Data Science Course Details

1. 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?

2. 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

3. Python Libraries for Data Science

⇒ Numpy

⇒ Scify

⇒ pandas

⇒ scikitlearn

⇒ statmodels

⇒ nltk

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

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.)

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

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

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

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

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

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

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

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

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

15. Project work

⇒ Applying different algorithms to solve the business problems and bench mark the results