Learning Journey

Algorithms

September 2020

Algorithmic techniques and ideas for computational problems. Sorting and searching, divide and conquer, greedy algorithms, dynamic programming. Visit

July 2020

OOP, Inheritance and Polymorphism for code reuse, Class design. Visit

List of DataCamp courses completed.

Software Engineering for Data Scientists in Python: Maintainability, utilizing classes, PEP-8
Command Line Automation in Python: IPython shell commands, Subprocess, Command line functions
Data Processing in Shell: Download, process, clean, and transform data via the command line.
Introduction to Git
Writing Efficient Code with pandas: Efficient iterating and faster task performance with Pandas
Writing Efficient Python Code: Timing and profiling code, code optimizations
Introduction to PySpark: Pipelines and model building with PySpark

Machine Learning Scientist specialization

Linear classifiers: Logistic regression, SVM, loss function.
Machine Learning with Tree-Based Models in Python: CART, Bagging, Boosting, Hyperparameter tuning
Extreme Gradient Boosting with XGBoost: Classification, Regression, Hyperparameter tuning and pipelines with XGBoost
Cluster analysis in python: SciPy library, Hierarchical Clustering, K-means clustering
Dimensionality Reduction in Python: Feature selection and extraction, PCA
Preprocessing for Machine Learning in Python: Data preprocessing and Standardization, Feature engineering, Feature selection
Machine Learning for Time Series Data in Python
Introduction to Natural Language Processing in Python
Hyperparameter Tuning in Python
Image Processing with Keras in Python
Deep Learning with Keras: Keras functional API, category embeddings and multiple-output networks.
Introduction to TensorFlow in Python: High-level APIs in TensorFlow 2.0, Linear models, Neural networks
Feature Engineering for NLP in Python: Text preprocessing, POS tagging and NER, N-gram, Tf-Idf
Model Validation in Python
Feature Engineering for Machine Learning in Python: Creating features, data manipulation, data distribution, text data feature engineering
Machine Learning with PySpark: Classification, regression, ensemble and pipelines with PySpark

July - Sep, 2019

Intermediate Python: matplotlib, Dictionaries, Pandas, Logic, Control flow and Filtering
Data Science tools: Functions and Error handling, Iterators, List comprehensions, Generators
Importing data in Python: API, requests module, http files, Flat files, Relational databases in python
Data Preparation: Extraction and transformation, Indexing, Reshaping, Aggregating data
Relational database in SQL: Creating and merging relational databases, selecting, filtering, aggregating and sorting.
Data Visualization: Matplotlib, Seaborn, Bokeh, Time Series, annotations, and interactions
Statistics in Python: Bootstrap confidence interval, Hypothesis testing
Data Processing with Shell: Manipulating data, Unix tools and Batch processing
Supervised Learning with scikit-learn: Scikit-learn, Classification, Regression, Model-tuning, Pre-processing and Pipelines
Unsupervised Learning in Python: SciPy, Clustering, t-SNE, Dimensionality reduction, Feature engineering
Other courses include: Conda Essentials, Network Analysis in Python, Introduction to Deep Learning in Python

Customer Analytics: Descriptive analytics, Predictive analytics and rescriptive analytics for data-informed decision making.