Introduction to CORE Python

1. History and Background
2. Comparison with other Programming Languages
3. Installation and Environment Setup

• Working in IDLE and CMD
• Working in VS-Code
• Working in Jupyter Notebook (Anaconda)
• Working in Google Colaboratory (with Google Drive)

• Writing your First Python Program

Core Programming Fundamentals

1. Displaying a Message
2. Datatypes and Variables
3. String Operations
   • Concatenation
   • Using title(), upper(), lower(), lstrip(), rstrip(), strip()
4. Arithmetic Operations
   • Add, Subtract, Multiply, Divide, Modulus
   • Using ** and //
   • Using Math Functions [import math] : sqrt(), factorial(), pow(), pi

5. Conditional Statements with Relational and Logical Operators

• If… Else
• If… Elif… Else
• Nested if… Else

6. Loops and Ranges

• While loop
• range() and for loop
• Creating Patterns
• Using break, continue and pass statements

7. Basic Data Structures
1. List using [ ]

• Using append(), extend(), insert(), remove(), pop(), clear(), index(), count(), sort(), reverse(), copy(), len(), max(), min(), range()

2. Slicing a List using [_:_:_] notation
3. Tuples using ( )

4. Dictionary using { key : value }

• Using clear(), copy(), fromkeys(), get(), items(), keys(), popitem(), setdefault(), pop(), len(), del

5. Set using { }

• Using add(), remove(), discard(), union(), intersection(), isdisjoint(), difference()

Comprehension

• List Comprehension
• Dictionary Comprehension
• Set Comprehension

Working with Strings

• String representation and structure
• String input and output
• String Slicing
• String Functions

1. Using len(), str(), upper(), lower(), format(), find(), replace()
2. Using in and not in operators

Functions

• Definition and Types of Function
• Working with Global and Local Variables
• Anonymous Function

1. Lambda
2. Filter, map, reduce

• Recursive Function
• Using *args and **kwargs
• Generators

Modules and Packages

• Introduction to Modules
• Importing a module
• Working with math and os modules
• Working with datetime module

1. Using now(), today()

• Introduction to Packages
• Working with packages

Exception Handling

• Definition and Example of Exception
• Using try and except block
• Using raise keyword
• Using finally block
• Using try with else block

File Handling

• Definition
• Opening and Closing a File
• Writing and Reading a File
• File Methods

Object Oriented Programming

• Definition
• Classes and Objects
• Encapsulation
• Inheritance
• Polymorphism

Regular expressions

• Introduction to CFG
• Match Function
• Search Function
• Matching VS Searching
• Patterns

Introduction to Data Science

• What is analytics and data science?
• Overview of data science and analytics.
• Why analytics is becoming popular now?
• Application of analytics in business.
• Analytics vs data warehousing and MIS reporting.
• Various terminologies in analytics.
• How business are using the power of analytics?
• Various analytics tools and their usage.

Introduction to Data Science with Python

• Installing python anaconda distribution.
• Python native data types.
• Basic programming concepts.
• Python data science packages overview.

Python Basics: Basics Syntax, Data Structures

• Python objects
• Math and comparison operators
• Conditional statements
• Loops
• Functions
• Exception handling

Python Concepts (Core)

• Overview and history of python.
• Python installation.
• Introduction to python editors & IDE’S (canaopy, pycharm, jupyter, rodeo etc.)
• Understand jupyter notebook & customize settings.
• Concept of packages / libraries – important packages (numpy, scipy, scikit-learn, pandas, matplotlib etc).
• Installing & loading packages & name spaces.
• Data types & data objects/ structure (strings, tuples, lists, dictionaries).
• List & dictionary comprehensions.
• Variables & value labels – date, times & values.
• Basic operations – mathematical – string – date.
• Reading & writing data.
• Simple plotting.
• Control flow & conditional statements.
• Debugging & code profiling.
• How to create class & modules and how to call them ?

Numpy Package

• What is numpy?
• Importing numpy
• Numpy overview
• Numpy array creation & basic operation
• Numpy universal function.
•  Selecting & retrieving data.
• Data slicing
• Iterating numpy data.
• Shape manipulation.
• Stacking & splitting arrays.
• Copies and views : no copy, shallow copy, deep copy.
• Indexing : arrays of indices, Boolean arrays.

