Introduction
Data science has quickly become one of the highest-paying and in-demand professions within the digital economy. As businesses now focus on data-driven decision-making more than ever, qualified candidates or trained data scientists, machine learning engineers, and AI specialists continue to skyrocket.
Provided your passion is to decode data, use machine learning algorithms, and tackle complex challenges, a career in data science is certainly for you.
From this guide, you will learn to become a data scientist in 7 steps, including skills, education, and opportunities. This roadmap will help beginners and professionals transitioning from other fields to start and scale their careers in data science.
Table of Content
Unlock the Power of Data Science
with Our Proven Certification
Who is a Data Scientist?
A data scientist is an expert who analyzes complex data sets using mathematical, statistical, or machine-oriented techniques. It is the data scientist’s role to convert raw data into implementable business strategies that assist organizations in decision-making based on facts and data.
Data scientists carry out a range of data-related tasks, such as preprocessing the data and performing exploratory data analysis to look into key business KPIs. After figuring out insights, they do the predictive modeling for future events using machine learning techniques.
An entry-level data scientist can earn a salary in the range of $90K-$120K, whereas one with hands-on experience earns more than $200K with bonuses. According to a Bureau of Labor Statistics report, job prospects in data science are expected to increase by 35% by the year 2030.
Notable corporations such as Google, Amazon, Tesla, and Facebook are injecting billions into AI and data-driven innovation making data science a perpetually flourishing career option.
What Does a Data Scientist Do?
The job role of a data scientist is primarily to collect and clean up data used in statistical analyses and build statistical models to help make predictions.
Let’s break down these tasks further to understand what exactly a data scientist does:
- Data Collection and Cleaning: Data scientists scrape data from websites and social media platforms. This process involves collecting, cleaning, and preparing raw data for analysis.
- Exploratory Data Analytics (EDA): By exploring data using various statistical techniques, the trends, anomalies, and patterns in the data are identified.
- Building Machine Learning Models: Construct AI-based models for outcome predictions and automation improvement.
- Data Visualization: Once the analysis is done, data analysts convert their findings into a visualization to help stakeholders understand the key insights. Transform the findings into graphs, plots, and dashboards using applications like Tableau, Power BI, and Matplotlib.
- Business Strategy and Decision Making: In the strategy and decision-making domain, data-driven insights are needed, especially for executives and stakeholders.
- Model Building: Data scientists further create predictive models that can be used to make predictions or recommendations based on the data.
Ready to test your Data Science Skills?
Take a quick Quiz to check it out
Skills Required to Become a Data Scientist
Data science in its broadest sense refers to the application of mathematics, programming, and machine learning in data extraction with a good measure of business prowess. A successful data scientist will possess technical and soft skills relevant to solving complex data problems.
Following are the technical attributes and set of soft skills a data scientist must possess:
Technical Skills for Data Science
- Programming Languages (Python, SQL, R)
- Python: The most popular language for data manipulation, machine learning, and AI.
- R: Used widely for statistical computing and visualization.
- SQL: Used as the primary interface for querying and maintaining databases.
- Data Processing and Analysis
- Efficient processing of large datasets using Pandas & NumPy.
- Data cleaning and visualization using Excel and Google Sheets.
- Data visualization for better insights using Matplotlib and Seaborn.
For example, a data scientist working with e-commerce has employed Pandas to understand consumer behavior as well as optimize the company’s marketing campaigns.
- Machine Learning and AI
- Scikit-learn – Implementation of supervised and unsupervised learning models.
- Tensor-Flow & Pytorch – Deep learning frameworks for AI development.
- XGBoost & LightGBM – Advanced methods of predictive modeling.
Tip: Begin with regression, decision trees, and clustering before getting into deep learning.
Data scientists must often work with huge volumes of data that can’t be processed in a single machine. Cloud platforms, as a result, have on-demand computing capabilities and distributed storage, in addition to being able to deploy any model in their AI kits to be the tool of choice for modern-day data professionals.
For example, a data scientist working at Netflix is training AI in the fields of recommendation engines through the application of AWS and Google Cloud to tailor the content for millions of consumers in real-time.
Why is Cloud Computing a Must-Have Skillset for Data Scientists?
- Most AI and ML workflows today take place within the cloud, which means that on-premise infrastructure is becoming less and less important.
