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Python with Data Science : Syllabus

Data Science using Python

  • Difference Between Data analytics & Data Science?
  • Why and How python used in Data Analytics and Data Science?
  • What is Cubes – OLAP Framework in Python
  • What is Warehousing and DBMS
  • Data Analytics vs. Data warehousing, OLAP, Extract Transform Load (ETL), MIS Reporting
  • What are the problems and business objectives in different industries?
  • How leading companies are using the power of analytics?
  • Critical success Factor for AI
  • What are the analytics tools & their popularity?
  • What are Analytics Methodology & its framework?
  • What are Analytics projects?

Python Core Concept

  • What is Python
  • Introduction to installation of Python
  • Overview of Python Editors & IDE’s(Rodeo, Canopy, Jupyter , pycharm, python)
  • How to work on Jupiter Notebook and customize settings.
  • Basic Syntax, Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • Conditional Statement (If, Else-If or Nested If Else If)
  • Variable & Labels – Date & Time Values
  • Looping with Python (For Loop, While Loop or Nested Loop)
  • Control Statement in Python (Break Continue or Pass)
  • String Manipulation in Python: – Basic Operators, Method, Function etc.
  • Function and Modules (Importing, Installation, Packages with Version handling, Composition)
  • Input and Output Module in Python
  • Exception Handling in Python
  • OOPs Concept in Python
  • Classes, Inheritance, Overloading, Overriding, Data Hiding in Python.
  • Regular Expression in Python : – Match, Search, Modifier or Pattern
  • CGI Concept in Python : – Get and Post Method, CGI Enviroment Variable, Cookies, Upload, Get and Post Method
  • Database Handling in Python :- Connections, Transitions, Execution and Error Handling in Python
  • Networking: – Socket, Method, Internet Modules
  • Multithreading in Python : – Thread, Multithreaded
  • GUI Programming: – Tkinter and Widgets
  • Important Packages of Python : – NumPy, SciPy, Seaborn, scikit-learn, Pandas, Matplotlib, etc
  • Reading and writing data
  • Simple Graph plotting
  • Debugging & Code profiling

Python Syllabus and Upcoming Batches

Python & Advance Python Web Development with Python

Scientific Distribution

  • Numpy, Scify, Seaborn, Pandas, Scikitlearn, Statmodels, Nltk, MetplotLib etc

How to Import and Export Data by using python modules

  • How to Import Data from different sources?
  • Connection setup with database
  • Viewing Data objects – sub setting, methods
  • Export Data to different formats
  • Most Important python modules: Pandas, beautiful soup

Data Manipulation in Python and its Techniques

  • What is Manipulation in Python.
  • What are important Modules to Manipulate Data?
  • Cleaning and Prepping Data using Python.
  • Detecting Missing Values using python during cleaning.
  • What are Map and Data Library?
  • Data Manipulation steps: – Sorting, filtering, merging, duplicates, appending, sub setting, sampling, derived variables, Data type conversions, renaming, formatting etc)
  • Data manipulation Tools : – Functions, Operators, Packages, Control Structures, Loops, Arrays, Method etc
  • Python Built-in Functions
  • Python User Defined Functions and Classes.
  • How to stripping out extraneous information
  • Normalizing and Formatting data
  • Important Python modules for data.

Perform Data Visualization in Python.

  • What is EDA (Exploratory Data Analysis) in Python?
  • Descriptive statistics, Frequency Tables and summarizing Data.
  • Univariate and Multivariate Analysis (Data Distribution & Graphical Analysis)
  • Bivariate Analysis in Statistics (Distributions & Relationships, Cross Tabs, Graphical Analysis)
  • How we Plot Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density…
  • Important Packages for Exploratory Analysis (NumPy Arrays, Scipy, Matplotlib, seaborn, and Pandas etc)

Statistics using Python

  • What is Statistics in Python. How we calculate Mean, Median, Mode, Central Tendencies and R-Squared or Adj R-Squared using python.
  • Probability Distribution in python, Normal Distribution and Central Theorem using Python.
  • Statistics Concept and Hypothesis Concept of Testing
  • What is KS Test or Z Test . How to Calculate P Value using Distribution?
  • What is Anova, Correlations and Chi-square
  • Important modules used for statistical methods: Numpy, Scipy, Pandas

Predictive Analysis using python framework.

  • What is model in analytics and how it is used?
  • What are the best algorithm for prediction?
  • What is Data Modeling in Python?
  • Common Techniques used for analytics & modeling process
  • Most Popular modeling algorithms
  • Phases of Predictive Modeling

Data Exploration

  • What is Data Exploration and why it important?
  • EDA Methods and How it used in Machine Learning.
  • Common EDA framework for exploring the data and identifying problems with the data.
  • How to identify missing data?
  • How to identify outlier’s data?
  • Method to visualize the data trends and patterns.

Data Preparation

  • Data Preparation for Machine Learning.
  • Consolidate and Aggregation of Missing Values, Dummy Creation, Variable Reduction, Outlier Treatment.
  • Variable Reduction, Data Reduction Techniques, Principal Component Analysis, Improve Accuracy.

