A Data Scientist is a professional who not only works with data but also helps organizations make more informed decisions using scientific methods and technologies. The profession of a Data Scientist offers vast opportunities for those who are ready to learn, adapt to new challenges, and apply their skills to solve real-world problems. It is not just a job but a chance to be part of the digital revolution.
Our values
Education at IT Nova is based on three essential components of active learning:
Up-to-dated Curricula
The knowledge and skills that our students gain must meet the requirements of employers today and in the future. Therefore, we review and update our course program every six months.
Instructors - Practitioners
Instructors at IT Nova are specialists in their field with more than five years experience. The methods they teach you are the same methods they use on a day-to-day basis at leading companies. In our classrooms, students receive only relevant knowledge from real practice.
Friendly atmosphere
We have created a unique learning environment where teachers and students communicate on an equal footing. Our classrooms and laboratories are equipped with comfortable workplaces, computer equipment, and licensed software.
Curriculum
- Python: Using Jupyter Notebook, variables, data types, loops, in-depth work with various data types, working with files.
- Python Libraries: Basics of statistics, linear algebra, calculus, probability theory.
- SQL: Basics of syntax, table joins, window functions, ranking functions, offset functions.
- Git and GitHub
- Fundamentals of Machine Learning
- Machine Learning Algorithms: Linear algorithms, decision trees, metric algorithms, cluster analysis.
- File Extraction
- Deep Learning: Theory of differences between DL and ML, neural networks.
- Docker
- Calculus: Graphs and functions.
- Probability Theory: Key definitions, probability properties, conditional probability.
- Bayes' Theorem, Combinatorics
- Packages: Scrapy, BeautifulSoup, Requests (sending HTTP requests to websites, HTML parsing, data retrieval).
- CSV/Excel Files: Reading PDF files, retrieving data from images (Pillow and OpenCV).
- Examples of Work with NLP
- Examples of Work with Computer Vision
Upon graduation of the course you will be able to
- Collect, clean, and transform data.
- Perform data analysis to identify patterns and anomalies.
- Use data visualization to interpret results.
- Master Python fundamentals and work with its core libraries (NumPy, Pandas, Matplotlib).
- Use SQL for database operations, including complex queries and table joins.
- Work with Git and GitHub for project management.
- Apply mathematical methods for data analysis.
- Use probability theory and statistics to build models and make predictions.
- Develop machine learning models, including linear regression, decision trees, and cluster analysis.
- Understand and apply feature scaling and encoding methods.
- Understand the fundamentals of neural networks and implement them using PyTorch.
- Work on Computer Vision and NLP tasks.
- Extract data from websites using tools like Scrapy, BeautifulSoup, and Requests.
- Work with various file formats (CSV, Excel, PDF, images).
- Use Docker for deploying models in production environments.
- Formulate hypotheses and validate them using data.
- Automate analysis and prediction processes.
- Contribute to Data Science projects, providing solutions for business needs.
Choose a form of training and sign up for a trial lesson in Taguig City
Certificates
IT Nova students receive international diplomas
