# How to Become a Data Scientist – A Complete Roadmap

Welcome to your comprehensive ** Data Science Roadmap**! If you’ve ever wondered, “

**”, you’re in the right place. This guide is perfect for**

**How to Become a Data Scientist****and seasoned professionals alike, covering everything from mastering**

**Data Science for Beginners****and**

**Python for Data Science****, to understanding the importance of**

**R for Data Science****and**

**Data Cleaning****.**

**Data Visualization**We’ll delve into the essential ** Data Science Tools** and how they’re used in real-world applications, including

**and**

**Machine Learning****. You’ll also learn about the role of**

**AI in Data Science****and get hands-on with**

**Statistics for Data Science****. In this rapidly evolving field,**

**Real-world Data Science Projects****is key. So, we’ll keep you updated with the latest**

**Continuous Learning in Data Science****to help you stay ahead in your**

**Data Science Trends****. Let’s embark on this exciting journey together.**

**Data Science Career**Join our

“Complete Machine Learning & Data Science Programto master data analysis, machine learning algorithms, and real-world projects. Get expert guidance and kickstart your career in data science today!“

## What is Data Science?

**Data science**** is the field of study that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data**. It combines various disciplines such as statistics, machine learning, data analysis, and visualization to uncover hidden patterns, trends, and correlations in data. Data science plays a crucial role in decision-making, forecasting, and problem-solving across industries, driving innovation and enabling organizations to make data-driven decisions.

**.**So briefly it can be said that Data Science involves:

- Statistics, computer science, mathematics
- Data cleaning and formatting
- Data visualization

Nowadays it is known to everyone how popular is ** Data Science**. Now the questions that arise are,

**Why Data**

**Science?***Do you need to learn all the concepts from a book or you should go with some online tutorials or you should learn Data Science by doing some projects on it? So in this article, we are going to discuss all these things in detail.*

**, how to start? Where to start? What topics one should cover? etc.****Why Data Science?**

**Why Data Science?**

So before jumping into the complete Roadmap of Data Science, one should have a clear goal in their mind about why they want to learn Data Science. Is it for the phrase “** The Sexiest Job of the 21st Century**“? Is it for your college academic projects? or is it for your long-term career? or do you want to switch your career to the data scientist world? So first make a clear goal.

** Why do you want to learn Data Science? **For example, if you want to learn Data Science for your college

**then it’s enough to just learn the**

**Academic projects****. Similarly, if you want to build your**

**beginner things in Data Science****then you should learn**

**long-term career****or**

**professional****also. You have to cover all the prerequisite things in detail. So it’s in your hand and it’s your decision why you want to learn Data Science.**

**advanced things**## What Does a Data Scientist Do?

A Data Scientist gathers and analyzes complex data to guide business decisions. They collect, clean, and explore data, develop machine learning models, and deploy them for real-world use.

Data Scientists also monitor and maintain models, communicate findings to non-technical stakeholders, and collaborate across teams to align with organizational goals.

## Why Become a Data Scientist

In the global landscape, ** data is the new oil**,

**and**

**driving innovation****Organizations crave skilled professionals to extract insights from this vast information ocean, and here’s where data scientists play a crucial role.**

**reshaping industries.****High Demand**

**High Demand**

- US Bureau of Labor Statistics forecasts a
for data scientists**23% job growth**, surpassing the average.**(2020-2030)** - Similar global trends indicate a surge in demand.

**Lucrative Salaries**

**Lucrative Salaries**

- Handsome rewards for expertise; US data scientists earn over
**$120,000 annually.** - In India, experienced professionals can fetch upwards of
**₹15 lakhs (USD 18,750).**

**Impactful Work**

**Impactful Work**

Develop algorithms for**Tangible societal impact:**,**disease detection**, or**optimize energy grids****predict natural disasters.**

## Skills Required to Become a Data Scientist

Usually, data scientists come from various educational and work experience backgrounds, and most should be proficient in, or in an ideal case be masters in four key areas.

**Domain Knowledge****Math Skills****Computer Science****Communication Skill**

**Domain Knowledge**

**Domain Knowledge**

Most people think that domain knowledge is not important in data science, but it is very important. Let’s take an example: If you want to be a data scientist in the banking sector, and you have much more information about the banking sector like stock trading, finance, etc. This is going to be very beneficial for you and the bank itself will give more preference to these types of applicants than a normal applicant.

