Data Science

Our Data Science course helps you to master data analysis and AI [Artificial Intelligence] training, including practical experience with Python libraries, machine learning, and deep learning. With experienced instructors, you’ll develop skills to tackle complex data challenges and help to take the lead in introducing new digital elements to any industry.

Summary

We offer an exceptional Data Science Program to equip you with the necessary skills for success in today's data-driven world. Our detailed program lasts for seven months, ensuring a thorough understanding of the data analysis and artificial intelligence (AI) groups of lessons.

In the data analysis lessons, you will study the basic elements of data processing, cleaning, and visualization techniques offered by Python libraries. You will learn to use different statistics-related methods and tools to extract meaningful information from complex sets of data. You will gain practical experience in exploring and controlling data through hands-on projects and real-world examples. This will enable you to make informed decisions and recommendations based on evidence which is driven by data.

The AI lessons will take you on a journey into the exciting world of artificial intelligence. You will explore the rules and algorithms behind machine learning, deep learning, natural language processing (NLP) and computer vision (CV), and learn the working rules behind large language models such as ChatGPT and Google Bard. You will acquire the expertise to develop solutions for analysis involving forecasts and prescriptions by building and training AI models. From recognising images to language translation, you will discover the endless possibilities of AI and the impact of its ability to transform many different industries.

Throughout the program, our degree-qualified instructors with will guide you every step of the way. They will provide individual and personal feedback and support to make your learning journey interesting and rewarding. After the 7-month program, you will become a skilled data scientist with all the necessary knowledge required. You will have the practical expertise to handle complex data challenges and introduce new developments in your field.

Join us today and unlock the vast potential of data science and AI.

Targeted Job Titles

- Data Scientist
- Data Analyst
- Business Analyst
- Data Storyteller
- Database Administrator
- Data Engineer
- Data Architect
- Machine Learning Engineer
- Deep Learning Engineer
- AI Specialist

Courses Content

Data Analysis Module

1.1. IT Fundamentals
1.1.1 Introduction to IT
1.1.2. Hardware Basics
1.1.3. Software Basics
1.1.4. Network Basics
1.1.5. Programming Basics
1.2. Python Programming Language-Core Python
1.2.1. Data Types
1.2.1.1. Python Intro-Installation-Data Types General-PEP-8 Rules
1.2.1.2. String Operations-1 (Indexing, slicing, other methods )
1.2.1.3. String Operations-2 (Formatting and other methods)
1.2.1.4. Boolean Types(logical, Artimetic, Assignment Operators and other operators
1.2.1.5. List and List methods
1.2.1.6. Tuples and Sets
1.2.1.7. Dictionaries
1.2.2. Key Topics
1.1.2.1. If-Elif-Else-Catch
1.1.2.2. While and For loops
1.1.2.3. Functions
1.3. Python Programming Language-Advanced Python
1.3.1 Try Exceptions
1.3.1.1. OOP
1.3.1.2. File_Handling
1.3.1.3. Random and Json module
1.3.1.4. Datetime and Regex
1.4. Git-Github
1.5. SDLC-Jira
1.6. HTML+CSS
1.7. SQL
1.8. Google SpreadSheets (GSS)
1.9. Numpy-Pandas
1.10. Statistics
1.11. Data Visualization (Matplotlib, Seaborn, Plotly, Bokeh)
1.12. Tableau
1.13. Power BI
1.14. Data Storytelling
1.15. Capstone Projects (5 Projects)