- Companies hire data scientists who apply cloud technology to engineer seamlessly scalable and automated models.
- Cloud AI and big data solutions are changing verticals such as finance, healthcare, and cybersecurity industries.
- Big Data Technologies and Data Engineering
- Apache, Spark & Hadoop – These technologies are utilized in processing enormous datasets.
- ETL (Extract, Transfer, Load) Pipelines -Transfer of data across systems.
- Data Warehousing (Snowflake, Redshift) – To optimize data storage for analytic purposes.
For example, the streaming habits analysis and recommendations improvement are done by a Netflix data scientist using SQL + Redshift.
Soft Skills for Data Science
- Develop Strong Critical Thinking and Problem-Solving Skills
- Identify patterns not easily observed in datasets.
- Question the assumptions and overlook the cognitive biases in the analysis.
For example, a fintech company leverages anomaly detection models to detect fraud patterns.
- Master Effective Communication and Storytelling
- Data insights that are intricate and complex need to be explained in layman’s terms to ease comprehension by stakeholders who have no technical background.
- Develop data-led reports and dashboards that create a positive impact within a business.
Learn tools like Tableau, Power BI, and Google Data Studio to improve communication and storytelling
- Develop Strong Business Acumen and Industry Expertise
- Understanding the trends in the industries (that of finance, healthcare, retail, and marketing).
- Serve data science solutions directly aligned with business outcomes.
For example, an Amazon data scientist uses Machine Learning to optimize the efficiency of their supply chain.
How to Become a Data Scientist: 7 Steps Guide
To become successful in a data scientist career, follow these steps:
Step 1: Choose the right learning path (Degree v/s Self-Taught)
Traditional Degree: The vast majority of employers favor candidates who possess some recognized qualification in data science, computer science, or mathematics. A structured degree program provides an adequate theoretical foundation in statistics, machine learning, programming, and all the other things an aspiring data scientist would need.
You can earn a bachelor’s degree in Data Science, Computer Science, Mathematics and Statistics, or Engineering. If you will, you may also secure a master’s degree in Data Science, Business Analytics, or Artificial Intelligence.
Self-Taught Path: A lot of successful data scientists are self-taught and use freely available and paid online resources to acquire knowledge.
Online learning platforms include Intellipaat, Coursera, DataCamp, and more.
Though a formal education may not always guarantee your likelihood to land a job as a data scientist, strong technical skills plus back-in-the-field experience can set you on the right path.
If you’re looking forward to figuring out a solution to your query about how to become a data scientist, you must first understand that you will need to master a programming language. The most prominent programming languages used in the data science space are Python, R, and SQL.
- Python is an enabler for data handling, machine learning, and automation.
- SQL is the threshold language you need to learn to work with databases and effectively manage large data sets.
- R is useful for predictive modeling and data representation.
A few essential Data Science tools include:
- Jupyter Notebooks & Google Colab – This is used to write and test Python scripts.
- Big Data Technologies – Hadoop, Spark, Kafka.
- Visualization Tools – Tableau, Matplotlib, Seaborn, Power BI.
Tip: Both Python and SQL are considered to be necessary prerequisite skills for almost all data science jobs. Make them your top priority.
Step 3: Learn Data Wrangling and EDA (Exploratory Data Analysis)
Data Wrangling, also referred to as Data Preprocessing, is a process by which raw data is cleansed, converted, and made ready for use in analyses. It helps prepare data in a critical stage of data science because, in reality, data is often messy, incomplete, and inconsistent. Data wrangling also satisfies the requirement for quality inputs for models and decision-making.
Data wrangling improves data accuracy, model performance, speed, and efficiency over time, and facilitates feature engineering.
A real-world example would involve a retail company that records customer buying patterns from several sources; however, the data contains duplicate records, missing values, and inconsistent date formats. A data scientist cleanses and formats the data to allow the analysis of sales trends accurately.
Alteryx, Talend, and OpenRefine are the most commonly used wrangling tools.
The Exploratory Data Analysis (EDA) opens up possibilities for the data to be analyzed and visualized in view of searching for past trends, behavior, and relationships after which machine learning algorithms may be applied. EDA confirms to any data scientist the possibility of understanding the dataset without being biased by any assumptions.