### Math Skills

**Linear Algebra, Multivariable Calculus**** & Optimization Techniques, are **three things that are very important as they help us in understanding various machine learning algorithms that play an important role in Data Science. Similarly, understanding

**is very significant as this is a part of Data analysis.**

**Statistics****is also significant to statistics and it is considered a prerequisite for mastering machine learning.**

**Probability****Computer Science**

**Computer Science**

There is much more to learn in computer science. But when it comes to the programming language one of the major questions that arise is:

**Python or R for Data Science?**

There are various reasons to choose which language for Data Science as both have a rich set of libraries to implement complex machine learning algorithms, visualization, and data cleaning. Please refer to R vs Python in Data Science to know more about this. Knowing both of these languages will provide an extra boost in your career as a data scientist.

Apart from the programming language, the other computer science skills you have to learn are:

- Basics of Data Structure and Algorithm
- SQL
- MongoDB
- Linux
- Git
- Distributed Computing
- Machine Learning and Deep Learning, etc.

**Communication Skills**

**Communication Skills**

It includes both ** written **and

**. What happens in a data science project is after concluding the analysis, the project has to be**

**verbal communication****. Sometimes this may be a**

**communicated to others****you send to your boss or team at work. Other times it may be a**

**report****. Often it may be a**

**blog post****to a group of colleagues.**

**presentation**Regardless, a data science project always involves some form of communication of the project’s findings. So it’s necessary to have communication skills for becoming a data scientist.

**Learning Resources**

**Learning Resources**

There are plenty of resources and videos available online and it’s confusing for someone where to start learning all the concepts. Initially, as a beginner, if you get overwhelmed with so many concepts then don’t be afraid and stop learning. Have patience, explore, and stay committed to it.

Some useful learning resource links are available at GeeksforGeeks:

## Data Scientist vs Data Analyst

Here is a quick comparison of Data Scientist and Data Analyst

Aspect | Data Scientist | Data Analyst |
---|---|---|

Scope | Broader focus: machine learning, predictive modeling. | Focus: analyzing data, and providing insights. |

Focus | Uncovering patterns, and predicting trends. | Summarizing historical data, providing insights. |

Responsibilities | End-to-end processes, complex models. | Proficient in tools, statistical methods, and reporting. |

Tools | Advanced: machine learning, Python/R. | Tools: Excel, Tableau, Power BI. |

Data Types | Structured, unstructured, large datasets. | Primarily structured data, occasional smaller sets. |

Outcome | Extract actionable insights, and solve complex problems. | Summarize data, and provide insights for decision-making. |

Overlap | Some overlap and Analysts contribute to the early stages. | Distinct roles, potential for collaboration. |

## Average Salary of a Data Scientist

The average salary of a data scientist varies depending on several factors, including ** experience**,

**, and**

**location****However, it’s generally a high-paying profession with strong growth prospects. Here’s a breakdown:**

**skillset.****Global Average**

**Global Average**

- The worldwide average annual salary for a data scientist is around
. (Source:**$105,000**)**Glassdoor**

**United States**

**United States**

- In the US, the average annual salary for a data scientist is
. (Source:**$124,678**)**Indeed** - The median salary is
, according to the Bureau of Labor Statistics. (Source:**$103,500**)**BLS** - Entry-level data scientists can expect to earn around
, while experienced data scientists with specialized skills can make upwards of**$86,000**. (Source:**$156,000**)**Glassdoor**

**India**

**India**

- In India, the average annual salary for a data scientist is
. (Source:**₹7,08,012**)**PayScale** - Freshers can expect to start at around
, while experienced professionals can earn as much as**₹5,77,893**. (Source:**₹19,44,566**)**KnowledgeHut**

**Factors Affecting Salary**

**Factors Affecting Salary**

Multiple factors might affect your salary as a data scientist:

As with most professions, experience plays a significant role in determining a data scientist’s salary. The more experience you have, the higher your earning potential.**Experience:**Salaries for data scientists tend to be higher in major tech hubs like San Francisco, New York, and Bangalore compared to smaller cities or rural areas.**Location:**Data scientists with specialized skills in areas like machine learning, natural language processing, or specific programming languages can command higher salaries.**Skills and Expertise:**Large tech companies and startups may offer different salary structures and benefits packages.**Company Size and Type:**

