What is Data science? The Data science process, A data scientist role in this process, NumPy Basics: The NumPy ndarrays: A Multidimensional Array Object (Creating ndarrays, Data Types for ndarrays, Operations between Arrays and Scalars, Basic Indexing and Slicing, Boolean Indexing, Fancy Indexing), Data Processing Using Arrays (Expressing Conditional Logic as Array Operations, Sorting, Unique)
………………………………………………………………………………………………………........Questions:
1. Define Data Science? Explain the process involved in the data science.
2. Explain about Data Processing using arrays with examples.
3. What are the roles and responsibilities of data scientist in the data science?
4. Explain various operations that can be applied on Array in python.
5. Why to use NumPy? What is ndarry in NumPy? Explain.
6. How indexing and slicing is done in NumPy?
7. How to crate ndarry and discus about data types for ndarry.
8. Write down the operations between arrays and scalars.
9. Write a python code to perform matrix addition using NumPy ndarry.
UNIT-II
Getting Started with pandas: Introduction to pandas, Library Architecture, Features, Applications, Data Structures (Series, Data Frame, Index Objects), Essential Functionality (Reindexing, dropping entries from an axis, Indexing, selection, and filtering), Sorting and ranking, Summarizing and Computing Descriptive Statistics (Unique Values, Value Counts), Handling Missing Data.
…………………………………………………………………………………………………………Questions:
1. What are pandas in Data Science? Why they are required?
2. Discuss the different types of data structures in Pandas.
3. How sorting and ranking functions will work on data frames. Explain.
4. Explain the steps to create data frame using list.
5. What are the key features of Pandas Library? (OR) File Hierarchy in Pandas
6. Explain the process involved in handling missing data.
7. Design a script to drop entries from an axis in series
8. How to drop entries from an axis without changing its index?
9. Narrate some descriptive statistical functions used with pandas.
UNIT-III
Data Loading, Storage, and File Formats: Reading and Writing Data in Text Format (Reading Text Files in Pieces, Writing Data Out to Text Format, Manually Working with Delimited Formats, JSON Data), Binary Data Formats (Using HDF5 Format, Reading Microsoft Excel Files), Interacting with HTML and Web APIs,
……………………………………………………………………………………………………………
Questions:
1. What is data storage in file formats?
2. What are different storage formats?
3. How do I connect to HTML to web API?
4. What are some common data formats used in data science?
5. What are the ways to read the different types of files in python?
6. Explain the steps involved in storing and loading data.
7. How web scrapping will be performed in Python with XML and HTML.
8. How do you write Data out to text format in python?
9. What is format of JSON data and how do you read the JSON data in Python.
10. How to load HDF5 Data format.
UNIT-IV
Data Wrangling: Clean, Transform, Merge, reshape: Combining and Merging Data Sets (Database-style, Merging on Index, Concatenating Along an Axis, Combining Data with Overlap), Reshaping and Pivoting (Reshaping with Hierarchical Indexing), Data Transformation (Removing Duplicates, Transforming Data Using a Function or Mapping, Replacing Values.)
Plotting and Visualization: A Brief matplotlib API Primer (Figures and Subplots, Colors, Markers, and Line Styles, Ticks, Labels, and Legends, Annotations and Drawing on a Subplot, Saving Plots to File), Plotting Functions in pandas (Line Plots, Bar Plots, Histograms and Density Plots, Scatter Plots)
……………………………………………………………………………………………………………
Questions:
1. What are data wrangling techniques?
2. How do I remove duplicate values from a list without changing the order?
3. Difference between data wrangling Vs Data cleaning
4. What are the techniques used for data reshaping?
5. Illustrate concatenating along an Axis of arrays in NumPy?
6. How can we combine and merge datasets in Python programming?
7. Explain the procedure of data transformation with examples.
8. How to remove duplicates in data frames? Explain with an example.
9. Illustrate the process of replacing values.
10. List and explain the various plotting functions with examples.
11. Explain the process of creating subplots in PYTHON?
12. Explain the different markers supported by matplotlib.
13. Differentiate histogram and density plots.
14. Illustrates ticks and tick labels
15. Explain how to save plots to file in pandas with an example.
UNIT-V
Data Aggregation and Group Operations: GroupBy Mechanics (Iterating Over Groups, selecting a Column or Subset of Columns, Grouping with Dicts and Series, Grouping with Functions, Grouping by Index Levels) Data Aggregation (Column-wise and Multiple Function Application, Returning Aggregated Data in “unindexed” Form), Group-wise Operations and Transformations (Apply: General split-apply-combine, Example: Filling Missing Values with Group-specific Values.)
…………………………………………………………………………………………………………Questions:
1. What are various Groupby mechanics? How to perform Groupby operations on any dataset.
2. Explain about data aggregation functions.
3. Compute group summary statistics unique values, value counts in data frame format.
4. Explain in detail the process of returning aggregated data in “unindexed” form in PYTHON
5. Explain the group-wise operations in Pandas with examples.
6. Explain in detail about data aggregate functions.
7. Illustrate the Groupby operation in dicts with examples.
8. What is data aggregation? Explain how to aggregate data using different functions depending on the column.
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