CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org
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Your data science career begins
Data scientists collect, clean, and model data to uncover insights that guide decision-making. The role sits at the intersection of analytics, machine learning, and AI—making it one of the most versatile careers in tech.
With AI jobs on the rise, is data science still a good career?
Far from being replaced by AI, data science is the foundation that powers it. Every model and algorithm is based on the same principles that data scientists apply in their daily work.
Our data scientist career track equips you with everything you need for a successful career in AI and data science—from math and statistics to Python, SQL, and machine learning.
Curriculum
Projects
Certificates
Student outcomes
Careers
More career paths
Overview
Breaking into data science can feel overwhelming, but this structured data science roadmap shows you exactly what to learn and how to apply it.
Each of the data science courses in the curriculum builds toward the skills hiring managers look for:
Mathematics, statistics, and probability
Python programming
SQL
Machine learning
Deep learning
Product management for AI and data science
Career preparation
By the time you complete this data science program, you won’t just “know” data science—you’ll have applied it through projects that mirror real business challenges. And with an accredited data science certification in hand, you’ll have proof that your skills meet industry standards.
Whether your future is in analytics, machine learning, or AI, data science is the foundation. We’ll help you master it, apply it, and turn it into a future-proof career.
Curriculum
CPE credits
ONLINE COURSE
Introduction to Data and Data Science
Gain a clear understanding of data science, including core concepts, tools, and techniques. Learn about data science roles and how to choose the best data scientist career path for you.
Master core statistics concepts, apply hypothesis testing, and develop fundamental data science and analytics skills needed to interpret data and build reliable, data-driven models.
Learn the basics of probability, combinatorics, Bayesian inference, discrete and continuous distributions, and their applications in data science jobs, statistics, and finance.
Learn Python from the ground up—from coding basics, functions, loops, and data structures to strings and expressions—essential skills for data analysis, machine learning, and data science projects.
Strengthen your math foundation by mastering calculus, linear algebra, matrices, and tensors—essential skills that underpin data science, machine learning, and AI models.
Gain the database skills every data scientist needs to retrieve, organize, and manipulate data effectively. Master SQL fundamentals from best practices to real-world applications.
Learn machine learning in Python, train and evaluate models, and master regression, classification, and clustering to solve real-world problems as a data scientist.
Gain a clear understanding of data science, including core concepts, tools, and techniques. Learn about data science roles and how to choose the best data scientist career path for you.
Master core statistics concepts, apply hypothesis testing, and develop fundamental data science and analytics skills needed to interpret data and build reliable, data-driven models.
Learn the basics of probability, combinatorics, Bayesian inference, discrete and continuous distributions, and their applications in data science jobs, statistics, and finance.
Learn Python from the ground up—from coding basics, functions, loops, and data structures to strings and expressions—essential skills for data analysis, machine learning, and data science projects.
Strengthen your math foundation by mastering calculus, linear algebra, matrices, and tensors—essential skills that underpin data science, machine learning, and AI models.
Gain the database skills every data scientist needs to retrieve, organize, and manipulate data effectively. Master SQL fundamentals from best practices to real-world applications.
Learn machine learning in Python, train and evaluate models, and master regression, classification, and clustering to solve real-world problems as a data scientist.
A Data Scientist career track with REAL AI projects
beginner
free
Customer Engagement Analysis in Excel Project
Duration: 2 hours
Case brief
Measure platform feature impact on student engagement using Excel. Compare activity across two quarters to assess if gamification and content expansion drove greater engagement using statistical hypothesis testing.
Core tasks
Summarize engagement with descriptive statistics
Calculate confidence intervals for comparison
Perform hypothesis testing to assess impact
Analyze paid vs. free user behavior across time
Topics covered
Descriptive & Inferential Statistics
Engagement Metrics Analysis
Go to project
beginner
free
Calculating Free-to-Paid Conversion Rate with SQL Project
Duration: 2 hours
Case brief
Use SQL to analyze real platform data. By joining and aggregating data from multiple tables, you'll practice key SQL techniques while uncovering insights into user behavior and engagement on the 365 platform.
Core tasks
Join tables to track user registration, engagement, and purchases
Create subqueries to isolate engaged users
Write main queries to calculate conversion metrics
Interpret and analyze the results in context
Topics covered
SQL Queries
Aggregation
Conversion Rate Analysis
Go to project
intermediate
Encryption and Decryption in Python Project
Duration: 8 hours
Case brief
Implement a columnar transposition cipher in Python. Build a class that can encrypt and decrypt messages based on a user-defined key. You'll gain hands-on experience with string manipulation, algorithmic thinking, and optional cipher-breaking.