## How to Become a Data Scientist – Roadmap 2024

This ** data science career roadmap** provides a structured path to master the critical

**needed for success. Remember, data science is**

**concepts and skills****, so staying**

**dynamic****with trends and technologies is**

**current****. Gaining**

**key****through projects and internships can**

**real-world experience****your skills and**

**boost****as a data scientist. Follow this roadmap,**

**credibility****, and**

**continuously learn****for a**

**adapt to advancements**

**rewarding data science journey**### 1) Mathematics

Math skills are very important as they help us understand various machine-learning algorithms that play an important role in Data Science.

**Part 1:**- Linear Algebra
- Analytic Geometry
- Matrix
- Vector Calculus
- Optimization

**Part 2:**

### 2) **Probability**

**Probability**

** Probability** 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
- Uniform
- Exponential
- Gamma

- Normal Distribution (Python | R)

### 3) **Statistics**

**Statistics**

Understanding ** Statistics** is very significant 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 Graphs

### 4) **Programming**

**Programming**

One needs to have a good grasp of programming concepts such as ** Data structures and Algorithms**. The programming languages used are

**Python, R,**

**Java**

**,****.**

**Scala****is also useful in some places where performance is very important.**

**C++****Python:**- Python Basics
- NumPy
- Pandas
- Matplotlib/Seaborn, etc.

**R:****DataBase:**- SQL
- MongoDB

**Other:**

### 5) Machine Learning

ML is one of the most vital parts of data science and the hottest subject of research among researchers so each year new advancements are made in this. One at least needs to understand the basic algorithms of **Supervised and Unsupervised**** Learning**. There are multiple libraries available in Python and R for implementing these algorithms.

**Introduction:**- How Model Works
- Basic Data Exploration
- First ML Model
- Model Validation
- Underfitting & Overfitting
- Random Forests (Python | R)
- scikit-learn

**Intermediate:**

### 6) **Deep Learning**

**Deep Learning**

Deep Learning 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**

**Feature Engineering**

In Feature Engineering discover the most effective way to improve your models.

- Baseline Model
- Categorical Encodings
- Feature Generation
- Feature Selection

### 8) **Natural Language Processing**

**Natural Language Processing**

In NLP distinguish yourself by learning to work with text data.

- Text Classification
- Word Vectors

### 9) Data Visualization Tools

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

### 10) Deployment

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

### 11) Other Points to Learn

- Domain Knowledge
- Communication Skill
- Reinforcement Learning
**Different Case Studies:**- Data Science at Netflix
- Data Science at Flipkart
- Project on Credit Card Fraud Detection
- Project on Movie Recommendation, etc.

**12) Keep Practicing**

**12) Keep Practicing**

**“Practice makes a man perfect” which tells the importance of continuous practice in any subject to learn anything. **

So keep practicing and improving your knowledge day by day. Below is a complete diagrammatical representation of the Data Scientist Roadmap.

## How to Become a Data Scientist: Education Routes

Regardless of your academic path, unlock success through ** lifelong learning and skill mastery**. Dive into coding languages like Python and R, conquer statistics and machine learning fundamentals, whether your background is in

**. Gain**

**computer science, math, or beyond****through data science projects, internships, and**

**hands-on experience****. Build a**

**powerful networking****and stay ahead of the curve with the**

**robust skillset**

**latest data science trends.****Educational Background:****Bachelor’s Degree:**- Most data scientists have at least a bachelor’s degree in fields like
,**computer science**,**statistics**, or**mathematics****engineering.** - Non-traditional backgrounds are okay, but having a solid foundation in quantitative subjects is beneficial.

- Most data scientists have at least a bachelor’s degree in fields like
**Advanced Degrees:**- Many data scientists pursue master’s or
especially for specialization or research.**Ph.D. degrees,** - Degrees in
,**data science**,**machine learning**or related fields are increasingly available.**artificial intelligence,**

- Many data scientists pursue master’s or

**Core Skills:****Programming Languages:**- Learn languages commonly used in data science, like
or**Python****R.** - Use libraries and frameworks such as
**NumPy,**,**Pandas**,**scikit-learn**, and**TensorFlow****PyTorch.**

- Learn languages commonly used in data science, like
**Statistics and Mathematics:**- Understand statistical concepts and mathematical foundations, including linear algebra and calculus.

