Professional Certificate in
Data Science & AI
- 4.5/5 (1500+ Student's Ratings)
Backed by leading industry experts, ScaleUp’s Post Graduate Program in Data Science & AI delivers real-world knowledge and practical experience to help you build a future-ready career in one of the world’s fastest-growing domains.
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Next Cohort
This Week
Duration
12 Months . Online
06 Months
Live Internship
Eligibility
Freshers, Experienced
What You’ll Get with the ScaleUp Experience
Our Post Graduate Program in Data Science & AI is designed to give you more than just knowledge—it gives you the edge to thrive in the real world. With hands-on learning, expert guidance, and career-focused support, here’s everything that comes with the program:
- 1:1 Doubt Clearing Session
- Live Hackathons
- Lifetime Career Support
- Webinars And Workshops
- Extensive Course Materials
- 250+ Hours of Self Paced Learning
- 400+ Hours of Live classes
- Industry Expert Mentors
- 360° Placement Assistance
- 24/7 Student Support
- Personalized Learning Paths
- Regular Assessments
- Progress Tracking
- Networking Opportunities
- Flexible Learning Schedules
- Strong Alumni Network
- Interview Preparation
- Certification Programs
- Interactive Learning Platform
- Real World Projects
What You’ll Learn
This program helps you go beyond just crunching numbers—you’ll learn how to use data to make smarter decisions and solve real problems. You’ll explore how top companies use data to drive growth, and you’ll build skills in:
Analyzing and visualizing data
Drawing clear insights from complex datasets
Making data-backed business decisions
Communicating your findings with confidence
Languages and Tools Covered
Course Curriculum
Our curriculum is designed to make you job-ready from day one. You’ll learn essential tools like Python, SQL, and Excel—skills every data professional needs. From writing clean code and automating tasks to mastering pivots, lookups, and queries, you’ll gain the confidence to handle real data challenges. We’ll also cover key statistical concepts like regression, correlation, distributions, and eigenvalues, helping you not just understand the math, but apply it to real-world problems. Every concept is tied to practical business use—so you’re not just learning, you’re preparing to perform.
- Modules
Excel
- Arithmetic Operators, Sort & Filter, Statistical and Mathematical Functions, Conditional Formatting
- Lookup, Index & Match, Logical, Text Functions, Pivot
Tables - Data Cleaning, What if analysis, Scenario Management.
- Charts, Dashboards, Regression, and Forecasting
Power BI
- Power BI Installation + Introduction to Data Cleaning and
Preparation using Power BI - Introduction to Feature Engineering and Data Modelling in
Power BI. - Introduction to Charts, Maps, and Dashboards in Power BI.
Tableau
- Tableau Installation + Introduction to Tableau, Charts and Maps, Fundamentals of Data Visualization and Reporting.
- Introduction to Calculated Fields, Table Calculations, Aggregations, Granularity and LOD Expressions
- Introduction to Data Extracts, Filters, Tableau Dashboards, Tableau Storyboards, and Formatting.
SQL Course: MySQL, NoSQL, & CQL
- Database Fundamentals, DDL, DML, DQL, CQL Queries.
- SQL Joins, Sub-Queries, Set Operations, and Writing Complex Queries.
- Accessing and Loading Databases and Performing Query Analysis in Python.
- Fundamentals of MongoDB: Documents and Collections.
- Introduction to MongoDB Replica Sets, Sharding, and Indexes.
- CQL (Cassandra Query Language) Fundamentals, Keyspaces, and Tables.
- Advanced CQL Queries, Indexes, and Materialized Views in Cassandra.
Python
- Python Installation + Variables, Operators, Strings, Datatypes, and Data Structures such as Lists, Tuples, Dictionaries, and Sets.
- Functions, Parameters, Arguments, Anonymous Functions, Strings, String methods, Regular Expressions
- Introduction to Loops, Conditionals, Break, Continue, Object Oriented Concepts, and Space-Time Complexity.
