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- To employ Python packages for data analysis applications
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Introductio of Python
Overview of Python
Why Python ?
When to use Python?
Setting Environment- Install Python Windows, UNIX, Linux.
Running basic Python commands.
Basic Syntax/ Construct of Python
“Programming(interactive/script),identifiers,Reserved words, line/indentation,multi-
Accessing/Parsing command -line arguments.
Python Variable Types:
Variables and Naming rules
Built-in Data Types in Python – Numeric: int, float, complex.
Sequence Types: list, tuple, range
Text Sequence: Str (String).
Set Types: Set, Forzenset
Mapping Types: Dictionary
Data type Conversions between built-in types
Constants: False, True, None, NotImplemented, Ellipsis,debug.
Python Basic Operators:
Basic Operators: Arithmetic, Comparison, Assignment, Identity, Logical, Bitwise, Membership.
Python Operators Precedence: highest to lowest
Python Decision Making:
if, else, nested if, range(), break, continue, elif, Single Statement Suites.
while, for, Iterating by Sequence Index, nested, Loop Control Statements, break.
Built-in Functions : len(),slice(),zip() ,random()etc
User Defined Functions : How to create / call a function ,Function arguments
“Anonymous Function: Lambda”
“Accessing Values in Strings, Updating Strings, Escape Characters, String Formatting
Operator, Built-in String Methods.”
“Accessing Values in Lists, Basic List Operations, Built-in List Functions & Methods.”
List Slicing ,List comprehension ,sorting,deletion
Python Sets & Tuples :
Python Sets and Tuples and its operations.
Python Dictionaries :
Accessing Values in dictionary, Basic dictionary Operations, Built-in dictionary Functions & Methods.
Python Modules & Packages :
Exploring Built-in modules, writing modules
Packages and create your own packages
Creating Classes, Class Inheritance, Objects, and Instances
Encapsulation of data, Functions vs Methods
Iterators, Generators and its expressions
Python Errors & Exceptions :
Syntax Errors, Exceptions
Handling and Raising an exception, User-Defined Exception
Python Standard Libraries :
Operating System(OS) Interface, Command Line arguments
Regular Expression (String Pattern matching)
Date and Time, Mathematics
Networking: Sending Email, Multithreading, GUI Programming.
Python File Handling
Open a File, Read from a File, Write into a File, File Position, Looping over a file object.
Pickle (Serialize and Deserialize Python Objects).
Shelve (Python Object Persistence)
NumPy(Mathematical computing with python)
“Arrays and Matrices, ND-array object, Array indexing, Datatypes, Array math
Broadcasting, Std Deviation, Conditional Prob, Covariance, and Correlation.”
SciPy(Scientific computing with python)
Builds on top of NumPy, SciPy and its characteristics, sub-packages.
Cluster, ﬀtpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPy.
Data Visualization (Matplotlib)
Plotting Graphs and Charts (Line, Pie, Bar, Scatter, Histogram, 3-D).
The Matplotlib API.
Data Analysis and Data Manipulation with Python (Pandas)
Data frames, NumPy array to a data frame.
Import Data (csv, json, excel, sql database).
“Data operations: View, Select, Filter, Sort, Group, Cleaning, Join/Combine,
Handling Missing Values.”
“Introduction to Machine Learning(ML): Definition, Concepts and Terminology, Lifecycle ”
Problem categories of ML : Classification ,Clustering,Regression,Optimization
Learning Sub-Fields: Supervised, Unsupervised, Semi-Supervised, Reinforcement, Deep learning
“Basic Performance measures: MSE, MAE, NMSE, ROC / AUC, Confusion Matrix
Accuracy, Precision / Recall etc.”
Installation of Python Packages for ML and Setting up Environment ( Anaconda Distribution)
Supervised Algorithms: Linear, Logistic, CART, Naïve Bayes, KNN, Decision Tree, Random Forest, SVM and with the case study(for all)
Unsupervised Algorithms: K-Means, PCA and with the case study(for all)
“Natural Language Processing, Machine
Introduction to Natural Language Processing (NLP) ~ NLTK package
Text Processing: Tokenization, Stemming, Lemmatization, Stop word removal
Text Feature Engineering: Syntactical Parsing, Entity Parsing, Statistical Features
Text Mining with Python ( web scraping)
Sentiment Analysis with the Twitter case study
Advanced Topics :
Deep Learning ( Tensorflow ) Overview
Computer Vision Overview
Important Scientific Research Papers and IBM Certification
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