Introduction to Pandas

• Selecting data from pandas data frame.
• Slicing & dicing using pandas.
• Group by/ aggregate.
• Strings with pandas.
• Cleaning up messy data with pandas.
• Dropping entries.
• Selecting entries

Data Manipulation using Pandas

• Data alignment
• Sorting and ranking
• Summary statistics
• Missing values
• Merging data
• Concatenation
• Combining DataFrames.
• Pivot
• Duplicates
• Binning

Pandas Package

• Importing pandas
• Pandas overview
• Object creation: series object, data frame object
• View data
• Selecting data by label & position
• Data slicing
• Boolean indexing
• Setting data

Python Advance : Data Mugging with Pandas

• Applying functions data
• Histogramming
• String methods
• Merge data: concat, join & append
• Grouping & aggregation
• Reshaping.
• Analyzing data for missing values.
• Filling missing values : fill with constant, forward filling, mean.
• Removing duplicates
• Transforming data

Python Advance: Visualization with Matplotlib

• Anatomy of a matplotlib plot.
• Matplotlib basics plots and it’s containers.
• A matplotlib figure, it’s components and properties.
• Axes and other graphical objects.
• Pylab and pyplot.
• Data and matplotlib plots.
• What is subplot?
• Modifying size of figures.
• Plotting size of figures.
• Plotting routines with pyplot.
• Customizing your pyplot.
• Deleting an axes.
• Setting up plot title, axes labels, legend, layout.
• Showing, saving and closing your plot.
• Save a plot to an image file and pdf file.
• Use Cla (). Clf () or close

Statistics for Data Science

Introduction to statistics

• Two areas of statistics in data science.
• Applies statistics in business.
• Descriptive statistics.
• Inferential statistics.
• Statistics terms and definitions.
• Type of data
• Quantitative vs qualitative data
• Data measurement scales.

Harnessing Data

• Sampling data, with and without replacement.
• Sampling  methods, random vs non – random
• Measurement on samples.
• Random sampling methods.
• Simple random, stratified, cluster, systematic sampling.
• Biased vs unbiased sampling.
• Sampling error
• Data collection methods.

Exploratory Analysis

• Measures of central tendencies.
• Mean, median and mode
• Data variability : range, quartiles, standard deviation
• Calculating standard deviation.
• Z-score/ standard score
• Empirical rule
• Calculating percentiles.
• Outliners

Distributions

• Distribution introduction
• Normal distribution
• Central limit theorem
• Histogram – normalization
• Other distribution : poisson, binominal etc.
• Normality testing
• Skewness
• Kurtosis
• Measure of distance
• Euclidean, manhattan and minkowski distance.

Hypothesis and computational techniques

• Hypothesis testing
• Null hypothesis, p- value
• Need for hypothesis testing in business.
• Two tailed, left tailed and right tailed test.
• Hypothesis testing outcomes : type 1 &2 errors.
• Parametric test, t – tests : one sample, two sample, paired.
• One way ANOVA.
• Importance of parametric tests
• Non parametric tests : chi- square, mann – whitney, Kruskal- wallis etc.
• Which test to choose?
• Ascertaining accuracy of data

Correlation and Regression

• Introduction to regression
• Types of regression
• Hands on of a regression with python
• Correlation
• Weak and strong correlation
• Finding correlation with python .

Introduction to Machine Learning

• What is machine learning?
• Data science vs machine learning.
• Fundamentals of machine learning.
• Converting business problems to data problems.
• Understand supervised and unsupervised learning with examples.
• Understand biases associated with my machine learning algorithm.
• Drivers of machine learning algorithms.
• Cost functions.
• Brief introduction to gradient descent.
• Importance of model validation.
• Model performance metrics.
  

Unsupervised Learning

• K-means clustering : theory, Euclidean method
• K-means hands on with python.
• K-means advantages and disadvantages.
  

Supervised Learning

• Simple linear regression : implementing in python, working on use case.
• Multiple linear regression : implementing in python, working on use case.
• K- nearest neighbor: implementing in python, KNN advantages, working on use case.
• Decision trees: implanting in python, decision trees pros and cons, working on use case.
  

Advance Machine Learning Concepts

• Tuning with hyper parameters.
• Popular machine learning algorithms.
• Clustering, classification and regression.
• Supervised vs unsupervised.
• Choice of machine learning algorithms.
• Grid search vs random search cross validation.
  

Principle Component Analysis (PCA)

• Key concepts of dimensionality reduction.
• PCA theory
• Hands on coding
• Case study on PCA

Random Forest – Ensemble

• Key concepts of random forest.
• Hands on coding
• Pros and cons
• Case study on random forest

Support Vector Machine (SVN)

• Key concepts of support vector machine.
• Hands on coding.
• Pros and cons,
• Text processing with vectorization
• Sentiment analysis with textBlob.
• Twitter sentiment analysis.

Naïve Bayes Classifier

• Key concept of Naïve Bayes.
• Hands on coding
• Pros and cons
• Naïve Bayes for test classification.
• New articles tagging.

Recommender System

• User-Based Collaborative Filtering
• Itembased collaborative filtering
• Finding moviesimilarities
• Improving the results
• Making movie recommendations
• Improving the results

Apache SPARK - Machine Learning on Big Data

• Installing SPARK
• Introduction and Overview
• Resilient Distributed Dataset
• MLLib
• SPARK Decision trees
• K-Means Clustering
• TF / IDF
• Searching Wikipedia with SPARK