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 outlier’s data
  • Visualize the data trends and patterns

Segmentation in Machine Learning

  • What is Segmentation?
  • Edge Based Segmentation, Image Based Segmentation or Region Based Segmentation.
  • Different types of Segmentation (Subjective vs Objective, Heuristic vs. Statistical)
  • Heuristic Segmentation Techniques.
  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling
  • Implementation on new data

Linear Regression using Python

  • Introduction & Assumptions on Linear Regression
  • How to build Linear Regression Model
  • Standard metrics (R-Square/Adjusted R-Square, Global hypothesis, Variable significance, etc.)
  • Framework to assess the overall effectiveness of the model
  • Statistical Model Validation (Re running Vs. Scoring)
  • Business Outputs (Error distribution (histogram), Decile Analysis, Model equation, drivers etc.)
  • Interpretation of the Results, Business Validation and New Data Implementation

Logistic Regression for Machine Learning

  • What is Logistic Regression?
  • Difference between Linear Regression Vs. Generalized Linear Models Vs. Logistic Regression.
  • Build Logistic Regression Model (Binary Logistic Model)
  • Understand standard model metrics (Variable significance, Concordance, Gini, KS, Misclassification, Hosmer Lemeshov Test, ROC Curve etc)
  • Validation of Logistic Regression Models.
  • Standard Business Outputs (Lift charts, Model equation, Decile Analysis, ROC Curve, Probability Cut-offs, Drivers or variable importance, etc)
  • Interpretation of the Results, Business Validation and New Data Implementation

Time Series Forecasting

  • Intro About Time Series Forecasting
  • Components in Time Series – Seasonality, Trend, Cyclicity, Systematic, Level and Decomposition
  • Classification of Techniques (Pattern based or Pattern less)
  • Basic Techniques for Forecasting : – Averages, Smoothening, etc
  • Advanced Techniques – AR Models, holt’s winter, holt’s linear, ARIMA, etc
  • Measure Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning

  • Machine Learning Vs Predictive Modeling
  • Types of Business problems – Cache Mapping of Techniques – Regression vs. segmentation vs. classification vs. Forecasting.
  • Essentials Classes of Learning Algorithms -Supervised Vs Unsupervised Learning
  • What are different Phases of Predictive Modeling (Data Pre-processing, Model Building, Sampling, Validation)
  • Overfitting & Performance Metrics
  • Feature engineering & dimensionality reduction
  • Cost function Optimization
  • Overview of gradient descent algorithms.
  • What is Cross validation (Bootstrapping, K-Fold validation etc)
  • Overview on Model performance metrics (R-square, Adjusted R-squre, precision, sensitivity, specificity, RMSE, MAPE, AUC, ROC curve, recall, confusion metrics )

Decision Trees in Machine Learning

  • Introduction on Decision Trees.
  • Types of the Decision Tree Algorithms
  • Construct of Decision Trees using Simplified Examples
  • Generalizing Decision Trees
  • Pruning a Decision Tree
  • Decision Trees with Validation
  • Overfitting is best practice.

Supervised Learning: Ensemble Trees

  • Ensembling in Machine Learning
  • Difference between Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling.
  • Bagging Boosting, Stacking and Random Forest (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost

Unsupervised Learning

  • Importance of segmentation & Role of ML in Segmentation?
  • Distance in Math – Formulas and Concept.
  • K-Means Clustering algorithm
  • Expectation Maximization algorithm
  • Hierarchical Cluster Analysis
  • Sklearn Clustering (DBSCAN)
  • Principle components Analysis (PCA)

ANN (Artificial Neural Network)

  • Neural Networks modification and Its Applications
  • Single Layer Neural Network (Perceptrons), and Hand Calculations
  • Learning In a Multi Layered Neural Net.
  • Deep neural Networks for Regression
  • Deep neural Networks for Classification
  • Interpretation of Outputs and Fine tune the models with Hyper Parameters Tunning
  • Validating Artificial Neural Network models

KNN (K-Nearest Neighbor’s)

  • What is KNN & Applications?
  • KNN for missing Values.
  • KNN for resolve regression problems in python.
  • KNN for solving classification problems using python.
  • How to validate KNN model
  • Model fine tuning using hyper parameters

NAÏVE BAYES

  • Conditional Probability in Naïve Bayes.
  • Bayes Theorem and Its Probability Theorem.
  • Naïve Bayes for classifier.
  • Applications of Naïve Bayes in Classifier

VECOR MACHINES ALGORITHM

  • Applications of Support Vector Machine
  • Support Vector Regression – Data Mining Map
  • Support vector machine algorithm (Linear & Non-Linear)
  • Mathematical Intuition and how to develop.
  • Validating SVM Results.

TEXT DATA MINING

  • Taming big Data
  • Difference between Structured vs Unstructured vs. Semi-structured Data
  • Finding patterns in text: text Analysis, text as a graph
  • What is Natural Language processing (NLP)
  • Text Analytics – Sentiment Analysis with Python
  • Text Analytics – Word cloud analysis with Python
  • Text Analytics – Classification (Spam/Not spam)
  • Text Analytics – Segmentation using K-Means and Hierarchical Clustering
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