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- Introduction to Data Science
What is Analytics & Data Science
Common Terms in Analytics
What is data
Classification of data
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
- Accessing/Importing and Exporting Data
Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects - sub setting, methods
Exporting Data to various formats
Important python modules: Pandas
- Data Manipulation: Cleansing - Munging Using Python Modules
Cleansing Data with Python
Filling missing values using lambda function and concept of Skewness.
Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
Normalizing data
Feature Engineering
Feature Selection
Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
Label encoding/one hot encoding
- 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 etc.)
- Introduction to Statistics
Descriptive Statistics
Sample vs Population Statistics
Random variables
Probability distribution functions
Expected value
Normal distribution
Gaussian distribution
Z-score
Central limit theorem
Spread and Dispersion
Inferential Statistics-Sampling
Hypothesis testing
Z-stats vs T-stats
Type 1 & Type 2 error
Confidence Interval
ANOVA Test
Chi Square Test
T-test 1-Tail 2-Tail Test
Correlation and Co-variance
- Introduction to Predictive Modelling
Concept of model in analytics and how it is used
Common terminology used in Analytics & Modelling process
Popular Modelling algorithms
Types of Business problems - Mapping of Techniques
Different Phases of Predictive Modelling
- Data Exploration for Modelling
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
Imbalanced Data Techniques
- Data Pre-Processing & Data Mining
Data Preparation
Feature Engineering
Feature Scaling
Datasets
Dimensionality Reduction
Anomaly Detection
Parameter Estimation
Data and Knowledge
Selected Applications in Data Mining
