Data Science & Machine Learning Course for Your Careers
Data science is about using data to solve real problems, not just writing code. Today, every business relies on data to understand customers, predict trends, and make better decisions. This data science & machine learning course is designed to teach you how that work is actually done in the real world. In this course, you’ll work with real datasets, learn how to clean and analyse data, and build machine learning models using Python. Concepts are explained in a simple, practical way, so even beginners can follow along. By the end of the course, you’ll have hands-on skills you can use in projects and interviews, whether you’re aiming for a role as a data analyst, data scientist, or machine learning professional.
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Name
Phone Number
How you find us ?
Current profile
Duration
4 Months
Mode
Online & Offline
Projects
5+ Industry-Grade Projects
Support
Lifetime
Duration
4 Months
Mode
Online & Offline
Projects
5+ Industry-Grade Projects
Support
Lifetime
Where Our Graduates Shine
Our students earn placements with top companies that value skill, dedication, and job-ready talent, showing the strength of our training
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Why choose Code Purple - Vellore
At Code Purple Academy, you learn by doing practical classes, instant doubt clearing, and real projects that prepare you for real data and interviews.
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Resume building that cracks ATS software
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Training by industry experts
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5hands-on projects
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Mock interviews & Group discussions
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HackerRank profile development
Santhosh
Full Stack Developer
Coimbatore, India
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LinkedIn profile
optimization
Take the first step towards your dream IT career in Vellore. Join Code Purple Academy’s Data Science Course in Vellore today!
Daily Life Clones:
Recreating Everyday Apps
for Practical Learning
Your Personal Learning Hub
Explore your own student portal — track progress, practice daily, stay organized, and manage your entire learning journey in one place.
Course Progress
Track every module, lesson, and milestone with clear
visual progress indicators.
Performance & Support
View your score dashboard, track
improvement, and raise grievances
for quick support.
Practice Zone
Sharpen your skills with coding challenges, MCQs, and regular practice tests.
Support Beyond the Classroom
Tools Covered
This section gives a quick overview of the key tools used during the data science training. These tools help students work with data, build models, visualize insights, and complete real-world projects.
Machine Learning
Tableau
TensorFlow
Python
Scikit-Learn
MySQL
Launch Your
Data Science Career Today
Learn Data Science and Machine Learning with Python from basics using real datasets, practical projects, and beginner-friendly explanations
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Data Science Course
Explore our Data Science course: practical skills, real-world projects, and job-ready training.
In Partnership with
Working with top colleges to provide essential full stack development training for student success.
ANJAC, Sivakasi
SFRC, Sivakasi
PSG, Coimbatore
In Partnership with
Working with top colleges to provide essential full stack development training for student success.
ANJAC, Sivakasi
SFRC, Sivakasi
PSG, Coimbatore
Syllabus Overview
Modern syllabus with practical learning and interview preparation to make you job-ready.
Module 1: Introduction to Data Science
Learn the role of a Data Scientist and the significance of data in today's world.
Module 2: Excel
Excel Basics:
- Workbooks, Worksheets, Cells, and Ranges.
- Basic functions: SUM, AVERAGE, COUNT, MIN, MAX.
- Sorting, Filtering, and Formatting Data.
Intermediate Excel:
- Conditional Formatting, Data Validation.
- Lookup Functions: VLOOKUP, HLOOKUP, INDEX, MATCH.
- Working with Dates and Time functions
- Basic Charts and Graphs (Bar, Line, Pie)
Module 3: Python
- Python syntax: Variables, Data Types (int, float, string)
- Data Structures and Methods
- Control flow: if-else, for loops, while loops.
- Functions
- Basic OOPs
- Advanced OOPs
- File and Error Handling
- Modules, Packages and Libraries
Module 4: SQL
- Introduction to relational databases and SQL.
- Basic SQL commands: SELECT, FROM, WHERE, ORDER BY.
- Filtering Data with AND, OR, NOT, and comparison operators.
- Aggregation Functions: COUNT(), AVG(), SUM(), MIN(), MAX().
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN.
- Subqueries: SELECT within SELECT.
- GROUP BY and HAVING: Aggregating data by groups.
- Advanced Filtering: Working with LIKE, IN, and wildcards.
- Data Cleaning: Handling NULL values and duplicates.
Module 5: Tableau
- Installing Tableau and connecting to data sources (Excel, SQL, etc.).
- Data Preparation in Tableau: Cleaning, filtering, and transforming data.
- Basic Visualization Types: Bar charts, line charts, scatter plots, and pie charts.
- Formatting and customizing visualizations (titles, labels, colors, etc.).
