1.1. About Bootcamp
1.2. What is Data Science?
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Are you ready to become a Data Scientist? You can give a new direction to your career with a 6-month bootcamp. The world of data science is waiting for you with our organized and comprehensive curriculum prepared with Python ecosystem.
Why You Should Become a Data Scientist ?
% 100 recruitment of graduates
Data science is one of the the fastest growing IT field today. There has been a huge increase in data flowing from millions of websites, mobile applications, sensors, and more. Data science has become an exciting and ever-evolving field thanks to the ever-increasing need for collecting and evaluating this increasing data.
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Whether large or small, companies realizing the importance of data. This situation has opened a great door for qualified professionals who is well-educated with important skills and certificated in data science. If you look at job postings, you will see that one of the most needed title is Data Scientist.
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If you choose to be a data scientist: You will have a worldwide profession, You will earn more than most of the people in IT field. Beacuse of the need for increasing need for data scientist in every field, you will choose the areawhich you want to work.
% 100 Satisfaction
You will be a data scientist who meets the needs of the field in 6 months by working 25 hours a week. With a comprehensive and application-oriented curriculum, you will graduate with the skills to enable you to have an advantageous position in the field, even if you have no previous experience in software development and coding.
Bootcamp Curriculum
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Chapter 1: What is Data Science?
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Chapter 2: Introduction to Python
2.1. Installations
2.2. Python Basics
2.3. Data Structures
2.4. Conditional Expressions and Loops
2.5. File Operations, Functions, Error Handling and Modules
2.6. Numpy : Scientific Computing in Python
2.7. Pandas: Spreadsheet of the Python World
2.8. Visualization with Matplotlib
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Chapter 3: Introduction to Statistics
3.1. Basic Statistics Concepts
3.2. Probability
3.3. Statistical Distributions
3.4. Population, Sampling and Related Theorems
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Chapter 4: Exploratory Data Analysis
4.1. What is Exploratory Data Analysis?
4.2. Data Cleaning 1: Data Types
4.3. Data Cleaning 2: Missing Values
4.4. Data Cleaning 3: Outliers
4.5. Exploratory Data Analysis 1: Univariate Analysis
4.6. Exploratory Data Analysis 2: Multivariate Analysis
4.7. Feature Engineering 1
4.8. Feature Engineering 2
4.9. The Concept of Statistical Model
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Project 1: Basic Data Analysis
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Chapter 5: SQL Basics
5.1. SQL and Data Access Methods
5.2. Introduction to Databases
5.3. MySQL Installation
5.4. SQL Basics
5.5. Clustering and Grouping
5.6. Join, CTE and Case
5.7. Accessing the Database with Pyhton
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Chapter 6: Regression Problems and Linear Regression Model
6.1. What is Regression?
6.2. Simple Linear Regression and OLS
6.3. Linear Regression Assumptions
6.4. Understanding the Relationship Between Target Variable and Features
6.5. Measuring Training Performance of Model
6.6. Prediction by Linear Regression
6.7. Overfitting and Regularization
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Project 2: Regression Project
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Chapter 7: Classification Problems
7.1. What is Classification?
7.2. Classification with Logistic Regression
7.3. Performance Measurement Metrics
7.4. Imbalance Data
7.5. Cross Validation
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Project 3: Classification Project
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Chapter 8: Supervised Machine Learning Algorithms
8.1. Classification with KNN
8.2. Regression with KNN
8.3. Decision Trees
8.3. Random Forests
8.5. Decision Support Machines 1
8.6. Kernel Trick
8.7. Decision Support Machines 2
8.8. Regression with Decision Support Machines
8.9. Boosting Methods
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Chapter 9: Clustering Algorithms
9.1. What is Unsupervised Learning?
9.2. K-means
9.3. Three Methods more
9.4. Performance Measuring of Clustering Algorithms
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Project 4: Unsupervised ML Project
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Chapter 10: Dimensionality Reduction
10.1. What is Dimensionality Reduction?
10.2. Principal Components Analysis (PCA)
10.3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
10.4. Uniform Manifold Approximation and Projection (UMAP)
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Chapter 11: Deep Learning
11.1. What is Deep Learning?
11.2. Artificial Neural Networks
11.3. Introduction to Keras and TensorFlow
11.4. CNN Models
11.5. RNN Models
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Project 5: Graduation Project
Frequently Asked Questions
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How does Bootcamp work?
You access the curriculum through our online platform (which will be available unlimitedly with updates to the curriculum). A data science mentor will be assigned for you. Your mentor plans how to follow the curriculum and follows you through two online audio/video conferences. In these conferences: You are reviewing the material you are working on that week. You ask the problems encountered in the lessons and you find solutions together. Your mentor sets weekly goals and motivates you to progress at the required pace for graduation and decide how to proceed.
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How is the curriculum content?
A very comprehensive curriculum awaits you. A curriculum that encompasses all the basic techniques and concepts needed in today’s world. Application and practice is in the foreground. The curriculum consists of chapters and lot of courses within each chapter. At the end of each lesson, hands-on exercises will be waiting for you. At the end of the main sections, you will have projects to do.
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What additional support is available outside the curriculum?
Every two months, we will be organizing workshops that bring you together with professional data scientists. So, you will be able to see what is going on in the business world and ask questions to professionals
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Who can participate in the Bootcamp?
Anyone interested in Data Science can attend bootcamp. You don’t even have to have any coding knowledge before. Bootcamp starts by teaching you coding with Python. The only need is enthusiasm. Since you set the time you can study on Bootcamp, it doesn’t matter if you’re working somewhere. Working students prefer to study and interview with own mentor in the evenings and weekends. There is a similar situation for students.
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Is Graduation guaranteed?
No it is not! Your projects and presentations will be evaluated, you may be asked to develop or redo them if necessary. You must complete the bootcamp within the specified time. This is a motivation element for you.