⇒ What 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?
⇒ Overview of Python- Starting with Python
⇒ Introduction to installation of Python
⇒ Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
⇒ Python Syntax
⇒ Variables & Data Types
⇒ Operators
⇒ Conditional Statements
⇒ Working With Numbers & Strings
⇒ Collections API
⇒ LISTS
⇒ TUPLES
⇒ DICTIONARY
⇒ Date and Time
⇒ Function & Modules
⇒ File handling
⇒ Exception Handling
⇒ OOPS Concepts in python
⇒ Regular Expression
⇒ Numpy
⇒ Scify
⇒ Pandas
⇒ scikitlearn
⇒ statmodels
⇒ nltk
⇒ 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, beautiful soup
⇒ 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.)
⇒ Data visualization with tableau
⇒ 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
⇒ 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 etc
⇒ Applying different algorithms to solve the business problems and bench mark the results
Duration
2-3 MonthsAvailable Seats
15Online Training Schedule
8.00 pm to 10.00 pmIndustrial Training Schedule in Chandigarh
8.00 am to 7.00 pm