Python with Data Science Training
Python with Data Science training lets you master the concepts of the widely used and powerful programming language, Python. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, NumPy, Matplotlib which are essential for Data Science. You will work on real-world projects in the domain of Python and apply it for various domains of Big Data, Data Science and Machine Learning.
Why Should you take Python with Data Analytics Training?
• Python is the preferred language for new technologies such as Data Science and Machine Learning.
• Average salary of Python Certified Developer is $123,656 per annum - Indeed.com
• Python is by far the most popular language for data science. Python held 65.6% of the data science market.
Curriculum
Introduction to Python
• Define Python
• Understand the need for Programming
• Know why to choose Python over other languages
• Setup Python environment
• Understand Various Python concepts – Variables, Data Types Operators, Conditional Statements and Loops
• Illustrate String formatting
• Understand Command Line Parameters and Flow control
Python Environment Setup and Essentials
• Python installation
• Windows, Mac & Linux distribution for Anaconda Python
• Deploying Python IDE
• Basic Python commands, data types, variables, keywords and more
Python language Basic Constructs
• Looping in Python
• Data Structures: List, Tuple, Dictionary, Set
• First Python program
• Write a Python Function (with and without parameters)
• Create a member function and a variable
• Tuple
• Dictionary
• Set and Frozen Set
• Lambda function
OOP (Object Oriented Programming) in Python
• Object-Oriented Concepts
Working with Modules, Handling Exceptions and File Handling
• Standard Libraries
• Modules Used in Python (OS, Sys, Date and Time etc.)
• The Import statements
• Module search path
• Package installation ways
• Errors and Exception Handling
• Handling multiple exceptions
Introduction to NumPy
• Introduction to arrays and matrices
• Indexing of array, datatypes, broadcasting of array math
• Standard deviation, Conditional probability
• Correlation and covariance
• NumPy Exercise Solution
Introduction to Pandas
• Pandas for data analysis and machine learning
• Pandas for data analysis and machine learning Continued
• Time series analysis
• Linear regression
• Logistic Regression
• ROC Curve
• Neural Network Implementation
• K Means Clustering Method
Data Visualisation
• Matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots - bar graphs, pie charts, histograms
• Contour plots
Data Manipulation
• Perform function manipulations on Data objects
• Perform Concatenation, Merging and Joining on DataFrames
• Iterate through DataFrames
• Explore Datasets and extract insights from it
Scikit-Learn for Natural Language Processing
• What is natural language processing, working with NLP on text data
• Scikit-Learn for Natural Language Processing
• The Scikit-Learn machine learning algorithms
• Sentimental Analysis – Twitter
Introduction to Python for Hadoop
• Deploying Python coding for MapReduce jobs on Hadoop framework.
• Python for Apache Spark coding
• Deploying Spark code with Python
• Machine learning library of Spark MLlib
• Deploying Spark MLlib for Classification, Clustering and Regression
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