Python

• Python Environment Setup and Essentials
  • Introduction to Python Language, features, the advantages of Python over other programming languages, Python installation, Windows, Mac & Linux distribution for Anaconda Python, deploying Python IDE, basic Python commands, data types, variables, keywords and more.
  • Hands-on Exercise – Installing Python Anaconda for Windows, Linux, and Mac.
• Python language Basic Constructs
  • Built-in data types in Python, tabs and spaces indentation, code comment Pound # character, variables and names, Python built-in data types, Numeric, int, float, complex, list tuple, set dict, containers, text sequence, exceptions, instances,  classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug, basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise, loop and control statements, while, for, if, break, else, continue.
  • Hands-on Exercise – Write your first Python program Write a Python Function (with and without parameters) Use Lambda expression Write a class, create a member function and a variable, Create an object Write a for loop to print all odd numbers
• OOP concepts in Python and database connection
  • How to write OOP concepts program in Python, connecting to a database, classes and objects in Python, OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation, Python functions, return types, and parameters, Lambda expressions, connecting to the database and pulling the data.
• NumPy for mathematical computing
  • Introduction to arrays and matrices, indexing of array, datatypes, broadcasting of array math, standard deviation, conditional probability, correlation, and covariance.
  • Hands-on Exercise – How to import NumPy module, creating arrays using ND-array, calculating standard deviation on an array of numbers, calculating the correlation between two variables.
• SciPy for scientific computing
  • Introduction to SciPy and its functions, building on top of NumPy, cluster, linalg, signal, optimize, integrate, sub packages, SciPy with Bayes Theorem.
  • Hands-on Exercise – Importing of SciPy, applying the Bayes theorem on the given dataset
• Matplotlib for data visualization
  • How to plot graph and chart with Python, various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.
  • Hands-on Exercise – deploying MatPlotLib for creating Pie, Scatter, Line, Histogram.

• Pandas for data analysis and machine learning
  • Introduction to Python data frames, importing data from JSON, CSV, Excel, SQL database, NumPy array to a data frame, various data operations like selecting, filtering, sorting, viewing, joining, combining, how to handle missing values, time series analysis, linear regression.
  • Hands-on Exercise – working on importing data from JSON files, selecting record by a group, applying the filter on top, viewing records, analyzing with linear regression, and creation of time series.
• Scikit-Learn for Natural Language Processing
  • What is natural language processing, working with NLP on text data, setting up the environment using Jupyter Notebook, analyzing a sentence, the Scikit-Learn machine learning algorithms, bags of words model, extracting feature from the text, searching a grid, model training, multiple parameters, building of a pipeline?
  • Hands-on Exercise – setting up the Jupyter notebook environment, loading of a dataset in Jupyter, algorithms in Scikit-Learn package for performing machine learning techniques, training a model to search a grid.
• Web scraping with Python
  • Introduction to web scraping in Python, the various web scraping libraries, beautiful soup, Scrapy Python packages, installing of beautifulsoup, installing Python parser lxml, creating soup object with input HTML, searching of the tree, full or partial parsing, output print, searching the tree.
  • Hands-on Exercise – Installation of Beautiful soup and lxml Python parser, making a soup object with input HTML file, navigating using Py objects in soup tree.
• Python deployed for Hadoop
  • Introduction to Python for Hadoop, the basics of the Hadoop ecosystem, Hadoop common, the architecture of MapReduce and HDFS, deploying Python coding for MapReduce jobs on Hadoop framework.
  • Hands-on Exercise – How to write a MapReduce job with Python, connecting to the Hadoop framework and performing the tasks.
• Python for Apache Spark coding
  • Introduction to Apache Spark, the importance of RDD, the Spark libraries, deploying Spark code with Python, the machine learning library of Spark MLlib, deploying Spark MLlib for classification, clustering, and regression.
  • Hands-on Exercise – How to implement Python in a sandbox, working with the HDFS file system.
• Python Projects

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