A Level - Syllabus of Module: A10.1-R5-Data Science Using Python
Introduction :
Data science is an interdisciplinary field that uses scientific processes and various
algorithms to extract knowledge and insights from data which may be
structured and unstructured.
Python has gathered a lot of interest recently as a choice of language for data
analysis/science. Python is a free and open source and a general-purpose programming
language which is easy to learn. Python, due to its versatility, is ideal for implementing
the steps involved in data science processes. Python is being used for web
development, data analysis, artificial intelligence, and scientific computing.
The three best and most important Python libraries for data science are NumPy,
Pandas, and Matplotlib. NumPy and Pandas are used for analyzing and exploring
with data. Matplotlib is a data visualization library used for making various types of
graphs depicting the analysis.
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| A Level - Syllabus of Module: A10.1-R5-Data Science Using Python |
(i) Python Language, Structures, Programming Constructs :
Review of Python Language, Data types, variables, assignments, immutable variables,
Strings, String Methods, Functions and Printing, Lists and its operations, Tuples and
Dictionaries programs, Slicing strings,lists, tuples.
(ii) Data Science and Analytics Concepts :
What is Data Science and Analytics? The Data Science Process, Framing the problem,
Collecting, Processing, Cleaning and Munging Data, Exploratory Data Analysis,
Visualizing results.
(iii)Introduction to NumPy Library :
Numpy - Array Processing Package, Array types, Array slicing, Computation on NumPy
Arrays – Universal functions ,Aggregations: Min, Max, etc., N-Dimensional arrays,
Broadcasting, Fancy indexing, sorting arrays, loading data in Numpy from various
formats.
(iv) Data Analysis Tool : Pandas : Introduction to the Data Analysis Library Pandas, Pandas objects – Series and Data
frames, Data indexing and selection, Nan objects, Manipulating Data Frames,
Grouping, filtering, Slicing, Sorting, Ufunc, Combining Datasets- Merge and join.
Query Data Frame structures for cleaning and processing, lambdas. Aggregation
functions and applying user defined functions for manipulations.
(iv) Statistical Concepts and Functions :
Statistics module, manipulating statistical data, calculating results of statistical
operations. Python Probability Distribution, Functions like mean, median, mode and
standard deviation. Concept of Correlation and Regression.
(v) Matplotlib :
Visualization with Matplotlib, Simple line plots, scatter plots, Density and Contour
plots – visualizing functions, Multiple subplots, Plotting histograms, bar charts, scatter
graphs and line graphs.
(vi) GUI – Tkinter :
Tk as Inbuilt Python module creating GUI applications in Python. Creating various
widgets like button, canvas, label, entry, frame, check button, label etc. Geometry
Management: pack, grid, place, organizing layouts and widgets, binding functions,
mouse clicking events. Building the complete interface of a project.
(vii) Machine Learning : The Next Step What is Machine Learning? Types of Machine Learning Algorithms, Training the data
and Introduction to Various Learning Algorithms. Applications of Machine Learning.
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