Dakota Meng

Junior data scientist and machine learning engineer from a rigorous engineering background.

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About Me

  Che-Ting (Dakota) Meng has always been fascinated by the power of numbers and data to predict and shape the future. He firmly believes that our history is encoded in data, and as we continue to generate more, it becomes the foundation for shaping our future.
  With a background in Civil Engineering and a prestigious national student researcher award in data analysis for optimizing energy consumption in green building designs, Dakota is driven by an insatiable curiosity to explore the rapidly evolving realms of AI and machine learning algorithms.
  Currently pursuing his master's degree at Georgia Tech in Computer Science and Engineering, Dakota aspires to become a problem solver who not only delivers insightful and accurate solutions but also infuses them with intuition and creativity, aiming to make a meaningful impact on the world.

Or...
A little showcasing

Tech Stack

Programming Languages Python, SQL, R, C++, Julia, JavaScript, HTML, CSS
Libraries & Frameforks Pandas, Numpy, Scipy, Scikit-Learn, XGBoost, Django;
Matplotlib, Seaborn, Plotly, Dash, ggplot;
NLTK, Textblob, OpenCV;
Tensorflow, PyTorch, Keras;
D3.js;
Tools MySQL, PostgreSQL, SQLite;
Docker, Github;
Spark (SparkML, PySpark), Databricks;
Linux, Bash script;
Power BI, Tableau;
AWS;
Microsoft Office.

Skills

Explorative data analysis (EDA);
BigData (Databricks, Hadoop and Spark, AWS, Google Cloud, OpenRefine);
Web crawling and scraping (Selenium, Scrapy, Beautiful Soup);

Relational database (MySQL, SQLite, PostgreSQL);

Data mining,Regression, Time series analysis, Decriptive statistics, Hypothesis testing;

Power BI, Tableau;
Python (Matplotlib, Seaborn, Plotly, Flask);
D3.js, HTML, CSS;

Classification (SVM, Random forest, Bayesian classifier, KNN, etc.);
Clustering (K-Means, Heirachical clustering, DBSCAN, etc.);
Regression (Linear regression, Gaussian mixture model, Kernel regresion, Bayesian regression, etc.);

Basics (MLP, RNN, CNN, LSTM);
Generative (GAN, Stable Diffusion, Transformers, etc.);
Embedding (VAE, Word2Vec, etc.);
NLP
Commputer Vision;