AI Powered Data Science
Course Duration
60 Hours**
Course Cost
3000 euros
Instructor hours only. Significant additional self-study will be required.
Join our immersive, instructor-led online training course tailored for beginners and professionals from diverse backgrounds
Unlock the Future with the Career Comeback Program through
- Interactive learning: Learn in an interactive and immersive classroom environment by our experts and get your doubts cleared on the spot
- Live Demonstrations: Experience cutting-edge technology in action.
- Immersive Simulations: Safely explore and interact with advanced tools and environments.
- Real-World Case Studies: Gain insights from practical applications across industries.
- Essential Tools Training: Master the tools and platforms needed to thrive in the modern digital landscape.
- Practical Applications: Engage in hands-on projects to apply concepts in real-world scenarios.
Why AI Powered Data Science is in demand?
- Massive Data Growth: Organizations need experts who can clean, analyze, and extract insights from massive datasets.
- AI Integration: Data Scientists must now understand and apply AI tools—from NLP to neural networks—to build smarter solutions.
- Cross-Domain Application: From finance to healthcare, companies seek data-literate professionals who can guide data-driven decisions.
- Fast-Paced Career Shifts: Mid-career tech professionals are quickly transitioning into AI and data science with targeted upskilling.
- Hands-On Tools: Employers look for candidates fluent in Python, SQL, Scikit-learn, and visualization tools, not just theory.
- High ROI Roles: Data scientists are among the top-paid professionals globally, especially those skilled in applied AI.
Program Structure
Module 1: Welcome to the World of Data Science
- What is Data Science? Defining the field and its impact.
- Data Science Roles and Career Paths (Leveraging your tech background).
- The Data Science Lifecycle (From understanding the problem to deployment).
- Essential Tools Overview (Python, Libraries, Environments).
- Setting up Your Data Science Environment (Installation and basic setup).
Module 2: Python for Data Science Kickstart
- Python Fundamentals Refresher (Syntax, Data Types, Control Flow).
- Working with Data Structures (Lists, Dictionaries, Tuples, Sets).
- Introduction to NumPy (Numerical Operations).
- Introduction to Pandas (Data Structures: Series & DataFrames).
- Basic File Handling (Reading CSV, Excel).
Module 3: Data Acquisition & Database Interaction
- Understanding Different Data Sources.
- Introduction to Relational Databases & SQL.
- Basic SQL Queries for Data Retrieval (SELECT, FROM, WHERE, LIMIT).
- Joining Data from Multiple Tables.
- Connecting Python to Databases.
Module 4: Data Wrangling & Cleaning with Pandas
- Handling Missing Data (Identification, Imputation, Dropping).
- Dealing with Duplicate Values.
- Data Transformation (Mapping, Applying Functions).
- Reshaping DataFrames (Pivot, Melt).
- Combining Datasets (Merge, Concatenate).
Module 5: Exploratory Data Analysis (EDA) & Descriptive Statistics
- Purpose and Process of EDA.
- Summary Statistics (Mean, Median, Mode, Variance, Std Dev, Quartiles).
- Understanding Data Distributions.
- Correlation and Covariance.
- Grouping and Aggregating Data.
Module 6: Data Visualization for Communication
- Principles of Effective Data Visualization.
- Introduction to Matplotlib & Seaborn.
- Creating Common Plots (Histograms, Scatter Plots, Line Plots, Bar Plots).
- Visualizing Relationships and Distributions.
- Customizing Plots for Clarity.
Module 7: Statistics and Probability Essentials
- Probability Basics (Events, Independence, Conditional Probability).
- Probability Distributions (Normal, Binomial, etc.).
- Sampling and Sampling Distributions.
- Introduction to Inferential Statistics.
- Hypothesis Testing Fundamentals (Null vs. Alternative, p-values).
Module 8: Introduction to Machine Learning
- What is Machine Learning? (Supervised, Unsupervised, etc.)
- ML Workflow (Data Prep, Model Training, Evaluation).
- Splitting Data (Training, Validation, Testing).
- Introduction to Scikit-learn.
- Evaluation Metrics (Accuracy, Precision, Recall, F1-Score – basic).
Module 9: Supervised Learning Algorithms
- Linear Regression (Predicting Continuous Values).
- Logistic Regression (Predicting Categories).
- Decision Trees (Understanding Decision Logic).
- Introduction to Ensemble Methods (e.g., Random Forests concept).
- Model Training and Prediction using Scikit-learn.
Module 10: Practical Application & Next Steps
- Working on a Capstone Project (Applying learned skills).
- Model Evaluation in Depth (Cross-validation, specific metrics).
- Brief Introduction to Model Tuning (Hyperparameters).
- Overview of Model Deployment Concepts.
- Beyond the Basics (Briefly touch upon Big Data, Cloud, MLOps, Ethics, Specializations).
- Building Your Portfolio and Career Guidance.
Why learn this skill from BlockVerse?
- Learning That’s Personal and Live: Real Humans, Real Impact Our live, instructor-led sessions connect you directly with experts who guide, teach, and answer your questions in real-time in a virtual class. Say goodbye to pre-recorded videos—experience dynamic, human-centered learning that adapts to your pace and needs. Our programs ensure you feel confident and capable from the start and are built on the timeless method of guided teaching that’s worked from Socrates to today.
- Hands-On, Tailored Support: Your Journey, Your Way With small batch sizes and expert mentorship, every learner gets personalized attention. From guided projects to live consultations, our instructors ensure you’re not just learning but excelling, with immediate feedback and support.
- Real Skills, Real Confidence: Whether you’re diving into a new field like crypto or coding, our focus is on building a strong foundation through practical skills and portfolio-worthy projects. We break down complex topics into clear, actionable steps, making even the most intimidating subjects approachable. Start your journey with confidence and practical knowledge you can apply from day one.
- Portfolio Building to Showcase Learners’ Skills Help learners create tangible outcomes (e.g.: projects, case studies, or capstone projects) that they can showcase to potential employers or clients.
- Placement Assistance Helping learners bridge the gap between learning and employment, is a critical step for many. We will also aim to connect learners with job opportunities and employers. We do not guarantee placement
Join Our Waitlist
Cashback
We understand the value of investing in your future, which is why we’re offering an Early Bird Discount of €300 off if you register by November 15. The full program price is €2000, and when you successfully get placed in a company, you receive 25% cashback.
The full program is priced competitively at €3000. If you get selected through us for jobs in organisations you also get a cashback of €500**
** Terms and conditions apply
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