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Data Science using Python Training in Chandigarh

Data Science Using Python

Who should do this course?

Candidates from various quantitative backgrounds, like Engineering, Finance, Math, Statistics, Economics, Business Management and have some knowledge on the data analysis, understanding on business problems etc.


Prerequisite Skills:

Basic SQL Queries

 
Data Science Using Python
Module1: Introduction
  • 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?
Module2:Core Python
  • 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
Module 3: Python Libraries for Data Science
  • Numpy
  • Scify
  • pandas
  • scikitlearn
  • statmodels
  • nltk
Module 4: Python Modules for Access, Import/Export Data
  • 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
Module 5: Data Manipulation, Cleansing and Munging
  • 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.)
Module 6: Data Analysis and Visualization
  • 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.
Module 7: 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/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  • Important modules for statistical methods: Numpy, Scipy, Pandas
Module 8: 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
Module 9: 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
Module 10: Data Preparation
  • Need of Data preparation
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
Module 11: 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 (K-Means Cluster Analysis)
  • Cluster evaluation and profiling - Identify cluster characteristics
  • Interpretation of results - Implementation on new data
Module 12: Linear Regression
  • 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
Module 13: Logistic Regression
  • 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
Module 14: Time Series Forecasting
  • 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
Module 15:Project work
  • Applying different algorithms to solve the business problems and bench mark the results