Core tasks
Build a class to handle encryption and decryption
Implement logic for the columnar transposition cipher
Test encryption with different keys and messages
(Optional) Create a function to crack the cipher without a key
Topics covered
Cryptography Fundamentals
Class-Based Programming
Go to project
advanced
Real Estate Market Analysis with Python Project
Duration: 18 hours
Case brief
Analyze real property and customer data to uncover trends in the real estate market. Clean datasets containing transaction and buyer information to understand market activity, customer profiles, and sales behavior.
Core tasks
Preprocess property and customer datasets
Generate descriptive statistics and visual insights
Identify trends in sales and buyer behavior
Interpret findings to support real estate strategy
Topics covered
Real Estate Data Analysis
Customer Profiling
Market Trends
Go to project
advanced
User Journey Analysis in Python Project
Duration: 13 hours
Case brief
Analyze real user behavior on a subscription-based learning platform by working with journey strings. Create custom Python tools to process and extract meaningful metrics from these patterns, helping identify what drives conversions and how users interact with the platform.
Core tasks
Preprocess and structure raw user journey data
Build Python functions to analyze visit patterns
Extract metrics to identify high-conversion paths
Draw insights to support business and marketing decisions
Topics covered
Behavioral Data Analysis
Python Functions
Data Preprocessing
Go to project
advanced
Building Conway's Game of Life in Python Project
Duration: 15 hours
Case brief
Simulate the iconic Game of Life using Python to explore how simple rules can lead to complex behavior. Implement a cellular automaton that evolves over time based on neighbor interactions. It's ideal for learners looking to challenge their Python skills.
Core tasks
Build and populate a 2D grid of cells
Implement the Game of Life evolution rules
Create visual outputs to simulate each step
Document your code and build modular methods
Topics covered
Object-Oriented Programming
Grid-Based Logic
Simulation
Go to project
We award accredited Data Scientist certification
Receive an accredited data science certificate—officially recognized by leading organizations as proof that your skills align with industry standards.
Accredited by the Association of Data Scientists (ADaSci)
Accredited as an eLearning Quality Network provider (ELQN)
Quality accreditation granted from the European Agency for Higher Education & Accreditation (EAHEA)
Approved CPE* provider under NASBA—our AI bootcamp qualifies for continuing education credit
Reviewed by the Institute of Analytics (IoA)
Member of the Global Association of Online Trainers and Examiners (GAOTE)
*Note: CPE credits are reflected per course in your official transcript, in line with accreditation requirements Learn more
Where our Data Scientist career path takes you
"365 Data Science had everything I was looking for—from BI tools and visuals to advanced ML frameworks. The constantly updated content kept me current, while hands-on projects strengthened my portfolio. The interview simulator gave me realistic practice for job applications, helping me feel prepared and confident. 365 doesn't just teach theory—it equips you for the market. The knowledge and practical skills I gained were key to landing my current role as a data scientist."
Daniel T.
After 365:
Data Scientist at CKDelta
"Before enrolling, I felt lost and unsure where to begin. Now, I feel well-equipped to tackle the challenges of the business world. I added all 138+ certificates achieved to my LinkedIn profile and shared the platforms' uniqueness to my connections, including my students in universities. The curriculum covers everything from basics to advanced techniques. I highly recommend 365 to anyone looking to start or advance their career in finance."
Chanh Truc T.
Before 365:
Lecturer at HUTECH
After 365:
Data Scientist at TranSwap
"I come from a background in electrical and electronic engineering, as well as cybersecurity. But I wanted to transition into machine learning and AI. After researching different platforms, I chose 365 Data Science. I loved the courses "Customer Analytics" and "Time Series". Today, I'm working as a Data Scientist at an EV startup. 365 Data Science played a major role in helping me get here."
James G.
Before 365:
Technical Engineer at GI Energy
After 365:
Data Scientist at ClearWatt
"As a Data Science student, I feared graduating without the practical skills to succeed. 365 Data Science gave me exactly what I needed—projects, career tracks, and in-depth courses in Python, R, SQL, Excel, Power BI, Tableau, and more. I applied these skills to university projects, built advanced solutions like chatbots, and understood the big picture. The structured learning, real-world applications, and interview preparation boosted my confidence, helping me become the top student in my class and ready for the industry."
Most data scientist roles list technical education as a preferred background. Our analysis shows that degrees in data science (70%), statistics (55%), computer science (55%), and engineering (40%) appear most often in job postings. But employers are open to candidates who’ve built their skills through alternative paths, mainly when supported by practical experience.
You don’t need a specialized degree to start a data scientist career path.
