You may frequently ask yourself what are the necessary data science skills that help me to stand out? Just take look to see how much people are searching for “Data Science Skills.” Such great interest prompts us to ponder more the importance of Data Science and its necessary skills. Let’s investigate how much people search for that according to Google Trends:

In this post, you learn about the top-5 Data Science skills that are crucial to be a successful Data Scientist!

NOTE: Please check the disclaimer page about the recommended products.

1. Linear Algebra & Numerical Computation

Linear Algebra is one of the fundamental branches of mathematics that is of great importance in Machine LearningWithout Linear Algebra, Data Science will be crippled! Many Data Science practitioners simply try to ignore Linear Algebra for a variety of reasons BUT that would grab their tie later! Linear Algebra is what makes a lot of Machine Learning algorithms very powerfulTo know how algorithms work and to implement them, you need to know Linear Algebra. It’s simply the very first must known knowledge as one of the Data Science skills. Let’s summarize some areas that Linear Algebra comes to play in Data Science:

Everything in Data Science is about vectors and matrices. You need to know the definitions.
The concept of linear dependency.
Matrix Decomposition to its constituent elements.
Optimization theory including the implementation of loss functions.

There is always the questions of “how much Linear Algebra should I know?”, “how deep should I learn?”, and “how should I learn to implement it?” You get the answer to those question in the following book:

Linear Algebra for Data Science

There are many different useful resources that you can check out:


2. Probability & Statistics

Probability theory is the mathematical framework of statistical analytics that is necessary for analyzing data and therefore crucial for Data Science. According to Wikipedia, Data Science is:

the study of the collection, analysis, interpretation, presentation, and organization of data.

So I guess that definition is self-explanatory if someone asks “why do I need to learn Statistics for Data Science?” The fundamental concepts such as probability distributionconditional probabilityhypothesis testing, etc, require a working knowledge in Probability Theory. It is one of the most important Data Science skills.


Check the following course if you can desire to have all the important Probability Theory concepts in one place:

probability of data science

The are many useful resources regarding the probability theory. I just picked a couple of those that I believe you can benefit from them the most:


3. Coding: Data Science Skills in Practice

How do you want to do Data Science without coding skills? There are tons of programming languages, libraries, and frameworks for Data Science. Not only you need to know them, but also you should practice constantly because they change every day. Let’s divide them into subcategories.

  1. Programming languages: Python is the most influential programming language for a data scientist. Learning and practice it like a guru! Python is very friendly and adopted for scientific computing.
  2. Standard libraries: Libraries such as NumPy, SciPy, Pandas, and Scikit-learn are designed to make Data Science and Machine Learning easier. You need them every day!

Check the following useful resources:


4. Machine Learning / Deep Learning

There are many many different machine learning areas and techniques. This involves deep neural networks as the usual number one choice. Without knowing categories such as supervised learning and unsupervised learning and a variety of powerful Deep Learning approaches, you cannot tackle or solve a lot of Data Science problems. When it comes to prediction and data generation, Machine Learning and Deep Learning are the must-known.

One maddeningly true fact: YOU NEED TO MASTER MACHINE LEARNING!

Check the following free ebook to explore about Deep Learning:


Check the following great resources:

5. Database Management (SQL)

Machine Learning Data

While working with large databases, we may need to collect and constantly access data having billions of data points, possibly. Working with SQL is easy and accessing data is fast using it. Those two characteristics make SQL a great choice.

SQL (Structured Query Language) is utilized to interact with a database. ANSI (American National Standards Institute) introduces SQL as the standard language for relational database management systems. SQL commands are employed to execute jobs such as retrieving, updating or removing data components in a database. Some familiar SQL-based relational database management systems: Oracle, Microsoft SQL Server, etc.

Although NoSQL and Hadoop have been largely adopted as part of Data Science, still, having the skill of executing rather complex queries in SQL is of great importance.

Check the following resources:

Conclusion

Well, you learned about what are the most important Data Science skills. The story of you becoming a successful Data Scientist does NOT end there! Those skills are the necessary ones. Not only there is a learning curve for them, but also the real-world experience is what makes people unique. The more someone becomes involved in the work itself, the more s/he realizes how much experience matters. Also, I only shared my personal view which comes from researching the topic and well as my personal experience. Do you have a point of view to share? Feel free to comment below so we can learn from each other.

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