Artificial Intelligence (AI) Module

2.1. Machine Learning
2.1.1. Supervised Learning
2.1.1.1. Naive Bayes
2.1.1.2. Linear Regression
2.1.1.3. Logistic Regression
2.1.1.4. Support Vector Machines (SVM)
2.1.1.5. K-Nearest Neighbor (KNN)
2.1.1.6. Decision Tree
2.1.1.7. Random Forest
2.1.1.8. Boosting Methods (BM)
2.1.1.8.1. Ada Boosting Methods (ABM)
2.1.1.8.2. Gradient Boosting Methods (GBM)
2.1.1.8.3. XGBoosting Methods (XGBM)
2.1.2. Unsupervised Learning
2.1.2.1. K-Means Clustering
2.1.2.2. Hierarchical Clustering
2.1.2.3. Principal Component Analysis (PCA)
2.1.3. Recommender Systems
2.2. Deep Learning
2.2.1. Artificial Neural Networks (ANN)
2.2.2. Convolutional Neural Networks (CNN)
2.2.3. Computer Vision
2.2.4. Recurrent Neural Networks (RNN)
2.2.4.1. Long Short-Term Memory (LSTM)
2.2.4.2. Gated Recurrent Unit (GRU)
2.2.5. Natural Language Processing (NLP)
2.3. Web Scraping
2.4. Prompt Engineering
2.5. Deployment
2.5.1. Streamlit
2.5.2. Flask
2.5.3. AWS-Docker
2.5.4. Heroku
2.5.5. Capstone Projects (15 Projects)
3. Career Coaching
4. Internship Project

Announcement

Are you ready to enter the extraordinary world of Data Science and Artificial Intelligence? Prepare for an exceptional journey with TechPro Education’s new intensive course designed to set you apart from your competitors.

What makes us different, you ask? Get ready to be amazed! We offer a thorough Data Science programme that goes beyond the ordinary. Get ready to learn all about cutting-edge technologies and open the doors to the mysterious world of data.

Here's what makes our Data Science programme stand out from the rest:

Sixty hours of intensive Computer Vision training: Master the art of extracting meaningful information from visual data, finding patterns and trends the naked eye can't see.

All training involving Chat GPT: Experience the power of AI as our curriculum smoothly connects with Chat GPT, allowing you to explore new approaches and use the latest developments in Natural Language Processing.

Training in telling Data Stories: Learn to transform complex data into fascinating stories that catch the interest of audiences and lead to informed decision-making.

Prompt Engineering: Develop your creativity with advanced Prompt Engineering techniques, enabling you to generate precise and context-aware model responses.

More than 20 Capstone Projects: Apply your skills to real-world challenges through our extensive collection of Capstone Projects, gaining hands-on experience and building an impressive display of examples of your work.

Opportunities to be an Intern in Europe and America: Widen your world and gain global exposure through our exclusive intern positions in famous companies across Europe and America.

University certificate from Richmond College, UK: Stand out with an approved university certificate, guaranteeing your expertise and opening doors to endless opportunities.

Opportunities to be an intern with academics from Stanford University in Silicon Valley: Join the centre of digital change, working with famous experts from Stanford University in a Silicon Valley company.

Opportunity to start to work four months after starting the course interview sessions, preparing you to shine in any professional setting.

Take advantage of this extraordinary chance to become a leading professional in the field of Data Science. Book your place on our course today and unlock a successful and satisfying career.

Timetable

Turkish Course Night Time Timetable

Day	EST	CET	TSI
Monday	01:30 pm - 04:30 pm	19:30 - 22:30	20:30 - 23:30
Tuesday	01:30 pm - 04:30 pm	19:30 - 22:30	20:30 - 23:30
Wednesday	01:30 pm - 04:30 pm	19:30 - 22:30	20:30 - 23:30
Thursday	01:30 pm - 04:30 pm	19:30 - 22:30	20:30 - 23:30
Friday	01:30 pm - 04:30 pm	19:30 - 22:30	20:30 - 23:30
Saturday	01:30 pm - 04:30 pm	19:30 - 22:30	20:30 - 23:30
Sunday	-	-	-

EST : Eastern Standard Time
CET : Central European Time
TSI : Turkish Time

Note: Please note that schedules may change due to daylight saving time in summer and winter.

FAQ

Do you need a Maths background to be a Data Scientist?

No, it is not necessary. Basic knowledge of Mathematics at high school level is enough. Although machine learning algorithms have a mathematical background, you do not need to master this mathematical content to use these algorithms. However, you may need to learn more about these topics if you want to do academic work or wish to deepen your knowledge of the mathematical side of things. Apart from that, you do not need an advanced knowledge of mathematics to work as a data scientist, data analyst, or in a similar position in the industry. The basic concepts of statistics necessary to understand the subject of machine learning will be taught on our course.