Using EDA, a streaming platform (like Netflix) would analyze user watch history to find trends in favorite movie genres and tailor recommendations.
Step 4: Acquire Skills in Machine Learning & Deep Learning
Machine learning(ML) is an aspect of artificial intelligence(AI) in which systems can learn from data to make predictions, without being programmed to make a decision. Instead of fixed rules, ML creates patterns and trends for decision-making and thus increases the accuracy automatically.
Without machine learning, tasks such as predictive analysis and descriptive analysis cannot be done. You will have to spend a good amount of time learning the important ML algorithms. Starting from data classification, regression, and clustering to ensemble learning and dimensionality reduction, you must learn every concept under the umbrella of machine learning.
Netflix’s machine learning algorithms are used to analyze the watcher’s viewing habits and draw personalized content insights for recommendations.
Deep learning is a branch of ML where learning is materialized through artificial neural networks. The anatomy of the human brain inspires these models, and they are particularly good at doing difficult jobs such as image recognition, speech processing, and understanding natural languages.
In computer vision where one works with images, in speech recognition and AI assistants like Siri & Alexa, Deep Learning deals with relatively complex data.
Tesla’s Autopilot system is a major example of how AI is being applied for processing images and sensor data in real time using some form of deep learning.
Step 5: Gain Hands-On Experience to Strengthen Your Portfolio
A data scientist should master some basic technical skills if not none. Almost every company is looking for a person who has actual experience in analyzing real datasets and drawing meaningful insights from them. A strong portfolio of real-world projects will set you apart from the rest and prove your capabilities to potential employers.
Having a portfolio stuffed with Python, SQL, and machine learning projects demonstrates that you are capable of writing and optimizing code. The logic or reasoning concerning the project is what the recruiters will be interested in.
For example, a data scientist applying for a fintech position could document the entire project related to credit card fraud detection in a portfolio, demonstrating the ability to use machines to find anomalies.
A good portfolio is built up of various projects across different domains. This should include:
- A balanced portfolio – A broad and extensive list of topics includes contextual modeling, supervised, unsupervised, convolution neural networks, and NLP.
- Real-world datasets – Use openly accessible datasets from Kaggle, UCI, and various government repositories.
- Clear problem statements – Define the objectives with clarity and why the problem matters.
- Clean and very well-structured code – Write scripts using Jupyter with each injecting information about the code.
- Exploratory Data Analysis: Practise data cleaning, feature engineering, and visualization.
- Machine-learning models – Utilize and contrast different models to develop better understandings.
- Performance Metrics – Assess the models using important measures like accuracy, precision, recall, and RMSE.
- Data Visualization – Use Matplotlib, Seaborn, and Tableau for visualization.
- GitHub Repository & Blog posts – Share projects through GIthub, Kaggle, or your personal website.
Step 6: Earn Certifications to Improve Your Resume
The best way of showcasing your skills is via certifications in data science, especially if you are switching careers or do not hold an actual degree in data science. Certifications are great for upholding both theoretical and practical experience, but they will boost credibility and commitment to continuous learning. In other words, they act like the icing on the cake when applying for jobs.
A software engineer wishing to transition into a data science career, therefore, will have better job prospects should they take a Google Data Analytics Certification, which will validate their knowledge of Python, SQL, and machine learning.
Top Data Science Certifications
There are quite a few data science certifications to select from, based on your experience. Here are some of the best:
- Google Data Analytics Professional Certificate – Provides hands-on experience, and relishes top brand acknowledgment.
- Intellipaat Data Science Certification – It features real-world projects and offers personal mentorship for each student, career guidance, job assistance, and placement.
- IBM Data Science Professional Certificate – You will be constructing some hands-on machine learning projects as well as learning its fundamentals.
- Microsoft Certified: Azure Data Scientist Associate – Required in organizations using big data and cloud computing services.
- AWS Certified Machine Learning – Getting certification from AWS is pivotal for cloud specialists.
- Tensorflow Developer Certificate – With this certification, you will be able to build AI models in real time.
Starting your career in data science would mean completing an Intellipaat training in Data Science, a Google training in Data Analytics, or maybe an IBM training in Data Science. If you are considering a cloud specialization in AI, then go for Microsoft Azure or AWS ML certification.
