Data Science: Advanced Level
DURATION: 36 HOURS ONSITE TRAINING
(12 Sessions of 3 Hours each)
Method: 1 on 1
The schedule is tentative; finalized after enrollment according to student/instructor availability.
- Introduction to Python programming
- Data science jobs
- Print Hello World
- Azure Notebooks & Jupyter Notebooks –
- Google Colab
- Functions (Arguments and Return)
- Python built-in Data types Concept of mutability and theory of different Data structures
- Control flow statements: If, Elif and Else Definite and Indefinite loops: For and While loops
- Writing user-defined functions in Python
- Loops (For While) If else List/Dictionary
- Nested Loops with if else List/Dictionary (JSON) Class Lambda Functions List Comprehension
- File Handling Web Scraping Exception handling SQLite Python Classes
- Python Read and write Text and CSV files with python List comprehensions and Lambda.
- Classes and inheritance.
- Automate the Boring Stuff
- Matplotlib Numpy Pandas Scipy Python Lambdas Python Regular
- Expressions Collection of powerful, open-source, tools needed to analyze data and to conduct data science.
- Working with jupyter anaconda notebooks pandas numpy matplotlib git and many other tools.
- Data Loading, Storage, and File Formats
- Data Cleaning and Preparation
- Data Wrangling: Join, Combine, and Reshape
- Plotting and Visualization
- Data Aggregation and Group Operations
- Time Series
- Machine learning and data mining techniques are used for in a simple example in Python.
- Run machine learning models on data of choice
- Supervised vs Unsupervised Learning
- Regression vs Classification models
- Categorical vs Continuous feature spaces
- Python Scikit-learn Library
- Modeling Fundamentals: Test-train split, Cross-validation (CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
- Interpreting Results of Regression and Classification Models
- Built your own model and start with your own Data from Kaggle.
- Select your project, download data, clean wrangle and massage your data and make it ready for analysis for Titanic, Iris, and other common Data sets.
- Model DATA
- Run Machine Learning Models and select the best model
- Tweak Model parameters for Titanic Iris Dataset
- Regression analysis K-Means Clustering Principal Component
- Analysis Train/Test and cross-validation Bayesian Methods
- Decision Trees and Random Forests Multivariate Regression
- Multi-Level Models Support Vector Machines K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Understanding and Interpreting results of Regression and Logistic Regression using Google