Instructor led Live Online Summer Training
Session Wise Schedule – Python , Data Science
| Sn. | Heading | Topic Name |
|---|---|---|
| 1 | Python Basic | Installation & Environment Setup Python Introduction Interactive Shell User interface or IDE |
| 2 | Variables & Strings in Python | What is Variable? Variables and Constants in Python, Variable, Variable names and Value, Mnemonic Variable Names, Strings |
| 3 | Python Operators | Arithmetic, Relational Operators and Comparison Operators, Python Assignment Operators, Short hand Assignment Operators, Logical Operators or Bitwise Operators, Membership Operators, Identity Operators, Operator precedence, Evaluating Expressions |
| 4 | Data Types in Python | ListTuple, |
| 5 | Data Types in Python | Dictionaries, Numbers, Sets |
| 6 | Conditional Statements | How to use “if condition” in conditional structures, if statement How to use “elif” condition, Nested IF Statement, Break, Continue & Pass Statement |
| 7 | Loops & User Input | While And For Loops, Nested For Loops, Iterations And Comprehensions |
| 8 | Functions | Function Definition And Call, Function Scope, Return Statement, Arguments |
| 9 | Anonymous Functions | Lambda Expression, Advance Functions |
| 10 | File Handling | Working with Files, CSV, PDF |
| 11 | Modules & Packages | Importing Modules, Standard Module –Sys , OSPackages |
| 12 | Exception Handling | Syntax Error, Runtime Error, Try except Statement, Finally statement |
| 13 | Python Advance Oops Concepts & Application | Classes and instances Inheritance and Compositions Static and Class Methods |
| 14 | Oops Concepts & Application | Operator Overloading Polymorphism, Iterators |
| 15 | Python Advance | Decorators , Generators |
| 16 | Regular Expressions | Match Function, Search Function, Grouping, Match Objects, Flags, Exercise |
| 17 | Multi threading | What is Multi-Threading, Threading Module, Defining a Thread, Thread Synchronization |
| 18 | Database Handling in Python | Working With Data Base, Connecting & Inserting Data to SQLite With Python |
| 19 | Web Scrapping | The components of a web page, Beautiful, Soup, HTML, CSS, JS, jQuery, Data frames, PIP Installing External Modules Using PIP |
| 20 | Projects | Building an Interactive Dictionary with Python |
| 21 | Projects | Food Ordering System with Python |
| 22 | Projects | Building a smart calculator desktop app using python |
| 23 | Projects | Online Book Store System Using Python |
| 24 | Projects | Scrapping a Real Estate Property data from the web |
| 25 | Projects | Creating a Website |
| 26 | Project | Creating a Blog Site |
| 27 | Data Science | What Data Science is, Why Data Scientists are in demand, What is a Data Product, The growing need for Data Science, Large Scale Analysis Cost vs Storage, Data Science Skills, Data Science Use Cases, Data Science Project Life Cycle & Stages, Data Acquisition |
| 28 | Statistics | What is Statistics, Descriptive Statistics, Central Tendency Measures, The Story of Average, Dispersion Measures, Data Distributions |
| 29 | Hypothesis Testing in Data Science | Central Limit Theorem, What is Sampling, Why Sampling, Sampling Methods, Inferential Statistics, What is Hypothesis testing, Confidence Level, Degrees of freedom |
| 30 | Advance Hypothesis | what is pValue, Chi-Square test, What is ANOVA, Correlation vs Regression, Uses of Correlation & Regression |
| 31 | Numpy | Learning NumPy |
| 32 | Pandas | Introduction to Pandas, Creating Data Frames, Grouping, Sorting |
| 33 | Data Analysis With Pandas | Plotting Data, Creating Functions, Slicing/Dicing Operations. |
| 34 | Visualization | Matplotlib, Working With Graphs |
| 35 | Exploratory_data_analysis | Working With Seaborn |
