you want to learn about becoming a data scientist, you must have basics in statistics. The concept of descriptive statistics like mode, mean, variance, standard, and median is a must.

Understand various probability sample, population, distribution, kurtosis, and skewness. Probability and statistics are a must for a successful career in data science.

4. Data Visualization

Data visualization is an exciting part of machine learning. There is no one-size-fits-all approach in this field. Ensure you’re familiar with plots like Bar charts, pie charts, and Histograms. You can then later move to advanced charts like thermometer charts and waterfall charts.

5. Data Analysis and Manipulation

Data analysis and manipulation are what separates a great machine learning from the rest. These are two different steps, but they have the same sequence.

Wrangling or data manipulation is where you clean data and transform it into a format that can be easily analyzed. Data manipulation is time-consuming, but it will help you make better data-driven decisions.

Data wrangling is useful in correcting data types, transformation, missing value imputation, and scaling.

On the other hand, data analysis where you learn almost everything about data. You’re able to analyze how various markets operate. Data analysis takes place in SQL, Excel, and Pandas in Python.

6. Big Data

The rise of social media networks, the internet, and IoT has led to a drastic increase in generated data. There are 2.5 quintillions of data generated per day; that is beyond expectation in the data world.  There is speculation that the amount of data volume will double by 2024.

Organizations are getting overwhelmed with a massive amount of data. To solve the issues, the companies adopt Big Data Technology to ensure data is stored efficiently and adequately.

7. Software Engineering

Fundamental software engineering skills like data types, time-space complexity, and software development projects will enable you with high-quality codes. Writing good, clean, and efficient code helps you collaborate with other team members.

The Bottom Line

Choosing a data science career is a viable decision. You need to have the above skills for better understanding and better progress in this field.