Data Science Roadmap 2025 : How to Become Data Scientist?

Data Science Roadmap: An Overview

How to Become a Data Scientist?

In the world of data space, the era of big data began when companies began handling petabytes and exabytes of data.By 2010, it had become very difficult for the industry to store data.Now, once the storage problem is solved by popular frameworks like Hadoop, the focus shifts to processing the data.Data science plays a big role here.Data science is growing in many ways today, and you should prepare for the future by learning what data science is and how it can add value.

However, if you are more curious about How to become a data scientist or How to begin your career as a data scientist consider learning this article: How to become a data scientist?

What is a data science?

So the first question that arises is, “What is data science?” Data science means different things to different people, but at its core, data science is about using data to answer questions.This definition is reasonably broad because it must be said that data science is a reasonably broad field.

Data science is the science of using statistics and machine learning techniques to analyze raw data and draw conclusions from that information.So we can say that data science includes: Statistics, Computer Science, and Mathematics Data Cleaning and Formatting Data Visualization By now, we all know how popular data science is.

This raises the following questions:

• Why do data science (set your goals in the first place)
• how do you get started? Where do you start?
• What topics should I cover?

If you want to learn all the concepts of Data science consider learning Data science Tutorial.

Read More - Data Science Interview Questions And Answers

How to Learn Data Science?

Data scientists typically have a variety of educational and professional experiences, and most are or should ideally be skilled in four main areas:

• Domain Knowledge
• Mathematical Skills
• Computer Science
• Communication Skills

Domain Knowledge :

Most people think that domain knowledge is not important in data science, but it is very important.Let me give you an example, If you want to become a data scientist in the banking sector and have more information about the banking sector such as stock trading and finance, it will be very beneficial for you. The banks themselves will have more preference.This type of applicant has an advantage over regular applicants.

Mathematics Skills:

Computer Science:

Some useful learning resource links from scholarhat:

1. Data Science
2. Python
3. AI Tutorial
4. Data Structure
5. Machine Learning

What is a Data Science Roadmap?

The easiest way to answer this question is to first define the term "roadmap." A roadmap is a strategic plan that establishes a goal or desired outcome and includes the important steps or milestones needed to achieve that goal.Meanwhile, according to this article, data science is: Data science is a combination of statistics, mathematics, programming, and problem-solving.Capture data in a sophisticated way.The ability to see things differently.

A Roadmap to Learn:

1. Mathematics:

In Data Science Math skills are very crucial as they help us in understanding various machine learning algorithms. You have to learn some basic concepts of math for it.

Section 1:

• Linear Algebra
• Analytic Geometry
• Matrix
• Vector Calculus
• Optimization

Section 2:

• Regression
• Dimensionality Reduction
• Density Estimation
• Classification

2. Probability:

It is also significant to statistics, and it is considered a prerequisite for mastering machine learning.

• Introduction to Probability
• 1D Random Variable
• The function of One Random Variable
• Joint Probability Distribution
• Discrete Distribution
• Continuous Distribution
• Normal Distribution (Python | R)

3. Statistics

Understanding Statistics is very important as this is a part of Data analysis:

• Introduction to Statistics
• Data Description
• Random Samples
• Sampling Distribution
• Parameter Estimation
• Hypotheses Testing (Python | R)
• ANOVA (Python | R)
• Reliability Engineering
• Stochastic Process
• Computer Simulation
• Design of Experiments
• Simple Linear Regression
• Correlation
• Multiple Regression (Python | R)
• Nonparametric Statistics
• Sign Test
• The Wilcoxon Signed-Rank Test (R)
• The Wilcoxon Rank Sum Test
• The Kruskal-Wallis Test (R)
• Statistical Quality Control
• Basics of Graph

4. Programming

Good knowledge of programming concepts such as data structures and algorithms is required.The programming languages ​​used are Python, R, Java, and Scala.C++ is also useful when performance is critical.

Python:

• Python Basics
• List
• Set
• Tuples
• Dictionary
• Function, etc.

R:R Basics

• Vector
• List
• Data Frame
• Matrix
• Array
• Function, etc.

Database:

• SQL
• MongoDB

Other:

• Data Structure(Time Complexity)
• Web Scraping (Python | R)
• Linux
• Git

5. Machine Learning:

It is one of the most important areas of data science and the hottest research topic among researchers, So we see new advances every year.At a minimum, you should understand the basic algorithms of supervised and unsupervised learning.In Python and R, several libraries can be used to implement these algorithms.

6. Deep Learning:

It uses TensorFlow and Keras to build and train neural networks for structured data.

• Artificial Neural Network
• Convolutional Neural Network
• Recurrent Neural Network
• TensorFlow
• Keras
• PyTorch
• A Single Neuron
• Deep Neural Network
• Stochastic Gradient Descent
• Overfitting and Underfitting
• Dropout Batch Normalization
• Binary Classification

7.Feature Engineering:

Discover the most effective way to improve your models.

• Baseline Model
• Categorical Encodings
• Feature Generation
• Feature Selection

8.Natural Language Processing:

• Text Classification
• Word Vectors

9.Data Visualization Tools:

Make good data visualizations. A great way to see the power of coding!Excel VBA

BI (Business Intelligence):

• Tableau
• Power BI: Qlik View, Qlik Sense

10.Deployment:

The last part is doing the deployment. Whether you are fresher or 5+ years of experience, or have 10+ years of experience, deployment is necessary. Because deployment will give you the fact that you worked a lot.

• Microsoft Azure
• Heroku
• Google Cloud Platform
• Flask
• DJango

11.Keep Practicing :

keep practicing and improving your knowledge day by day. Don't miss your chance to ride the wave of the data revolution!Every industry is reaching new heights by harnessing the power of data.Sharpen your skills and become part of the hottest trends of the 21st century.

Summary:

You should also keep in mind that learning the data science field can take time. So be persistent, be patient, and be open to feedback. By continually learning, building your skills, and expanding your network, you increase your chances of finding the right job. Fill free to give feedback on this article. Also, consider learning Artificial Intelligence Certification Course and Data Science with Python Certification Course for better understanding.