Step 7: Solve Data Science Problems
While mastering technical skills, it is very critical to do hands-on practice. Projects are the best gateways to achieve that. It is recommended that you visit multiple data science platforms, such as Kaggle, UCI Machine Learning Repo, OpenML, etc. Get the datasets from there, understand the problem, and figure out how the solution can be approached.
This will provide you with the know-how of how exactly the data science process works, and how you can grab insights out of data, by using appropriate techniques.
This learning schema is your answer to how to become a data scientist. You need to learn the above technologies one by one. This could be a bit challenging, which is why professional certifications are significant if you’re someone getting started with data science from a beginner’s standpoint.
Solve Complex Problems with Data Science
with Our Proven Certification
Data Scientist Jobs Available Right Now
Right now, if you just Google and browse ‘data scientist jobs in India’, you will find out that just on LinkedIn, there are 93,000+ jobs available.
In the United States, the number is even better, 1,68,000 jobs.
And this is just LinkedIn. If you consider other job boards such as Naukri.com or Indeed, the numbers would be even larger. That is why you should understand how to become a Python developer and start your preparation right away!
Data Scientist Salary (India & USA)
Here’s a detailed reference of data scientist salaries in India and the USA, based on insights from different sources:
Data Scientist Salary in India
- According to Glassdoor, the salary of an entry-level data scientist in India lies between ₹6 lakh to ₹22 lakh.
- The average salary for data scientists is ₹14 lakh, and with more than 8 years of experience on the back, these professionals could easily earn above ₹30 lakh.
Data Scientist Salary in the USA
- According to Glassdoor, the salary of an entry-level data scientist in the USA lies between $100,000 and $200,000.
- The average pay for data science professionals is $156,000. Fortunately, as you get experience in this field, you can easily expect a salary above $250,000 as a senior data scientist.
Career in Data Science
As mentioned above, many data scientists begin their careers as data analysts and then enter the field of data science via job changes or an internal promotion. Once experienced, these professionals can then look for senior roles in this profession. More experts who have management skills can go for director-level and executive-level roles.
Here are a few ways one can stay active in the world of data science:
1. Follow Data Science groups and influencers
Joining Data Science groups is an effective way to stay updated and maintain relationships with fellow enthusiasts. Furthermore, attending meetups from time to time is also a great way to expand your network. Many professionals and big players have turned to social media to share Data Science-related know-how. Make sure to follow these accounts for regular updates.
2. Build a public portfolio of Data Science projects
As a beginner, you can start building a simple portfolio of interesting data science projects you have taken up and showcase them on platforms such as GitHub. Not only will you be creating a personal brand from scratch, but it will also ensure future growth in your career path.
3. Go for an Online Training Course and Earn a Certification
The most effective and the least costly way to boost your skills is by enrolling in courses that develop hot skills such as Python, R, Tableau, SQL, and Machine Learning.
4. Target employers
Every business, big or small, generates data. Not every company might be able to afford a full-sized team of data science experts. They need capable professionals who can do the work for them. Your job is to define your target employers and make a list of organizations that appeal to you. Once you start following them and stay updated over social media, you will get a clear picture of what you should be doing as a Data Scientist if you want to join their team.
Data Science at Work
Since you are clear about the demand and lucrative compensation data science offers, the only factor that needs to be addressed now is job satisfaction. This field of technology has created an immense impact. From healthcare and E-commerce to government service optimization, data science has revolutionized the entire process.
Here are certain examples of data science at work:
- Data Science in Healthcare: Deep learning algorithms detect and figure out medical conditions based on scans like MRI, X-ray, and CT scans. This removes human error and provides better detection of medical concerns.
- Data Science in E-Commerce: E-commerce giants use data science to determine customer lifetime value. The tool like recommendation engines, and actions like promotional emails and ad campaigns are all driven by the way customers behave on their websites.
- Internet Search: Search engines like Google, Bing, Safari, and Yahoo all use data science to comprehend how users behave and their search habits. Based on the results and interests, these search engines come up with content you may like.
Transitioning into Data Science from a Non-Technical Background
In case you are looking to switch to data science from a non-technical background you need to gather some essential skills, the career transition steps, and the successful strategies to break into this high-salaried field without even having a degree in computer science.
Can You Become a Data Scientist Without a Computer Science Degree?
