Data Scientist Roadmap – A Complete Guide

Welcome to your comprehensive Data Science Roadmap! If you’ve ever wondered, about “Steps or Path to Become a Data Scientist”, you’re in the right place. This guide is perfect for Data Science for Beginners and seasoned professionals alike, covering everything from mastering Python for Data Science and R for Data Science, to understanding the importance of Data Cleaning and Data Visualization.

We’ll delve into the essential Data Science Tools and how they’re used in real-world applications, including Machine Learning and AI in Data Science. You’ll also learn about the role of Statistics for Data Science and get hands-on with Real-world Data Science Projects. In this rapidly evolving field, Continuous Learning in Data Science is key. So, we’ll keep you updated with the latest Data Science Trends to help you stay ahead in your Data Science Career. Let’s embark on this exciting journey together.

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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?, how to start? Where to start? What topics one should cover? etc. 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.

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 Academic projects then it’s enough to just learn the beginner things in Data Science. Similarly, if you want to build your long-term career then you should learn professional or advanced things 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.

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

High Demand

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

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

• Tangible societal impact: Develop algorithms for disease detection, optimize energy grids, or 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.

1. Domain Knowledge
2. Math Skills
3. Computer Science
4. Communication Skill

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 Statistics is very significant as this is a part of Data analysis. Probability is also significant to statistics and it is considered a prerequisite for mastering machine learning.

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

It includes both written and verbal communication. What happens in a data science project is after concluding the analysis, the project has to be communicated to others. Sometimes this may be a report you send to your boss or team at work. Other times it may be a blog post. Often it may be a presentation to a group of colleagues. 

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

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 neveropen:

• Mathematics
• Python
• R
• SQL
• Data Structure
• Machine Learning

Data Scientist vs Data Analyst

Here is a quick comparison of Data Scientist and Data Analyst

Average Salary of a Data Scientist

The average salary of a data scientist varies depending on several factors, including experience, location, and skillset. However, it’s generally a high-paying profession with strong growth prospects. Here’s a breakdown:

Global Average

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

United States

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

India

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

Factors Affecting Salary

Multiple factors might affect your salary as a data scientist:

• Experience: 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.
• Location: 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.
• Skills and Expertise: Data scientists with specialized skills in areas like machine learning, natural language processing, or specific programming languages can command higher salaries.
• Company Size and Type: Large tech companies and startups may offer different salary structures and benefits packages.

Data Scientist Roadmap[2024]

This data science career roadmap provides a structured path to master the critical concepts and skills needed for success. Remember, data science is dynamic, so staying current with trends and technologies is key. Gaining real-world experience through projects and internships can boost your skills and credibility as a data scientist. Follow this roadmap, continuously learn, and adapt to advancements for a 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:
  • Regression
  • Dimensionality Reduction
  • Density Estimation
  • Classification

2) 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
  • Binomial (Python | R)
  • Bernoulli
  • Geometric etc
• Continuous Distribution
  • Uniform
  • Exponential
  • Gamma
• Normal Distribution (Python | R)

3) 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

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. C++ is also useful in some places where performance is very important.

• Python:
  • Python Basics
    • List
    • Set
    • Tuples
    • Dictionary
    • Function, etc.
  • NumPy
  • Pandas
  • Matplotlib/Seaborn, etc.
• R:
  • R Basics
    • Vector
    • List
    • Data Frame
    • Matrix
    • Array
    • Function, etc.
  • dplyr
  • ggplot2
  • Tidyr
  • Shiny, etc.
• DataBase:
  • SQL
  • MongoDB
• Other:
  • Data Structure
    • Time Complexity
  • Web Scraping (Python | R)
  • Linux
  • Git

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:
  • Handling Missing Values
  • Handling Categorical Variables
  • Pipelines
  • Cross-Validation (R)
  • XGBoost (Python | R)
  • Data Leakage

6) 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

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

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

8) 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!

• Excel VBA
• BI (Business Intelligence):
  • Tableau
  • Power BI
  • Qlik View
  • Qlik Sense

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.  

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

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

“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.

Data Scientist Roadmap: 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 computer science, math, or beyond. Gain hands-on experience through data science projects, internships, and powerful networking. Build a robust skillset and stay ahead of the curve with the latest data science trends.

1. Educational Background:
   • Bachelor’s Degree:
     • Most data scientists have at least a bachelor’s degree in fields like computer science, statistics, mathematics, or engineering.
     • Non-traditional backgrounds are okay, but having a solid foundation in quantitative subjects is beneficial.
   • Advanced Degrees:
     • Many data scientists pursue master’s or Ph.D. degrees, especially for specialization or research.
     • Degrees in data science, machine learning, artificial intelligence, or related fields are increasingly available.
2. Core Skills:
   • Programming Languages:
     • Learn languages commonly used in data science, like Python or R.
     • Use libraries and frameworks such as NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch.
   • 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 SQL and Pandas.
   • Machine Learning:
     • Gain expertise in machine learning algorithms, covering supervised and unsupervised learning, regression, classification, clustering, etc.
   • Data Visualization:
     • Communicate insights through visualization tools like Matplotlib, Seaborn, or Tableau.
   • Big Data Technologies:
     • Familiarize yourself with big data technologies like Hadoop and Spark.
3. 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.
4. Networking:
   • Attend data science meetups, conferences, and networking events.
   • Join online communities, forums, and social media groups related to data science.
5. Continuous Learning:
   • Stay updated with the latest trends and technologies in data science.
   • Take online courses, attend workshops, and pursue certifications for skill enhancement.
6. Internships and Work Experience:
   • Seek internships or entry-level positions for practical experience.
   • Get exposure to real-world data science problems.
7. 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 Big Data and frameworks like Hadoop, data science focuses on processing vast amounts of data. This field’s significant growth underscores its importance for future readiness.

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 on Data Scientist Roadmap – A Complete Guide

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?

No, 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.

Can you become a data scientist without a degree?

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

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 4 years, bootcamps 3 months to a year. Analyst roles may come sooner, specialized positions 2-5 years or more. Consistent practice speeds progress. Estimates: 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