**Data Manipulation and Analysis:**- Master data manipulation and analysis with tools like
and**SQL****Pandas.**

- Master data manipulation and analysis with tools like
**Machine Learning:**- Gain expertise in
,**machine learning algorithms**and**covering supervised****unsupervised learning,****regression, classification,**etc.**clustering,**

- Gain expertise in
**Data Visualization:**- Communicate insights through visualization tools like
,**Matplotlib**or**Seaborn,****Tableau.**

- Communicate insights through visualization tools like
**Big Data Technologies:**- Familiarize yourself with big data technologies like
and**Hadoop****Spark.**

- Familiarize yourself with big data technologies like

**Projects and Practical Experience:**- Work on real-world projects to apply knowledge and build a portfolio.
- Participate in Kaggle competitions or similar challenges.
- Contribute to open-source projects or collaborate on data-related projects.

**Networking:**- Attend data science meetups, conferences, and networking events.
- Join
**online communities,**, and**forums**related to data science.**social media groups**

**Continuous Learning:**- Stay updated with the latest trends and technologies in data science.
- Take
**online courses,**, and**attend workshops**for skill enhancement.**pursue certifications**

**Internships and Work Experience:**or**Seek internships**for practical experience.**entry-level positions**- Get exposure to real-world data science problems.

**Soft Skills:**- Develop communication skills to convey findings effectively to non-technical stakeholders.
- Cultivate problem-solving, critical thinking, and attention to detail.

## Conclusion

In the 21st century, data science has emerged as a crucial profession, often dubbed ** “The Sexiest Job”** by Harvard Business Review. With the rise of

**and frameworks like**

**Big Data****data science focuses on processing vast amounts of data. This field’s significant growth underscores its importance for future readiness.**

**Hadoop,**The comparison between data science and data analyst roles highlights data scientists’ broader scope and responsibilities in predicting trends and solving complex problems. To become a data scientist, a strong educational background, core skills in programming and statistics, practical experience through projects, and continuous learning are essential.

The global demand for data scientists is high, offering lucrative salaries and impactful work opportunities. The roadmap for learning data science covers key domains like mathematics, programming, machine learning, deep learning, natural language processing, data visualization, and deployment. Continuous practice, networking, and soft skills development are emphasized for success in this dynamic field.

## FAQs

### What qualifications do you need to be a data scientist?

Master Python, R, SQL, and Java for data science, blend math foundations with efficient data handling (Pandas, SQL), and hone soft skills. Pursue relevant degrees or alternative paths, build a standout portfolio, network, and stay updated for success in this dynamic field.

### Is data science an IT job?

Data science is more closely tied to statistics, mathematics, and business intelligence than traditional IT. While it leverages technology heavily, its primary focus lies on the analysis and interpretation of data, making it a distinct field with its own set of skills and goals.

### Is it hard to become a Data Scientist?

Becoming a data scientist requires a lot of skills and dedication. It involves mastering technical skills like mathematics, programming and various tools. There is a lot of competition and is evolving at high rate. Learning data science depends on your dedication and approach.

### Is data science dead in 10 years?

, Global data explosion requires skilled interpreters—data scientists. Applications expand across sectors, from healthcare to art. Automation aids but can’t replace vital data scientist skills. Continuous learning is crucial in the evolving data science landscape.No

### Can you become a data scientist without a degree?

Master

,Python,R, andstats, math, Pandas, SQL, MLBuild a strong portfolio, contribute to open source, network at meetups. Stay connected online, keep learning, and persistently showcase skills to break into the field.data viz.

### How long does it take to become a data scientist?

Enter data science in 6 months to a year with a strong background. Traditional degrees take

bootcamps4 years,Analyst roles may come sooner, specialized positions 2-5 years or more. Consistent practice speeds progress. Estimates:3 months to a year.Entry (6 months – 1 year),,Junior (1-2 years)Mid-level (2-5 years),Senior (5+ years).

### What are the benefits of becoming a data scientist?

Some of the benefits of becoming data scientist include:

- High demand & salary
- Versatile career
- Personal growth
- Global opportunities