Statistics and Hypothesis Testing
- Python Installation + Variables, Operators, Strings, Datatypes, and Data Structures such as Lists, Tuples, Dictionaries, and Sets.
- Functions, Parameters, Arguments, Anonymous Functions, Strings, String methods, Regular Expressions
- Introduction to Loops, Conditionals, Break, Continue, Object Oriented Concepts, and Space-Time Complexity.
Data Analysis and Visualization
- Introduction to Numpy and Pandas
- Introduction to Matplotlib and Seaborn
Data Cleaning, Preparation, Processing
- Dealing with Missing Values, Dealing with Outliers and Skewness, and Encoding Categorical Data
- Introduction to Data Manipulation Functions, Statistical Transformations, and Feature Engineering
- Introduction to Sampling and Resampling Techniques, Introduction to Feature Scaling Techniques.
Machine Learning
- Introduction to Linear and Logistic Regression.
- Introduction to KNN, SVM, and Naive Bayes Theorem.
- Introduction to Decision Trees and Random Forests.
- Introduction to Boosting Algorithms, and Imbalanced
Introduction to Unsupervised Learning
- Introduction and Implementation of K Means and
Hierarchical Clustering. - Introduction and Implementation of PCA and LDA.
Time Series and Recommender Systems
- Time Series Fundamentals, AR, MA, ARMA, ARIMA, SARIMA,
ARIMAX etc. - Content & Collaborative based Filtering.
Introduction to Model Deployment
- Overview of Model Deployment , ML System Architecture,
Packaging ML Model for Production - Serving and Deploying the Model via REST API, Continuous
Integrations, and Deployment Pipelines - Deploying ML API with Containers, Differential Testing,
Deploying to IaaS (AWS EC2).
Natural Language Processing(NLP)
- Fundamentals of Natural Language Processing, Part of
Speech Tagging, Named Entity Recognition - Introduction to Text Classification, Semantics Rule, andFundamentals of Sentimental Analysis.
- Understanding the Complex concepts of Topic Modeling
and Text Summarization Techniques.
Deep Learning
- Introduction to Artificial Neural Networks
- Introduction to Convolutional Neural, Networks and CNN
Architectures.
- Internship Program
- Web Scraping from different websites
- Movement Detection with Computer vision
- Model Deployment in Cloud Services
- Data cleaning with Excel and Python
- Data Visualization with Power BI and Tableau
- Model Accuracy Optimization
- Smart Product Recommendations
- Voice Command NLP Models
- Autonomous Deep Learning Solutions
- Machine Learning Database Integration
- Soft Skills Program
- Resume Building
- Communication Skills
- Interview Preparation
- Time Management
- HR Round Guidance
- E-mail Writing
- Personality Development
- Emotional Intelligence
- Conflict Management
- Salary Negotiation
- Creativity
- Get Certification
Frequently Asked Questions (FAQ)
ScaleUp offers skill-focused programs in Data Science, AI, Cybersecurity, Business Analytics, and more — all built to help you grow in today’s digital-first world.
Yes. Whether you’re just starting out or switching careers, our courses are designed to guide you step by step — no prior experience needed.
Definitely. Every program includes practical projects, case studies, and challenges that help you apply your skills in real-life scenarios.
You’ll learn from seasoned industry professionals — people who’ve worked in top companies and bring real-world experience into every session.
Yes, we do. You’ll get help with resume building, job hunting strategies, interview prep, and even referrals through our career support team.
Of course. You’ll retain access to all your learning materials, so you can revisit lessons or refresh your knowledge anytime you need.
Yes. You’ll earn an industry-recognized certificate from ScaleUp.
Yes. You can learn at your own pace, with access to both live sessions and recorded content — designed to fit around your schedule.
You’ll have access to mentors, doubt-solving sessions, discussion forums, and personal learning assistants to help you stay on track.
Absolutely. You’ll join a vibrant group of peers, mentors, and alumni — a space to ask questions, share progress, and grow together.