Nearly 20% of postings don’t mention a specific degree. What employers value most is evidence of skills: hands-on projects, a solid portfolio, and a recognized data science certificate. With structured training like the 365 Data Scientist Career Track, you can build that credibility and start applying without the need to return to university. Enroll to take the first step today.
Step 2
Skills
Data scientists today need versatile technical skills. According to our research of over 1,000 job postings, 77% of roles request machine learning expertise, followed by Python (85%), SQL (58%), and R (46%) as core tools. Employers also value data visualization (22%) and statistical analysis (18%), as well as experience in cloud and data engineering, such as AWS (27%), Apache Spark (19%), Azure (16%), and big data skills (16%).
You don’t need to master every tool—but flexibility across methods gives you a significant edge.
Build a portfolio of real-world projects—training models, cleaning messy datasets, and designing dashboards—and share your work publicly. Demonstrated results like these carry far more weight than keywords on a resume. Our Data Scientist Career Track emphasizes hands-on skills that enable you to present your capabilities to employers. Join our data scientist training today.
Step 3
Branding
Your personal brand often speaks louder than your first interview. A strong LinkedIn profile, a polished resume, and a clear project portfolio are critical for data scientists. Sharing code on GitHub and presenting your data science certification from an accredited provider signals to employers that you’re serious and job-ready.
CPE credits add a layer of trust and demonstrate ongoing professional development.
Use our resume builder and cover letter template to highlight your achievements. Profiles with credentials from trusted data science certificate programs, such as 365 Data Science, often rank higher in recruiter searches. And by showcasing projects that solve real business problems—like predictive modeling, customer segmentation, or fraud detection—you’ll demonstrate the kind of impact employers are hiring data scientists to deliver. Enroll now
Step 4
Job Search
The data scientist job market is growing at 36%, but with only 4% of positions being entry-level—meaning that you need to work hard to land an offer. Beyond learning skills, you can practice with mock interviews, refine your project presentation, and learn to communicate like a data scientist. Tools like our AI-powered interview simulator and monthly job insider newsletter give you an edge—helping you anticipate questions, discover hidden opportunities, and stand out from other candidates.
The average data scientist salary in 2025 is $152K—proof that the effort pays off.
The proper support makes all the difference. Career mentors and a community of peers keep you accountable, motivated, and moving forward—so when it’s time to apply, you’re ready and confident. With guidance from industry experts, you’ll never feel alone. Follow the data scientist career path.
Is a data scientist career the right move for you? Data science is a powerful gateway to numerous AI and data roles—like data analyst, machine learning scientist, or AI engineer. But which path best fits your strengths?
Our free career quiz reveals where you’re most likely to thrive. In under 5 minutes, you’ll discover which data science job matches your skills, interests, and goals.
The typical data scientist job description includes collecting, cleaning, and analyzing large datasets, building machine learning models, and communicating insights that support business decisions. Other core data scientist job responsibilities involve designing experiments, evaluating results, and ensuring data is reliable and actionable. Because the role spans both technical and strategic work, it opens doors to a wide range of opportunities in analytics, machine learning, and even AI engineering. If you want to become a data scientist, hands-on projects and a recognized certification can help you prove your skills to employers.
How long does it take to become a data scientist?
The timeline depends on your starting point, but most people can expect to spend 6 to 12 months building the skills needed for an entry-level role if they follow a structured data scientist career path. This usually includes learning programming, statistics, machine learning, and SQL while applying those skills in real projects. Earning a data science certification can accelerate the process by giving you a clear roadmap and proof of competence. With consistent effort, it?s possible to transition into the field without needing years of retraining.
How do I become a data scientist without a degree?
A traditional diploma isn’t the only way into data science. Our analysis of 1,000+ job postings shows that nearly 20% of employers don’t require a degree. What they look for instead is proof that you can do the work—coding in Python, R, and SQL, applying statistics, and building machine learning models. You can achieve this by following a structured data scientist roadmap that teaches the required skills and how to showcase them. By completing projects, earning an accredited data science certificate online, and building an outstanding portfolio, you can demonstrate job readiness. Many of our learners have broken into data science from backgrounds as diverse as teaching, finance, and engineering—without ever going back to university.
How can I become a data scientist with no experience?
Landing a first role in data science can feel daunting, but it’s possible—even without prior job experience. Research shows that only 4% of data scientist jobs are entry-level—meaning you’ll need to stand out by proving you can already do the work. The most effective path is to learn data science through courses that combine theory with hands-on projects. By following a structured data science career path, you’ll build skills in Python, SQL, statistics, and machine learning while applying them to real datasets. Adding an accredited data science certification to your resume also signals credibility to hiring managers. What bridges the gap from “no experience” to job-ready is your portfolio. Projects like predictive modeling, churn analysis, or building a chatbot show employers you can solve practical problems. Combine that with strong personal branding—on LinkedIn and GitHub—and you can break into the field even as a beginner.
