Data Science
This data science training covers data handling, visualization, statistical modelling and machine learning effectively with practical examples and case studies making it one of the most practical Python online training. Even if you are looking for live Data Science oriented Python training in your college this is just the right course.
Course Features
 Students 307
 Duration4/6 week
 Skill levelall
 LanguageEnglish

DAY
DESCRIPTION
1
INTRODUCTION TO DATA SCIENCE WITH PYTHON
• 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?
1,2 and 3
PYTHON: ESSENTIALS (CORE)
• Overview of Python Starting with Python
• 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, scikitlearn, 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?
4
SCIENTIFIC DISTRIBUTIONS USED IN PYTHON FOR DATA SCIENCE
• 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
5 and 6
DATA MANIPULATION – CLEANSING – MUNGING USING PYTHON MODULES
• 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 Builtin 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)
7 and 8
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)
9 and 10
INTRODUCTION TO STATISTICS
• 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/ttests( One sample, independent, paired), Anova, Correlations and Chisquare
• Important modules for statistical methods: Numpy, Scipy, Pandas
10 and 11
INTRODUCTION TO PREDICTIVE MODELING
• 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
12
DATA EXPLORATION FOR 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
13 and 14
DATA PREPARATION
• Need of Data preparation
• Consolidation/Aggregation  Outlier treatment  Flat Liners  Missing values Dummy creation  Variable Reduction
• Variable Reduction Techniques  Factor & PCA Analysis
14 and 15
SEGMENTATION: SOLVING SEGMENTATION PROBLEMS
• 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 (KMeans Cluster Analysis)
• Cluster evaluation and profiling  Identify cluster characteristics
• Interpretation of results  Implementation on new data
16 and 17
LINEAR REGRESSION: SOLVING REGRESSION PROBLEMS
• Introduction  Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, Rsquare/Adjusted Rsquare, 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
18 and 19
LOGISTIC REGRESSION: SOLVING CLASSIFICATION PROBLEMS
• 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 Cutoffs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results  Business Validation  Implementation on new data
20 and 21
TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS
• 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
22,23 and 24
MACHINE LEARNING PREDICTIVE MODELING – BASICS
• 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 Preprocessing, Sampling, Model Building, Validation)
• Overfitting (BiasVariance Trade off) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Overview of gradient descent algorithm
• Overview of Cross validation(Bootstrapping, KFold validation etc)
• Model performance metrics (Rsquare, Adjusted Rsqure, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
24 and 25
UNSUPERVISED LEARNING: SEGMENTATION
• What is segmentation & Role of ML in Segmentation?
• Concept of Distance and related math background
• KMeans Clustering
• Expectation Maximization
• Hierarchical Clustering
• Spectral Clustering (DBSCAN)
• Principle component Analysis (PCA)
•
26 and 27
SUPERVISED LEARNING: DECISION TREES
• Decision Trees  Introduction  Applications
• Types of Decision Tree Algorithms
• Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each NonLeaf 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
28 and 30
SUPERVISED LEARNING: ENSEMBLE LEARNINSG
• 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
31 and 32
SUPERVISED LEARNING: ARTIFICIAL NEURAL NETWORKS (ANN)
• 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
33 and 34
SUPERVISED LEARNING: SUPPORT VECTOR MACHINES
• Motivation for Support Vector Machine & Applications
• Support Vector Regression
• Support vector classifier (Linear & NonLinear)
• Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating SVM models
34 and 35
SUPERVISED LEARNING: KNN
• 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
36 and 37
SUPERVISED LEARNING: NAÏVE BAYES
• Concept of Conditional Probability
• Bayes Theorem and Its Applications
• Naïve Bayes for classification
• Applications of Naïve Bayes in Classifications
37 and 38
TEXT MINING & ANALYTICS
• Taming big text, Unstructured vs. Semistructured Data; Fundamentals of information retrieval, Properties of words; Creating TermDocument (TxD);Matrices; Similarity measures, Lowlevel processes (Sentence Splitting; Tokenization; PartofSpeech 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 KMeans/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.