| 36 | Exploratory_data_analysis | Bi variate and Multi-variance analysis, Univariate analysis and outliers handling |
| 37 | Machine Learning | ML Fundamentals, ML Common Use Cases, Understanding Supervised and Unsupervised Learning Techniques |
| 38 | Probability | Introduction of PDF, RDF functions, Gaussian Distribution, Maximum Likelihood Estimation |
| 39 | Feature Engineering | Machine Learning Use-CasesMachine Learning Process FlowMachine Learning Categories |
| 40 | Working With Python For ML | Installation Of Jupyter Notebook |
| 41 | Linear Regression | Introduction to Predictive Modeling, Linear Regression Overview, Simple Linear Regression, Multiple Linear Regression |
| 42 | Optimization Algorithm | Gradient Descent, Stochastic Gradient Descent, Batch Gradient Descent |
| 43 | Assignment 1 | Linear Regression – Using kc housing Dataset |
| 44 | Logistic Regression | Logistic Regression Overview, Loss Function |
| 45 | Performance Measurment | Data Partitioning, Univariate Analysis, Bivariate Analysis, Multicollinearity Analysis, Model Building, Model Validation, Model Performance Assessment AUC & ROC curves, Scorecard |
| 46 | Use Case | MNIST Classification Using Logistic RegressionLogistic Regression – Using Titanic Dataset |
| 47 | KNN | kNN Introduction kNN Concepts kNN and Iris Dataset Demo Distance Metric |
| 48 | Naive Bayes Algorithm | What is Naïve Bayes?, How Naïve Bayes works?Implementing Naïve Bayes Classifier |
| 49 | Use Case | Text Classification Using Naïve Bayes Classifier, Tumor Classification |
| 50 | Decision Tree Classifier | How to build Decision trees, What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Decision Tree, Confusion Matrix |
| 51 | Use Case | Breast Cancer Diagnosis Using Decision Tree Classifier |
| 52 | Random Forest Classifier | What is Random Forests, Features of Random Forest, Out of Box Error Estimate and Variable Importance |
| 53 | Use Case | Breast Cancer Diagnosis Using Random Forest Classifier |
| 54 | Support Vector Machines | Case Study, Introduction to SVMs, SVM History, Vectors Overview, Decision Surfaces, Linear SVMs, The Kernel Trick, Non-Linear SVMs, The Kernel SVM |
| 55 | Use Case | SVM using Bike Sharing Dataset |
| 56 | Time Series Analysis | What is Time Series Analysis?, Importance of TSA, Components of TSA, White Noise, AR model, MA model, ARMA model, ARIMA model Stationarity ACF & PACF |
| 57 | Use Case | Checking Stationarity Converting a non-stationary data to stationary Implementing Dickey Fuller TestPlot ACF and PACF, Generating the ARIMA plot, TSA Forecasting |
| 58 | Problem Statement and Analysis | Various approaches to solve a Data Science ProblemPros and Cons of different approaches and algorithms. |
| 59 | Principal Component Analysis | Introduction to Dimensionality, Why Dimensionality Reduction, PCAFactor Analysis, Scaling dimensional model, LDA |
| 60 | Use Case | Face Recognition with Eigen faces |
| 61 | Unsupervised Learning Algorithm | What is Clustering & its Use Cases?, What is K-means Clustering?, How K-means algorithm works?, How to do optimal clustering |
| 62 | Use Case | Implementing K-means Clustering |
| 63 | Which Algorithms perform best | Highly efficient machine learning algorithms, Bagging Decision Trees, The power of ensembles, Random Forest Ensemble technique |
| 64 | Which Algorithms perform best | Boosting – Ada boost, Boosting ensemble stochastic gradient boosting, A final ensemble technique |
| 65 | Miscellaneous | Curse of dimensionality, Regularization methods:- Ridge, LASSO, Kernel density Estimation, Bias-variance trade-off, Over fitting, under fitting, Radial basis functions |
| 66 | Project | Big Mart Sales Analysis |
| 67 | Project | FIFA-2018-World-cup-predictions |
