Evan Hanif Widiatama
Statistics Student | Data Specialist
Certification Pages
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Summary
- Data Analytics by Google - Coursera
- Advanced Data Analytics by Google - Coursera
- Deeplearning Specialization by Deeplearning.Ai - Coursera
- Data Science with Python (Career Track) - Datacamp
- Data Analyst with Python (Career Track) - Datacamp
- Machine Learning Scientist with Pyhton (Career Track) - Datacamp
- Machine Learning Engineer - Dicoding
- Data Scientist - Dicoding
- to be continued. (#TODO)
Full Certification
Data Analytics by Google- Coursera
Completed the Google Data Analytics Professional Certificate, a thorough program requiring approximately 240 hours of coursework. Developed foundational skills in data collection, cleaning, analysis, and visualization using tools such as SQL, R, Tableau, and spreadsheets. Achieved a strong understanding of data-driven decision making, preparing for entry-level roles in data analytics.
(8/8) Courses completed:
- Foundations: Data, Data, Everywhere
- Ask Questions to Make Data-Driven Decisions
- Prepare Data for Exploration
- Process Data from Dirty to Clean
- Analyze Data to Answer Questions
- Share Data Through the Art of Visualization
- Data Analysis with R Programming
- Google Data Analytics Capstone: Complete a Case Study
Advanced Data Analytics by Google - Coursera
Completed the Google Advanced Data Analytics Professional Certificate, a rigorous program requiring a substantial time commitment. Developed advanced skills in data analysis, statistical methods, and machine learning over approximately 150 hours of coursework. Acquired expertise in advanced techniques using a comprehensive tech stack including Python, SQL, TensorFlow, and BigQuery. Prepared for higher-level roles in data analytics, capable of performing complex data-driven decision making and predictive modeling.
(7/7) Courses Completed:
Deep Learning Specialization by Deeplearning.Ai - Coursera
Currently progressing through the DeepLearning Specialization by DeepLearning.AI, a detailed program consisting of 5 courses and requiring around 80 hours of coursework. Developing advanced skills in neural networks, deep learning techniques, and AI model implementation using tools such as TensorFlow and Keras. Gaining a deep understanding of concepts like neural networks, CNNs, RNNs, LSTMs, and more, preparing for intermediate-level roles in artificial intelligence and machine learning.
(3/5 Courses Completed):
Data Science with Python (Career Track) - Datacamp [X]
Completed the Data Science with Python Career Track by DataCamp, a comprehensive program comprising 30 courses and 13 projects, totaling 112 hours of content. Developed intermediate-level skills in Python programming, data manipulation, visualization, and machine learning using tools such as Pandas, matplotlib, seaborn, scikit-learn, NumPy, Pingouin, statsmodels, and Git, preparing for a career in data science.
(30/30) Courses Completed:
- Introduction to Python
- Intermediate Python
- Pandas: Data Manipulation
- Pandas: Joining Data
- Introduction to Statistics
- Matplotlib: Introduction to Data Visualization
- Seaborn: Introduction to Data Visualization
- Introduction to Functions in Python
- Python Toolbox
- Exploratory Data Analysis
- Working with Categorical Data
- Data Communication Concepts [X]
- Introduction to Importing Data
- Cleaning Data[XX]
- Working with Dates and Times[XX]
- Python - Writing Functions
- statsmodels - Introduction to Regression with[XX]
- Sampling
- Experimental Design[XXX]
- scikit-learn: Supervised Learning
- Unsupervised Learning
- Tree-Based Models Machine Learning
- Intermediate Importing Data[X]
- Preprocessing for Machine Learning
- Developing Python Packages[X]
- Machine Learning for Business[X]
- Introduction to SQL[X]
- Intermediate SQL[X]
- Joining Data in SQL[X]
- Introduction to Git[X]
Data Analyst with Python (Career Track) - Datacamp[X]
Completed the Data Analyst with Python Career Track by DataCamp, a comprehensive program consisting of 9 courses and 5 projects, totaling 36 hours of content. Developed foundational skills in hypothesis testing, data analysis, data manipulation, and data visualization using tools such as Python, Matplotlib, Seaborn, Pandas, statsmodels, and Pingouin. Prepared for entry-level roles in data analysis. (9/9) Courses Completed:
Machine Learning Scientist with Python (Career Track) - Datacamp
Currently progressing through the Machine Learning Scientist with Python Career Track by DataCamp, a comprehensive program consisting of 21 courses and 3 projects, totaling 85 hours of content. Developing intermediate-level skills in end-to-end machine learning processes, including feature engineering, cross-validation, and hyperparameter tuning using tools such as Python, PyTorch, scikit-learn, XGBoost, and Pandas. Preparing for roles focused on machine learning and data science. (16/21) Courses Completed:
- scikit-learn: Supervised Learning
- Unsupervised Learning
- [Linear Classifiers]
- Tree-Based Models Machine Learning
- XGBoost - Extreme Gradient Boosting
- Cluster Analysis
- Dimensionality Reduction
- Machine Learning for Time Series
- Feature Engineering for Machine Learning
- Model Validation
- Hyperparameter Tuning
- Introduction to NLP
- NLP with spaCy
- Feature Engineering for NLP
- PyTorch: Introduction to Deep Learning
- PyTorch: Intermediate Deep Learning
- Image Processing in Python
- PySpark: Introduction
- PySpark: Machine Learning
- Winning a Kaggle Competition
Machine Learning Engineer - Dicoding
Currently progressing through the Machine Learning Engineer program by Dicoding, a comprehensive curriculum developed in collaboration with IBM and industry professionals. The program consists of 6 courses, totaling 256 hours of content, covering topics such as basic data visualization, introduction to programming with Python, introduction to machine learning, machine learning development, applied machine learning, and MLOps. Developing advanced skills using tools such as spreadsheets, Python, NumPy, Pandas, Seaborn, Matplotlib, scikit-learn, TensorFlow, and Keras. Engaging in 2 image classification projects and 1 text classification project. Preparing for roles requiring beginner to advanced proficiency in machine learning and engineering practices. (4/6) Course Completed:
Data Scientist - Dicoding
Currently progressing through the Data Scientist program by Dicoding, a comprehensive curriculum designed by expert teams in collaboration with industry practitioners. The program consists of 7 courses, totaling 247 hours of content, covering topics such as basic data science, SQL, programming with Python, data analysis with Python, machine learning for beginners, machine learning development, and data science application. Developing skills using tools such as spreadsheets, Python, NumPy, Pandas, Seaborn, Matplotlib, scikit-learn, TensorFlow, Keras, and Streamlit. Preparing for roles requiring beginner to advanced proficiency in data science and machine learning practices. (5/7) Courses Completed: