- Python
- Jupyter Notebook
- pandas
- NumPy
- Data Cleaning
- Data Analysis
- Data Visualisation
- SQL
- APIs and Web Scraping
- Statistics
- Conditional Probability
- Git
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Analysing Android and iOS app store data
- Analysed Google Play Store and iOS App Store application data to identify trends and provide insight on categories and genres likely to draw high traffic with little saturation.
- Python; pandas; Data cleaning and analysis
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Exploring and analysing Hacker News forum categories and hourly traffic data
- Analysed categorical posts and identified hourly trends in traffic to inform users of efficient practices.
- Python; pandas; Data cleaning and analysis
- Part 1.1 - Introduction to Python Programming
- Part 1.2 - Basic Operators and Data Structures in Python
- Part 1.3 - Python Functions and Jupyter Notebook
- Part 1.4 - Intermediate Python for Data Science
- Part 2.1 - Introduction to Pandas and NumPy for Data Analysis
- Part 2.2 - Introduction to Data Visualization in Python
- Part 2.3 - Telling Stories Using Data Visualization and Information Design
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https://www.dataquest.io/path/data-scientist/
(Updated 2023/09/29)
- Introduction to Data Analysis in Excel
- Write computer programs using Python
- Save values using variables
- Process numerical data and text data
- Create lists using Python
- Basic Operators and Data Structures in Python
- Use for loops to repeat processes and conduct data analysis
- Implement if, else, and elif statements in programming logic
- Employ logical and comparison operators in Python
- Develop and update Python dictionaries for data manipulation
- Construct frequency tables using dictionaries for data analytics
- Python Functions and Jupyter Notebook
- Write Python functions
- ebug functions
- Define function arguments
- Write functions that return multiple variables
- Employ Jupyter notebook
- Build a portfolio project
- Intermediate Python for Data Science
- Clean and analyze text data
- Define object-oriented programming in Python
- Process dates and times
- Introduction to Pandas and NumPy for Data Analysis
- Improve your workflow using vectorized operations
- Select data by value using Boolean indexing
- Analyze data using pandas and NumPy
- Introduction to Data Visualization in Python
- Visualize time series data with line plots
- Define correlations and visualize them with scatter plots
- Visualize frequency distributions with bar plots and histograms
- Improve your exploratory data visualization workflow using pandas
- Visualize multiple variables using Seaborn's relational plots
- Telling Stories Using Data Visualization and Information Design
- Create graphs using information design principles
- Create narrative data visualizations using Matplotlib
- Create visual patterns using Gestalt principles
- Control attention using pre-attentive attributes
- Employ Matplotlib's built-in styles