Data Science Skills Everyone Need

There are no denying facts that Data Science has been one of the fastest-growing fields and created numerous jobs across the globe. Data Science is a broad field that includes several sub-tasks such as data visualization, data representation and transformation, predictive analysis, data analysis, and much more. Today, it is common for people to take up courses like a PG in Data Science and step into this promising career field.

The application in this field is endless, and the entire world would be revolving around Data Science in the coming years, from product recommendation and sales prediction to all the way up to personal assistants and self-driven cars; Data Science drives everything. No wonder why every organization craves skillful Data Scientists and Data Analysts. But what skills does one need to become an expert in the Data Science world?

Now, we will discuss the ten mandatory skills that are necessary for an individual to become a Data Scientist or Data Analyst. These skills could be categorized into technical skills and soft skills.

Technical skills covers – Machine Learning, Maths, Statistics, Coding, Data Visualization, Data Wrangling, and Big Data tools. On the other hand, soft skills cover – communication skills, learning ability, team player, and ethical skills.

Essential Data Science Skills

  1. Basics of Data Science
  2. Machine Learning skills
  3. Programming knowledge
  4. SQL
  5. Math and Statistics
  6. Data visualization
  7. Deep Learning
  8. Big Data
  9. Communication Skills
  10. Problem Solving

1. Basics of Data Science

The first step and the most important skill you require is understanding the fundamentals of Data Science, Machine Learning, and Artificial Intelligence. You need to understand the following:

2. Machine Learning Skills

Machine Learning is an important part of Data Science, and for a Data Scientist, Machine Learning is a crucial and core skill to have. Therefore, it is important to have an in-depth knowledge of Machine Learning frameworks, predictive analysis, model building, testing, and evaluation. The best way to learn, and master Machine Learning is by practicing problem statements.

Machine Learning uses two types of techniques; supervised and unsupervised learning. Supervised learning trains a model on known input and output data so that it can predict future outputs, and unsupervised learning finds patterns or structures in the input data.

3.Programming Knowledge

Programming simply provides us a way to communicate with machines, and programming skills are quite essential in Data Science. Although you don’t need to learn and master all the programming languages, since Python and R are considered two of the most preferred languages in Data Science, you should choose either as companies require skills in either R or Python.

4. SQL

Regardless of which programming language you learn and choose further, you need to master SQL. Structured Query Language is an old programming language that is used to request and filter information from the database. Although some can find it boring when compared to modern programming languages. However, SQL is still an essential skill for all Data Science enthusiasts because most companies are using SQL to store the data in some form of SQL-based database.

5. Mathematical and Statistical Skills

Mathematical skills like calculus and algebra are the two most important skills in Machine Learning. Algebra is used in data preprocessing, data transformation, and data evaluation. Calculus is used to build the Machine Learning model.

Statistics such as mean, median, mode, and standard deviation are used to visualize data imputation, data preprocessing, model evaluation, etc.

6. Data Visualization

Data visualization is the graphical representation of data and information. It is more like an art and one of the most fun parts of Machine Learning.

As a data visualization expert, you must be familiar with pie charts, bar charts, histograms, waterfall, thermometer charts, etc. The bivariate and univariate analyses become much easier to understand by using a colorful chart.

Every language offers a great set of libraries for advanced charts, and these plots come in handy during the several stages of data analysis.

7. Deep Learning

Deep learning is a high-growth vertical in the field of Artificial Intelligence due to several improvements in computational advancement and data storage capabilities.

It requires several skill-sets that would enable you to excel in this field. Indeed, it would be best if you were well-versed in programming languages, especially Python, and had a good understanding of linear algebra and maths.

8. Big Data

Due to the rise of the internet, social media, and IoT, there has been a great shift in the data we use to generate daily. As a result, there has been a sudden boost in the rate of data we are generating, and this data is high in volume, velocity, and veracity, which forms the 3V’s of Big Data.

It is challenging for organizations to manage and handle a huge amount of data with traditional methods, and Big Data technology helps to store and analyze data more efficiently.

Big Data tools/frameworks such as Hadoop, Spark, Hive, Apache Storm, and Cassandra are being used by most engineers and scientists. Hence, you need to master these tools to stand out.

9. Communication Skills

When people talk about Data Science skills, soft skills and communication are mostly sidelined, but this is one of the most important skills for data enthusiasts. All the analysis done by the Data Scientists or Data Analysts is only useful when you are able to make people understand, convince, and act on it.

So, communication is the key.

10. Problem Solving

One of the fundamental skills to become a Data Scientist and Data Analyst is to have a hook for solving real-world problems. In addition, data enthusiasts need to approach a problem to understand the risk involved with specific business models.

As a Data Analyst, you might get the problems to solve, while as a Data Scientist, you need to find insights on your own. Curiosity, problem-solving, critical and structured thinking are the key.

In this article, we discussed ten important Data Science skills that everyone needs. Data Science is evolving, and it’s not wrong to say that it’s the future, and organizations are going to invest more in Data Science. However, learning never stops in any field. Today, you master the tool, and it gets run over by an advanced tool the next day. Therefore, a data scientist needs to be curious and always learning.

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