- Calculations in Tableau: Basic calculated fields (e.g., profit margins, growth rates).
- Table Calculations: Running totals, percent of total, moving averages.
- Building Interactive Dashboards: Filters, parameters, and dynamic actions.
- Storytelling with Data: Creating data-driven narratives and dashboards.
Module 6: Power BI
- Installing Power BI and connecting to data sources (Excel, SQL, web, APIs).
- Power Query for Data Transformation: Cleaning and reshaping data before analysis.
- Basic Visualization Types in Power BI: Bar, line, pie charts, maps.
- Introduction to DAX (Data Analysis Expressions): Basic calculations (e.g., sum, average, and count).
- Data Modeling: Relationships between tables, star schema, fact and dimension tables.
- Advanced DAX: Calculated columns, measures, and time intelligence functions.
- Building and customizing Dashboards: Filters, slicers, drill-through actions.
- Publishing and Sharing Reports: Export to PDF, share via Power BI Service.
Module 7: Statistic & Probability
Covers Central Tendency measures, variance, standard deviation, probability, key distributions, hypothesis testing, sampling, and A/B testing for data-driven decisions.
Module 8: Machine Learning
Introduces regression, classification, decision trees, random forests, clustering, PCA, ensemble methods like XGBoost, and hyperparameter tuning.
Module 9: Deep Learning & NLP
Teaches neural networks, CNNs, RNNs/LSTMs, training techniques, and NLP concepts including tokenization, embeddings, text classification.
Module 10: Capstone Project and Portfolio Development
Capstone Project: Choose a real-world dataset (e.g., sales, marketing, or customer data)
- Includes dashboards, prediction models, sentiment analysis, image classification, and recommendation systems to build industry-ready practical skills.
Portfolio Creation: Upload your projects to GitHub, LinkedIn, or a personal website.
- Write clear documentation for each project, explaining your process and insights.
- Highlight the tools and techniques used in each project.
Discover What Makes Us Unique
Key Features That Set Us Apart from the Rest
Leading Professionals
Hands-On Training
Career Guidance
Success stories from Our Students
At Code Purple Academy, beginners start unsure but grow through real projects, cleaning data, analysing customers, and building simple ML models with clear guidance.
Dharanya Selvaraj
Accenture
In the Information Technology field, staying updated with current skills is crucial, and Code Purple Academy ensured I was well-prepared.Read More
Shanjana Suresh
Billion Face
Code Purple Academy helped me learn new skills and coding languages while also giving me valuable insight into the IT sector.Read More
Sri Hari
Coastal Aquaculture Research Institute Pvt. Ltd
Joining Code Purple Academy has been a game-changer for me. I was able to stay updated with the latest trends in the development field.Read More
Kishore
Billion Face
The Full Stack Development course at Code Purple Academy provided me with a solid foundation in both front-end and back-end technologies.Read More
Subash
ResultueTechno
Code Purple Academy provided exceptional mentor support, daily tasks, and updates, which kept me on track throughout my learning journey.Read More
Sai
TCS
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Coming from an ECE background, Code Purple Academy helped me transition into coding, making me well-versed in Read More
Siva
Renault Nissan Technology & Business Centre India
Code Purple Academy played a significant role in securing my job at Renault Nissan Technology & Business Centre India.Read More
Andrea Lincy
Haashtag HSI Pvt Ltd
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Code Purple Academy played a key role in helping me secure my position at Haashtag HSI Pvt Ltd.Read More
S. Mano Priya
Brand Mindz Global Technology
I’m truly grateful for the valuable learning experience I gained through the Front-End & UI/UX Design course at Code Purple Academy.Read More
Amuthapriya K
Slash Lab
My journey with the Code Purple Full Stack Development course was very useful and confidence-building.Read More
Abiilesh
Quest global
Code Purple Academy’s Data Analytics program helped me build strong analytical skills and understand how to work with real-world data.Read More
S.Nandhakumar
Gamantic
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The Full Stack Development course at Code Purple Academy helped me understand how modern websites are built from start to finish.Read More
Harish T
Ascend Technologies
My experience with the Code Purple Full Stack with Python course was highly beneficial. The training helped me understand Read More
Sathya Sri
Stutzen Technologies
My experience with the Code Purple Full Stack with Python course was highly beneficial. The training helped me understand Read More
FAQ's
The details below are designed to give you quick and simple guidance
1. What is data science and machine learning?
Machine learning is what happens when we stop spoon-feeding instructions to the computer and instead allow it to learn from experience, just like you do. If you solve one math problem, you might forget it, but if you solve a hundred similar ones, patterns start making sense, and in the same way, when a system looks at years of past data – like customer behaviour, exam results, or price trends - it slowly learns how to make better guesses on its own.
