Introduction to Python
Two Day Course

Getting started with Python 
•Introduction to Python Programming Interfaces
•Understanding data types
•Understanding data structures
Importing data in Python
•Flat files
•Other files
•Relational databases
•Web 
Data Preparation
•Foundation of pandas
•Reshape, rearrange, transform
•Cleaning
•Combining
•Data pre-processing 
Data Exploration  
•Numeric statistics with panda and numpy
Visualization in Python using Matplotlib 
•Customizing plots
•Statistical plots

Introduction to Machine Learning Using Python
Two Day Course

Introduction to Machine Learning
•Intro to machine learning
•Types of machine learning algorithms
•Understandingthe basics 
• Steps in the model  building, testing and scoring
•` Model Evaluation Metrics
Supervised learning algorithms 
•Understanding Linear and non-linear  algorithms
•Understanding Regression and  Classification
Linear  Algorithms
•Simple linear regression
•Multiple linear regression
•Logistic regression
Non-linear Algorithms 
•k nearest neighbors
•Classification and regression trees
•Naïve byes
•Neural networks
Ensemble Algorithms 
•Random forest
•Gradient boosting
Unsupervised learning algorithms 
•Understanding Clustering and  Association
   *k -means Clustering
   *MBA using Word2Vec
Introduction to Deep Learning
•Introduction to Deep Learning

Advanced Machine Learning using Python & Tensorflow
Three Day Course

Linear Algorithms
Multiple linear regression
Logistic regression
Non-linear Algorithms
Classification And Regression Trees
Naïve byes
Ensemble Algorithms
Random forest
Getting started with TensorFlow
Introduction to TensorFlow
Installing TensorFlow Environment
Fundamentals of TensorFlow
Model building with TensorFlow
Build and train a model with TensorFlow
Doing regression with TensorFlow
Doing classification with TensorFlow
Introduction to Neural Networks
Forward and backward propagation
Introduction to Perceptron
Neural Network Activation Functions
Cost Functions
Introduction to Deep Learning
Introduction to Deep Learning
Convolution Neural Network
Recurrent Neural Network

INTRODUCTION TO DATA SCIENCE WITH PYTHON 

Two Day course 16 hours

 

  • What is analytics & Data Science?

  • Common Terms in Analytics

  • Analytics vs. Data warehousing, OLAP, MIS Reporting

  • Relevance in industry and need of the hour

  • Types of problems and business objectives in various industries

  • How leading companies are harnessing the power of analytics?

  • Critical success drivers

  • Overview of analytics tools & their popularity

  • Analytics Methodology & problem-solving framework

  • List of steps in Analytics projects

  • Identify the most appropriate solution design for the given problem statement

  • Project plan for Analytics project & key milestones based on effort estimates

  • Build Resource plan for an analytics project

  • Why Python for data science?

 

 

 

 

 

 

PYTHON: ESSENTIALS (CORE)

Two Day Course

 

  • Overview of Python- Starting with Python

  • Introduction to the installation of Python

  • Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)

  • Understand Jupyter notebook & Customize Settings

  • The concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)

  • Installing & loading Packages & Name Spaces

  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)

  • List and Dictionary Comprehensions

  • Variable & Value Labels –  Date & Time Values

  • Basic Operations - Mathematical - string - date

  • Reading and writing data

  • Simple plotting

  • Control flow & conditional statements

  • Debugging & Code profiling

  • How to create class and modules and how to call them?

 

 

 

 

 

DATA MANIPULATION – CLEANSING – MUNGING USING PYTHON MODULES

Two Day Course

 

Cleansing Data with Python

  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)

  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)

  • Python Built-in Functions (Text, numeric, date, utility functions)

  • Python User Defined Functions

  • Stripping out extraneous information

  • Normalizing data

  • Formatting data

  • Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

 

 

 

 

 

 

DATA ANALYSIS – VISUALIZATION USING PYTHON

  • Introduction exploratory data analysis

  • Descriptive statistics, Frequency Tables and summarization

  • Univariate Analysis (Distribution of data & Graphical Analysis)

  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

 

 

 

 

 

 

INTRODUCTION TO STATISTICS

Two Day Course

 

  • Basic Statistics - Measures of Central Tendencies and Variance

  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem

  • Inferential Statistics -Sampling - Concept of Hypothesis Testing

  • Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

  • Important modules for statistical methods: Numpy, Scipy, Pandas

 

 

 

 

 

INTRODUCTION TO PREDICTIVE MODELING

Two Day Course

 

  • Concept of model in analytics and how it is used?

  • Common terminology used in analytics & modeling process

  • Popular modeling algorithms

  • Types of Business problems - Mapping of Techniques

  • Different Phases of Predictive Modeling

 

 

 

 

DATA EXPLORATION FOR MODELING

Two Day Course

 

  • Need for structured exploratory data

  • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

  • Identify missing data

  • Identify outliers data

  • Visualize the data trends and patterns

 

 

 

 

 

SEGMENTATION: SOLVING SEGMENTATION PROBLEMS

Two Day Course

 

  • Introduction to Segmentation

  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)

  • Cluster evaluation and profiling - Identify cluster characteristics

  • Interpretation of results - Implementation on new data

 

 

 

 

 

LINEAR REGRESSION: SOLVING REGRESSION PROBLEMS

Two Day Course

 

  • Introduction - Applications

  • Assumptions of Linear Regression

  • Building Linear Regression Model

  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)

  • Assess the overall effectiveness of the model

  • Validation of Models (Re running Vs. Scoring)

  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)

  • Interpretation of Results - Business Validation - Implementation on new data

 

 

 

 

LOGISTIC TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

Two Day Course

 

  • Introduction - Applications

  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

  • Classification of Techniques(Pattern based - Pattern less)

  • Basic Techniques - Averages, Smoothening, etc

  • Advanced Techniques - AR Models, ARIMA, etc

  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

 

 

 

 

MACHINE LEARNING -PREDICTIVE MODELING – BASICS

Two Day Course

 

  • Introduction to Machine Learning & Predictive Modeling

  • Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting

  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

  • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

  • Overfitting (Bias-Variance Trade off) & Performance Metrics

  • Feature engineering & dimension reduction

  • Concept of optimization & cost function

  • Overview of gradient descent algorithm

  • Overview of Cross validation(Bootstrapping, K-Fold validation etc)

  • Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

 

 

 

 

UNSUPERVISED LEARNING: SEGMENTATION

Two Day Course

 

  • What is segmentation & Role of ML in Segmentation?

  • Concept of Distance and related math background

  • K-Means Clustering

  • Expectation Maximization

  • Hierarchical Clustering

  • Spectral Clustering (DBSCAN)

  • Principle component Analysis (PCA)T - CONSOLIDATE LEARNINGS :

Applying different algorithms to solve the business problems and bench mark the results

© 2018 by The Enigma Group of Companies

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