Must-have Skills to Master Data Science

The need to process a massive amount of data sets is making Data Science the most-demanded job across diverse industry verticals. In today’s times, organizations are actively looking for Data Scientists.

But What does a Data Scientist do?

Data Scientist design data models, create various algorithms to extract the data the organization needs, and then they analyze the gathered data and communicate the data insights with the business stakeholders.

If you are looking forward to pursuing a career in Data Science, then this blog is for you 🙂

Data Scientists often come from many different educational and work experience backgrounds but few skills are common and essential.

Let’s have a look at all the essential skills required to become a Data Scientist:

1. Multivariable Calculus & Linear Algebra
2. Probability & Statistics
3. Programming Skills (Python & R)
4. Machine Learning Algorithms
5. Data Visualization
6. Data Wrangling
7. Data Intuition

Let’s dive deeper into all these skills one by one.

Multivariable Calculus & Linear Algebra:

Having a solid understanding of math concepts is very helpful for a Data Scientist.

Key Concepts:

• Matrices
• Linear Algebra Functions
• Derivatives and Gradient
• Relational Algebra

Probability & Statistics:

Probability and Statistics play a major role in Data Science for estimation and prediction purposes.

Key concepts required:

• Probability Distributions
• Conditional Probability
• Bayesian Thinking
• Descriptive Statistics
• Random Variables
• Hypothesis Testing and Regression
• Maximum Likelihood Estimation

Programming Skills (Python & R):

Python :

Start with Python Fundamentals using a jupyter notebook, which comes pre-packaged with Python libraries.

Important Python Libraries used:

• NumPy (For Data Exploration)
• Pandas (For Data Exploration)
• Matplotlib (For Data Visualization)

R:

It is a programming language and software environment used for statistical computing and graphics. 

Key Concepts required:

• R Languages fundamentals and basic syntax
• Vectors, Matrices, Factors
• Data frames
• Basic Graphics

Machine Learning Algorithms

Machine Learning is an innovative and essential field in the industry. There are quite a few algorithms out there, major ones are as follows –

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Naïve Bayes
• Support Vector Machines
• Dimensionality Reduction
• K-means
• Artificial Neural Networks

Data Visualization:

Data visualization is very essential when it comes to analyzing a massive amount of information and data. 

To make data-driven decisions, data visualization tools, and technologies are essential in the world of Data Science.

Data Visualization tools:

• Tableau
• Microsoft Power Bi
• E Charts
• Datawrapper
• HighCharts

Data Wrangling:

Data wrangling, this term refers to the process of cleaning and refining the messy and complex data available into a more usable format. 

It is considered one of the most crucial parts of working with data.

Important Steps to Data Wrangling:

1. Discovering
2. Structuring
3. Cleaning
4. Enriching
5. Validating
6. Documenting

Tools used:

• Tabula
• Google DataPrep
• Data Wrangler
• CSVkit

Data Wrangling can be done using Python and R.

Data Intuition:

Data Intuition in Data Science is an intuitive understanding of concepts. It’s one of the most significant skills required to become a Data Scientist.

It’s about recognizing patterns where none are observable on the surface.

This is something that you need to develop. It is a skill that will only come with experience.

A Data Scientist should know which Data Science methods to apply to the problem at hand.

Conclusion:

 As you can see, all these skills – from programming to algorithmic methods, work with one another to build on top of each other for gathering deeper data insights.

There are a wide number of courses available online for developing these skills and to help you become a true talent in this data industry.

Sure, this journey isn’t an easy one to follow but it’s not impossible. With sheer determination and consistency, you will be able to cross all the hurdles in your Data Science career path.