INTRODUCTION TO DATA SCIENCE WITH PYTHON 

Two Day course 16 hours

 

• 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 an analytics project

• Why Python for data science?

 

 

 

 

 

 

PYTHON: ESSENTIALS (CORE)

Two Day Course

 

• Overview of Python- Starting with Python

• Introduction to the installation of Python

• Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)

• Understand Jupyter notebook & Customize Settings

• The concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, 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?

 

 

 

 

 

DATA MANIPULATION – CLEANSING – MUNGING USING PYTHON MODULES

Two Day Course

 

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)

 

 

 

 

 

 

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)

 

 

 

 

 

 

INTRODUCTION TO STATISTICS

Two Day Course

 

• 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

 

 

 

 

 

INTRODUCTION TO PREDICTIVE MODELING

Two Day Course

 

• 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

 

 

 

 

DATA EXPLORATION FOR MODELING

Two Day Course

 

• 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

 

 

 

 

 

SEGMENTATION: SOLVING SEGMENTATION PROBLEMS

Two Day Course

 

• 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

 

 

 

 

 

LINEAR REGRESSION: SOLVING REGRESSION PROBLEMS

Two Day Course

 

• 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

 

 

 

 

LOGISTIC TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

Two Day Course

 

• 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

 

 

 

 

MACHINE LEARNING -PREDICTIVE MODELING – BASICS

Two Day Course

 

• 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 Pre-processing, Sampling, Model Building, Validation)

• Overfitting (Bias-Variance Trade off) & Performance Metrics

• Feature engineering & dimension reduction

• Concept of optimization & cost function

• Overview of gradient descent algorithm

• Overview of Cross validation(Bootstrapping, K-Fold validation etc)

• Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

 

 

 

 

UNSUPERVISED LEARNING: SEGMENTATION

Two Day Course

 

• What is segmentation & Role of ML in Segmentation?

• Concept of Distance and related math background

• K-Means Clustering

• Expectation Maximization

• Hierarchical Clustering

• Spectral Clustering (DBSCAN)

• Principle component Analysis (PCA)T - CONSOLIDATE LEARNINGS :

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