Last 7 Days participants will guided in the projects
CASE STUDIES
1. Core Python and Pandas Exercises
Solving different exercises related data importing, data manipulation, data processing, data visualization, data exploratory analysis (Univariate, BiVariate analysis) etc using different packages like Pandas, Numpy, matplotlib, seaborn
2
2. Pandas case study
1. Solving different problems related customer analytics using pandas package 2. Understand the customers spend & repayment behavior and Evaluate areas of bankruptcy, delinquency, and collections etc using pandas packages
3
3. Visualization case study
Perform different graphical analysis (bar chart, pie chart, box plot, histogram, stacked charts, heat maps, scatter plots, panel charts etc) for solving different business problems
4. Credit Card Customers Segmentation
A credit card company wishes to understand its customer behavior so to have an enriched customer profile by having intelligent KPI’s. The idea is to apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.
5
5. Proactive Attrition Management
A wireless telecom companies wants to reduce customer churn by developing a proactive churn management model. The idea is to build a logistic regression based predictive model to develop an incentive plan for enticing wouldbe churners to remain with the company.
6
6. Predicting Loan Default
A bank would like to build credit risk model (application score card using PD models) to accept/ reject applications for loans. Also it wants to understand the key drivers for default or delinquency.
7
7. Key Drivers for Customer credit card spending
The objective of this case study is to understand what's driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow endtoend model building process and help set the credit limit and designing new product offerings.
8
8. Time Series Forecasting
Use time series analysis to forecast the outbound passenger movement for next few quarters.
9
9. Sentiment Analysis
Objective of this analysis is to obtain data from Twitter and check how the sentiment varies by country for a particular brand/keyword/company
.
10. Social Media Analytics Case Study
Objective of this analysis is to obtain the data from social media platforms like Twitter/Facebook/Youtube etc and perform different analysis using text mining and Machine learning techniques
PROJECT  CONSOLIDATE LEARNINGS :
Project #1: Real Estate Price Prediction
Industry: Business Intelligence and Analytics
Description: The goal of this Usecase is to make predictions using Real Estate market data. The dataset contains the of the price of apartments in Boston. This data contains values such as "crime rate", "age", "accessibility", "population" etc. Based on this data, decide on the price of new apartments.
Project #2: Recommendation System for Grocery Store
Industry: Food Retail Industry
Description: The UseCase scenario is to create recommendations for customers of a grocery store based upon historical transaction data, which could recommend preferable articles.
Project #3: Air Passengers Forecasting
Industry: Commercial Aviation
Description: This UseCase is about analyzing the data and applying time series model to forecast the number of bookings an Airline firm can expect each month. The dataset we will analyze contains monthly totals of international airline passengers between 1949 to 1960.You have to make informed decisions on staffing, hospitality and pricing for tickets.
Project#4: Movies Collection
Industry: Entertainment Industry
Description: The goal of this UseCase is to explore the movie dataset, given the parameters like: "duration", "movie title", "gross collection", "budget", "title year", etc. You will explore the following:
· Know top ten movies with the highest profits.
· Know top rated movies in the list and average IMDB score.
· Plot a graphical representation to show the number of movies released each year.
· Group the movies into clusters based on the Facebook likes.
· Group the directors based on movie collection and budget.
Course Name: Data Science with Python
Student
8,500
6000
14,500
Professional
9,500
6,500
16,000
To enroll in a course:
1. Click Registration Form.2. Fill each and every details in the form and submit it.
3. After successful registration you will get a confirmation mail from Teach Tech Services.
To deposit your course fee
1. Click on Pay Now.2. After successful payment our team member will contact you within 3 hours.
Certification:
All participants will get ISO certified Certificate of the course from Teach Tech Services in association with iSmriti, IIT Kanpur
This certificate is globally accepted.
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