5 Questions
What are the key topics covered in Module 3: Data Science Methodology?
Data collection and preparation, Missing value handling, Data scrubbing, Data transformation
Explain the purpose of Module 4: Basic Statistics required to handle data.
To cover topics such as Exploratory Data Analytics, Population and sample, Moments and generating functions, Measure of Variability, Hypothesis Testing, bias, and variance.
List some of the specialized visualization tools covered in Module 5.
Matplotlib (area plot, scatter plot, line plot, histogram, bar charts, box plot, heat map, faceting, pair plot), seaborn, ggplot2
What are the different types and levels of data discussed in Module 2: Introduction to Data Science?
Various types and levels of data, Structured vs. Unstructured data, quantitative vs. qualitative data
What are the two platforms introduced in Module 1: Introduction to Python for Data Science?
Google Colab and Jupyter Notebook
Study Notes
Module 3: Data Science Methodology
- Covers data science methodology, including key concepts and frameworks
Module 4: Basic Statistics
- Introduces basic statistics required to handle data, including descriptive statistics and inferential statistics
- Enables students to understand and work with data effectively
Module 5: Data Visualization
- Covers specialized visualization tools, including:
- Matplotlib
- Seaborn
- Plotly
- Bokeh
- Enables students to effectively communicate insights and results using data visualization
Module 2: Introduction to Data Science
- Discusses different types of data, including:
- Qualitative data
- Quantitative data
- Examines different levels of data, including:
- Nominal data
- Ordinal data
- Interval data
- Ratio data
Module 1: Introduction to Python for Data Science
- Introduces two key platforms for data science with Python:
- Jupyter Notebook
- Google Colab
Test your knowledge of Python for Data Science and Introduction to Data Science with this quiz. Explore topics such as Google Colab, Jupyter Notebook, data structures, Pandas, NumPy, types of data, data science life cycle, and more. Sharpen your understanding of key concepts and techniques essential for data analysis and manipulation.
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