What is the career path of a data scientist?
The data scientist career path typically starts with an entry-level role?often as a data analyst, junior data scientist, or business intelligence specialist. From there, professionals advance to mid-level data scientist jobs, leading projects that involve predictive modeling, experimentation, and machine learning. With experience, many progress into senior data science roles or transition to positions like machine learning scientist or AI engineer. This means your career path can evolve in multiple directions?from technical specialization to leadership.
What is the career path to become a data scientist?
If you?re wondering how to get into data science, the typical path combines technical training, project experience, and career preparation. Start by mastering the fundamentals?Python, SQL, statistics, and machine learning?through structured data scientist courses or certificate programs. Next, you?ll build a portfolio of real-world projects, which shows employers you can apply your skills beyond the classroom. Earning recognized data science certifications adds credibility to your profile, especially if you don?t have a traditional degree. From there, many begin as data analysts or junior data scientists before moving into more advanced roles in AI, machine learning, or leadership.
Will data science be dead in 10 years?
No?far from it. While automation and AI tools are changing the field, data science and analytics remain the foundation of how those systems work. Every AI model depends on the same principles data scientists apply daily: cleaning data, designing experiments, and building models that can be trusted. The numbers back this up. The data scientist job outlook shows 36% projected growth?much faster than the average for other careers. That means that not only is data science not ?dying,? but the demand for skilled professionals is increasing. If anything, the role is evolving, opening up even more opportunities for those who enter the field today.
What is the salary of a data scientist career path?
Data science is one of the most lucrative fields in tech. On average, a junior data scientist earns about $88,000 a year, while mid-level professionals make between $128,000 and $154,560. As you advance, senior data scientist salaries range from $158,000 to $182,905. Those who move into leadership roles, such as data scientist managers, can earn over $200,000, with top salaries reaching $212,987. The data scientist career path offers clear upward mobility?as your skills and responsibilities grow, so does your earning potential. With a projected 36% job growth rate, this path combines strong demand with some of the highest salaries in the job market.
Is it hard to become a data scientist?
Breaking into data science isn?t easy, but it?s achievable with the proper structure. The challenge comes from the breadth of skills required: Python, SQL, statistics, machine learning, data visualization, and even cloud and data engineering skills. Instead of piecing together scattered tutorials, structured data science training shows you what to learn and in what order. Many learners start from non-technical backgrounds and succeed by following a guided learning path, applying their knowledge in projects, and earning an accredited certification to prove they?re job-ready.
Which certification is best for data science?
The best data science certifications are those that reflect what the job market demands and prepare you for long-term growth. It?s not enough to earn a certificate?you need a program that proves you can apply your skills to real problems. That?s why the 365 Data Scientist Career Track goes beyond theory. You?ll follow a structured learning path?covering Python, SQL, statistics, and machine learning step by step?while building portfolio projects with real datasets. By the end, you?ll graduate as a certified data scientist with accredited credentials recognized by leading institutions?and the portfolio to back them up. For those targeting advanced data science careers, additional programs like DASCA?s SDS or PDS, the SAS Certified Data Scientist, or cloud-focused certifications (Google or Microsoft) can provide valuable specialization. But across the board, employers want more than paper credentials?they want job-ready professionals who can deliver real impact.
What can you do with a data science degree?
A degree in data science opens doors to a wide range of careers. Typical jobs you can do with a data science degree include data analyst, business analyst, data scientist, and machine learning engineer, with opportunities to grow into senior roles like data science manager or AI specialist. Because data is used in every industry (finance, healthcare, tech, retail, and more), your career options are flexible. A data science degree provides a strong foundation, but pairing it with practical experience, portfolio projects, and advanced data science certification makes you even more competitive in the job market.
Is 30 too late for data science?
No, 30 years old is an excellent time to become a data scientist. Many people transition into the field in their late 20s or 30s, often leveraging experience from other careers. Since most data science jobs are mid-level rather than entry-level, being a little older can actually work in your favor?you bring maturity, context, and domain expertise that younger candidates often lack. With the proper data science training and a portfolio of projects, you can confidently show employers you?re ready for the role. Age isn?t a barrier?proof of skills is.
Is 40 too old to become a data scientist?
Not at all. Many professionals switch careers into data science in their 30s, 40s, or even later, bringing valuable experience from fields like business, finance, or engineering. If you?re 40 and wondering how to become a data scientist, focus on building practical skills and showcasing them. Completing portfolio projects, following a clear data scientist career path, and earning an accredited data science certification signals to employers that you?re prepared for the job?no matter your background.