In real life, these two work together everywhere, from your phone unlocking with your face to apps suggesting routes that avoid traffic, and in this course, the focus is not on memorizing fancy terms but on helping you understand how these ideas actually work, why they matter, and how you can use them confidently to solve problems you already see around you every day.
2. Who should take this course?
It’s also very useful for working professionals who already deal with Excel sheets, monthly reports, targets, or customer numbers and feel they’re just following instructions without really understanding what the data is saying, because once you learn how to read patterns properly, even routine work starts making a lot more sense.
And don’t worry if you’re from a non-technical background or switching careers after a break, because this course is built to guide you step by step, using examples you see around you every day, so as long as you’re willing to learn and practice, you’ll slowly build the confidence to work with data comfortably.
3. What skills will I learn in this data science course?
You’ll spend a lot of time working with Python in a hands-on way, learning how to explore data, create simple graphs that actually explain something, and answer everyday questions such as why a product isn’t selling well or what factors affect student performance, rather than just memorizing commands or copying code from the internet.
By the end of the course, you’ll know how machines learn from past data, how to check whether the results you’re getting can be trusted, and how to explain your findings clearly in normal language, so you can confidently share insights with teachers, managers, or teammates without hiding behind technical jargon.
4. What are the prerequisites for enrolling in a data science & machine learning course?
If you have basic school-level math knowledge, things like percentages, averages, and simple charts that you already use while checking exam results, electricity bills, or monthly expenses, you are absolutely fine, and even if these concepts feel a little rusty, they are explained again using everyday examples that make them easy to understand.
More than any qualification or background, what really matters here is your willingness to learn and practice regularly, because students who stay curious, ask questions, and put in steady effort whether they come from engineering, commerce, arts, or any other field tend to pick up these skills comfortably and grow with confidence over time.
5. Is this course theoretical or practical?
There is some theory, but it’s explained in a simple way and only when it’s needed, like understanding basic traffic rules before riding a bike on a busy road, and every concept is followed by examples you can relate to, such as looking at shop sales, exam results, or simple trends you see around you.
By the time you move forward in the course, you’ll realize most of your learning is coming from doing the work yourself, solving problems, working on small projects, and practising regularly, so instead of memorizing definitions, you’ll actually know how to use these skills confidently in real situations.
6. What are the eligibility criteria for joining the course?
Your background doesn’t have to be technical at all, because students from science, commerce, arts, diploma, or even completely different fields join this course, and as long as you’re comfortable with simple maths like percentages and averages and can use a computer for basic daily work, you are good to go.
What really matters more than degrees or marks is your interest in learning and your willingness to practice, because students who stay curious, ask questions, and put in regular effort usually pick things up well and gain confidence, no matter where they start from.
7. Will I get a certificate after completing the course?
Many students use this certificate while applying for jobs or internships or even for showing skill upgrades in their current workplace, and since you’ll have worked on real problems during the course, you won’t feel awkward explaining what you learned when someone asks about it.
The real value, though, is not the paper itself, but the confidence that comes from finishing the course properly, because by then you’ll have real work to talk about and the clarity to explain your skills honestly and comfortably.
8. What career opportunities are available after this course?
Many learners start working in departments like marketing, finance, operations, or product teams, where they look at things like customer behavior, monthly sales, or performance reports and help answer practical questions such as why a campaign worked, why sales slowed down, or which product is doing better in a particular area.
As you gain more hands-on experience, these skills can naturally grow into higher roles related to analytics or machine learning, giving you the freedom to switch industries, take on more meaningful work, and build a career that keeps growing instead of feeling stuck in repetitive tasks.
9. Is the Data Science & Machine Learning course available online?
The sessions are taken live, so you’re not just watching a screen quietly; you can ask questions, see problems being solved in real time, and follow along as concepts are explained slowly and clearly, just like in a physical classroom.
On top of that, you get recordings of the classes, which really helps when you want to revise a topic, clear a doubt again, or catch up on a session you missed, allowing you to learn comfortably at your own pace.
10. How can I enroll in the Data Science course?
After that, you’ll be guided properly, where your doubts about the course, class timings, and batch options are cleared, and you can choose a schedule that fits your daily routine, whether you’re studying, working, or managing both.
Once everything is confirmed, you’ll get access to the classes and study materials, and from there you can focus completely on learning and building skills, without stressing about paperwork or formal processes.
