Roadmap To Becoming A Data Scientist in 2023

Currently, No one is surprised that Big Data, Artificial Intelligence, or Data Science issues matter week by week in the media. As it is already a fact, data changed the world And with this, our continuous use of technology. This constant production of data has led to the rise of many profiles, especially in data processing and analytics, an essential role of a data scientist if you are. Do not miss this post’s content to learn more about this profession or want to work as a data scientist in the future.

Before mentioning the skills required by a data scientist, it is essential to know what a data scientist is?

A data scientist is a person who is an expert in statistics, who puts this knowledge into practice by programming programs to extract the maximum value from the data, either as a source. Or from multiple sources, this data can be structured, semi-structured, or unstructured. According to data scientist Josh Wish “Data Scientist: Person who is better at statistics than a programmer and who is better at programming than any statistician.”

An educational requirement in a Data scientist

There are many paths to reach your goal as a data scientist, but keep in mind that most of these paths go through college, as a four-year college degree is a minimum requirement.

The best path is to complete a data science bachelor’s degree as it will teach you the skills necessary to collect, analyze, and interpret large amounts of data. You will know everything about statistics, analysis techniques, programming languages, etc. And This will help you in your work as a data scientist.

Another route you can take is to complete any technical college that helps you in your role as a data scientist, such as studying computer science, statistics, mathematics, economics, etc. It will be good for beginners to complete a job or master’s and doctorate.

Skills to become a data scientist.

1. Statistical Analysis:

As a data scientist, your main job is to collect, analyze and explain a large amount of data to create valuable insights for your company. Of course, statistical analysis is a big part of the job description! In other words, you need to be familiar with the basics of statistical analysis, such as statistical tests, distribution, linear regression, and probability theory. Several analytical tools such as SAS, Hadoop, Spark, Hive, etc. these are useful for statistical analysis as a data scientist. It is essential to have accurate knowledge of these technologies.

2. Programming skills:

It is necessary to write algorithms according to the requirements a examine and understand the data, for which it is imperative to learn the programming language. Today, Python is the most popular language among data scientists and artificial intelligence engineers, and the most important libraries you need are NumPy, SciPy, Pandas, Matplotlib, SciKit-Learn. The R programming language has more than 5000 libraries and differs in data visualization. But nowadays, Python is more popular because it is faster than R, and both have a robust library and a strong developer community.

3. Machine learning:

You have probably heard about machine learning. It allows machines and computers to learn from the training without specific programming is done using various data and algorithms. Therefore, you need to be aware of supervised and unsupervised learning algorithms in machine learning, such as linear regression, logistic regression, decision tree, K-Nearest Neighbourhood, etc.

4. Data Management:

It makes no sense to work with data without experience in data management languages like SQL and NoSQL. Data plays a vital role in data scientists’ lives, so you need to be familiar with “data management,” including extracting, modifying, and uploading data. It means that you need to extract data from different sources, convert it to the format you need for your analysis, and finally download it for data warehouses. You also need to pay attention to the conflicting data, which you need to consistently clean and validate your data before analyzing it for practical insights.

5. Data Intuition:

Don’t underestimate the power of data intuition! Distinguishing between a scientist and a data analyst is an essential technical skill. It’s almost like finding a needle in a haystack that represents the real potential of an undeveloped big data mountain. Teaching data intuition is not an easy skill, but it brings constant experience and practice. It makes him more effective and valuable in his role as a data scientist.

6. Communication Skills:

To become an expert in data scientists, you need communication skills. The data results need to be converted into quantitative assumptions of non-technical teams to assist in decision-making. You need to be able to present your data in a way that others can understand. You say.

Online Platforms to learn data science

There are many MOOCs(Massive Open Online Courses) platforms from which you can take a data science course. Both free and paid courses are available on these platforms.

1. Udacity(Nanodegree)

2. Coursera

3. edX

4. Analytics Vidhya

5. Udemy

6. Lambda School

Note: It takes a lot of hard work, time, dedication, consistency to become a data scientist. It is a professional career that will change your life completely. Always keep learning something new related to data science.