Absolutely, yes! It is certainly not mandatory that you possess a degree in computer science (CS) or engineering to be a data scientist. At the same time, it might be interesting for you to know that many professionals from diverse non-tech areas such as finance, marketing, healthcare, HR, economics, business, etc., are now well-established as data scientists.
If you’re not from a technical background, you can responsibly follow some key steps to transition into data science:
- Navigate the World of Data Science
Before you dive in, you must be familiar with:
- The data science field (data wrangling, visualization, ML, AI)
- Roles within data science (Data Analyst, Machine Learning Engineer, Data Engineer)
- Applications in the industry (finance, healthcare, retail, e-commerce, HR analytics)
Learn and grow along with the leading players in the industry; stay aware of the latest developments in modern data analytics, be an active follower of blogs in data science, and watch videos of real-world applications on YouTube.
- Eliminate Mental Barriers
So many people think data science is about programming. Yes, indeed coding is very important, but equally important are problem-solving, business knowledge, and storytelling.
- Embrace data first thinking and know the ways in which organizations make use of the data to make their decisions.
- Understand how to model business problems as data-oriented questions.
- Master Fundamental Data Science Skills
Even if you are from a non-tech background, here is what is worth focusing on:
- Programming (Python & SQL)
- Python is the number one programming language for data science.
- SQL is required to deal with databases.
- Begin learning basic scripting/data manipulation (Pandas) and SQL queries.
- Data Analysis and Visualization
- Get a good grip on Excel, Pandas, Matplotlib, and Seaborn for data exploration.
- Learn Power BI or Tableau to present insights effectively.
- Statistics and Probability
- Make sure that terms like mean, median, standard deviation, correlation, and regression are well understood.
- Learn probability theory concerning prediction.
- Basics of Machine Learning
- Be trained in both supervised and unsupervised learning methods.
- Get well acquainted with linear regression, decision trees, and clustering techniques.
The next steps include working on real-world data science projects, building a portfolio to showcase your work, and earning relevant certifications. At the end of it all, apply for Data Science jobs even if you’re not 100% ready.
Begin with small projects, learn your way into Python and SQL, and then remember to showcase your work online. With proper mentorship and guided learning, transitioning into data science CAN be done successfully!
Get 100% Hike!
Master Most in Demand Skills Now!
Conclusion
The increase in demand for data science professionals is absolutely astounding. The world is significantly moving towards introducing artificial intelligence, machine learning, and big data in almost all businesses to make sound business decisions. Whether your background is technical or not, this guide will show you what it takes to become a data scientist.
The future of data science is no longer confined to data. It is rapidly becoming an integral service in industries such as finance, healthcare, retail, and cybersecurity, amid advancements in automation, cloud computing, and AI-enabled analytics. Thus, industries have adopted strategies based on data and require professionals to interpret complex datasets, build predictive models, and extract actionable insights.
Have you been searching for structured learning coupled with hands-on projects and job placement support? Then check out the Data Science Certification Course at Intellipaat.
FAQs
What is Needed to Become a Data Scientist?
If you wish to become a data scientist you will need strong analytical skills. Statistics and probability that you learned at the college level would help you build that. Then you need to learn how to work with large datasets, machine learning practices, and python/R programming framework.
How Long Does it Take to Become a Data Scientist?
If you have the analytical intuition, you just will have to master web scraping, programming languages like R or Python, data visualization, and machine learning. Which would generally take a year if you follow the right curriculum and learning plan.
Is It Hard to Become a Data Scientist?
It can be difficult if you don’t have an interest in learning multiple technical dependencies. Data Science requires both business acumen and an inherent ability to deal with numbers. If you love math and are interested in solving business problems, the willingness to learn data science.
What Should I Take After 12th to Become a Data Scientist?
There is no defined educational criteria. But preferably you can go for a degree in mathematical and computer science background. You can take on courses like BCA, B.Tech (Computer Science), B.Sc. (Mathematics and Statistics), B.Sc.(Data Science), or a professional certification to learn important skills, and to get a job as a data scientist.
What is the Monthly Salary of a Data Scientist?
The average monthly data scientist salary in India ranges between ₹60,000 to ₹2,41,000, whereas in the USA it lies between US$20,000 to US$38,000 as per the report from Glassdoor.