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Data Science with Python and R Course Content

Overview

Data Science is Study and evaluation of data using mathematics, statistics and computer science. Used to extract key information for top management that helps in strategic business making, product development, forecasting and trend analysis. Organize and analyze large amounts of data, using specially designed software tools. Python and R are widely used in Data Science. Python is a general purpose language and R is for statistical computing.

Training Objective (What you will learn)

Develop in depth understanding in data science and business analytics. Covers machine learning, data mining, predictive modeling, visualization techniques and statistics. Helps in applying quantitative modelling and data analysis techniques on huge transactional data. Knowledge of statistical data analysis techniques to bring out information that help in business decision making. Provide required expertise in Python and R languages.

Prerequisites

It is a vast field based on multiple fields. Should have strong exposure to Statistics, Mathematics and programming knowledge. Experience in data analytics or hands on with any ETL tool will be added advantage. Basics of Machine learning and deep learning will help as they are integral part of data science.

Market Demand

Demand for data scientists in current market is only beginning. With AI and big data demand for data science is growing very rapidly.

  • 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 analytics project
  • Why Python for data science?
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython 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/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?
  • Numpy, scify, pandas, scikitlearn, statmodels, nltk etc
  • Accessing / Importing and Exporting Data Using Python Modules
  • Importing Data from various sources (Csv, txt, excel, access etc)
  • Database Input (Connecting to database)
  • Viewing Data objects - subsetting, methods
  • Exporting Data to various formats
  • Important python modules: Pandas, beautifulsoup
  • 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)
  • 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)
  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Security Audit
  • Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  • Important modules for statistical methods: Numpy, Scipy, Pandas
  • 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
  • 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
  • Need of Data preparation
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • 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
  • 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
  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model (Binary Logistic Model)
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  • Interpretation of Results - Business Validation - Implementation on new data
  • 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
  • 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 )
  • 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)
  • Decision Trees - Introduction - Applications
  • Types of Decision Tree Algorithms
  • Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
  • Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
  • Decision Trees - Validation
  • Overfitting - Best Practices to avoid
  • Concept of Ensembling
  • Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling (Stacking, Mixture of Experts)
  • Bagging (Logic, Practical Applications)
  • Random forest (Logic, Practical Applications)
  • Boosting (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost
  • Motivation for Neural Networks and Its Applications
  • Perceptron and Single Layer Neural Network, and Hand Calculations
  • Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
  • Neural Networks for Regression
  • Neural Networks for Classification
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating ANN models
  • Motivation for Support Vector Machine & Applications
  • Support Vector Regression
  • Support vector classifier (Linear & Non-Linear)
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating SVM models
  • What is KNN & Applications?
  • KNN for missing treatment
  • KNN For solving regression problems
  • KNN for solving classification problems
  • Validating KNN model
  • Model fine tuning with hyper parameters
  • Concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naïve Bayes for classification
  • Applications of Naïve Bayes in Classifications
  • Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
  • Finding patterns in text: text mining, text as a graph
  • Natural Language processing (NLP)
  • Text Analytics – Sentiment Analysis using Python
  • Text Analytics – Word cloud analysis using Python
  • Text Analytics - Segmentation using K-Means/Hierarchical Clustering
  • Text Analytics - Classification (Spam/Not spam)
  • Applications of Social Media Analytics
  • Metrics(Measures Actions) in social media analytics
  • Examples & Actionable Insights using Social Media Analytics
  • Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
  • Fine tuning the models using Hyper parameters, grid search, piping
  • Data Types
  • Introduction to Data Science Tools
  • Statistics
  • Approach to Business Problems
  • An overview of Analytics
  • Models and Algorithms
  • Importance of Data Science
  • Data Science as a Strategic Asset
  • R, Python, WEKA, RapidMiner
  • Overview of R
  • Introduction to R
  • Data Structures and Manipulation in R
  • Installing R on Vsrious Operating Systems
  • IDEs for R
  • Steps in R Initiation
  • Installing an R Package
  • Overview of Business Analytics
  • Introduction to Business Analytics
  • Analytics Technology and Resources
  • Need of Business Analytics
  • Types and Features of Business Analytics
  • Descriptive and Predictive Analytics
  • Business Decisions
  • Analytical Tools
  • Data Science as a Strategic Asset
  • Introduction
  • Types of Graphics
  • Save Graphics to a File
  • Graphics in R
  • Create a word cloud
  • Exporting Graphs in EStudio
  • Need of Hypothesis Testing in Businesses
  • Chances of Errors in Sampling
  • Level of Significance
  • Types of Statistical Hypothesis Tests
  • Test Statistic
  • Types of Errors
  • Use Normal and Student Probability Distribution Functions
  • Objectives of Null Hypothesis Test
  • Use Chi-Squared Test Statistics
  • Introduction to Regression Analysis and Usage
  • Types of Regression Analysis
  • Interaction Regression Model
  • Correlation
  • Logit Function
  • Lift charts
  • Decile Analysis
  • Introduction to Cluster Techniques
  • Examples of Classification
  • Classification Process - Model Construction
  • Data Preparation Issues
  • Basic Algorithm for a Decision Tree
  • Decision Trees in Data Mining
  • Naive Bayes Classifier
  • Clustering Models
  • Use Cases of Clustering
  • DBSCAN Clustering Algorithm
  • Distance Methodologies
  • Hierarchical and Non-Hierarchical Procedure
  • K-Means clustering
 
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