|
Examination Scheme (in Marks) |
||||
|
Theory ESE
(E) |
Theory PA (M) |
Practical ESE Viva (V) |
Practical PA(I) |
Total |
|
70 |
30 |
30 |
20 |
150 |
|
Teaching Scheme (in Hours) |
|||
|
Theory |
Tutorial |
Practical |
Total |
|
3 |
0 |
2 |
3 |
 |
Unit-1: Overview of Python and Data Structures
Basics of
Python including data types, variables, expressions, objects and functions.
Python data structures including String, Array, List, Tuple, Set, Dictionary
and operations them. Unit-2: Data Science and Python
Discovering
the match between data science and python: Considering the emergence of data
science, Outlining the core competencies of a data scientist, Linking data
science, big data, and AI , Understanding the role of programming, Creating
the Data Science Pipeline, Preparing the data, Performing exploratory data
analysis, Learning from data, Visualizing, Obtaining insights and data
products Understanding Python's Role in Data Science: Introducing Python's
Capabilities and Wonders:Why Python?, Grasping Python's Core Philosophy,
Contributing to data science, Discovering present and future development
goals, Working with Python, Getting a taste of the language, Understanding
the need for indentation, Working at the command line or in the IDE Unit-3: Getting Your Hands Dirty With
Data
Using the
Jupyter Console, Interacting with screen text, Changing the window
appearance, Getting Python help, Getting IPython help, Using magic functions,
Discovering objects, Using Jupyter Notebook, Working with styles, Restarting
the kernel, Restoring a checkpoint, Performing Multimedia and Graphic
Integration, Embedding plots and other images, Loading examples from online
sites, Obtaining online graphics and multimedia. Unit-4: Data Visulization
Visualizing
Information: Starting with a Graph, Defining the plot, Drawing multiple lines
and plots, Saving your work to disk, Setting the Axis, Ticks, Grids, Getting
the axes, Formatting the axes, Adding grids, Defining the Line Appearance,
Working with line style, Using colors, Adding markers, Using Labels,
Annotations, and Legends, Adding labels, Annotating the chart, Creating a
legend. Visualizing the Data: Choosing the Right Graph, Showing parts of a
whole with pie charts, Creating comparisons with bar charts, Showing
distributions using histograms, Depicting groups using boxplots, Seeing data
patterns using scatterplots, Creating Advanced Scatterplots, Depicting
groups, Showing correlations, Plotting Time Series, Representing time on
axes, Plotting trends over time, Plotting Geographical Data, Using an
environment in Notebook, Getting the Basemap toolkit, Dealing with deprecated
library issues, Using Basemap to plot geographic data, Visualizing Graphs,
Developing undirected graphs, Developing directed graphs. Unit-5: Data Wrangling
Wrangling
Data: Playing with Scikit-learn, Understanding classes in Scikit-learn,
Defining applications for data science, Performing the Hashing Trick, Using
hash functions, Demonstrating the hashing trick, Working with deterministic
selection, Considering Timing and Performance, Benchmarkin, with,timeit,
Working with the memory profiler, Running in Parallel on Multiple Cores,
Performing multicore parallelism, Demonstrating multiprocessing. Exploring
Data Analysis: The EDA Approach, Defining Descriptive Statistics for Numeric
Data, Measuring central tendency,Measuring variance and range ,Working with
percentiles, Defining measures of normality, Counting for Categorical Data,
Understanding frequencies, Creating contingency tables, Creating Applied
Visualization for EDA ,Inspecting boxplots |
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Reference Books |
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Index |
Title |
Author |
Publication |
Link |
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2 |
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3 |
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4 |
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5 |
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6 |
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7 |
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8 |
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