Data Science with Python Training

• What is analytics and data science?

• Common terms in analytics

• Different Sectors Using Data Science

• Purpose and Components of Python

• What is Python?

• Features of Python

• Why Python?

• Interpreter and types

• Applications of Python

• "Hello World" program

• Variables

• Types of variable datatypes

• Example programs with each type

• Operators

• Types of operators

• Basic programs

• Operator overloading

• Define control statements

• Types of control statements

• Why Looping statements are used?

• Types of looping statements

• Range function

• Functions

• Types of functions

• Global and local variables

• Modules

• Types of modules and use

• What is Files?

• Type of Files

• File Access Mode

• Handling I/O

• Oops concept

• Collection

• Collection module and types

• Types of error

• Exception handling

• Concept of Packages/Libraries - Important packages(NumPy, Pandas, Matplotlib)

• What is Numpy

• What is Ndarray

• Data types in NumPy

• Mathematical Functions

• Array manipulation

• Numpy array visualization

• Broadcasting

• What is Pandas

• Concepts of Pandas

• Why and how pandas is used for data manipulation

• Cleansing Data with Python

• Data Manipulation

• Data manipulation tools

• Python Built-in Functions (Text, numeric, date, utility functions)

• Python User Defined Functions

• Stripping out extraneous information

• Normalizing data

• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

• Data Analytics Conclusion or Predictions

• Data Analytics Communication

• Importing Data from various sources (csv, txt, excel, access, etc)

• Connecting to database

• Viewing Data objects - sub setting, methods

• Exporting Data to various formats

• 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

• Introduction exploratory data analysis

• Descriptive statistics, Frequency Tables

• Univariate Analysis (Distribution of data & Graphical Analysis)

• Concept of model in analytics and how it is used?

• Common terminology used

• Popular modelling algorithms

• Types of Business problems - Mapping of Techniques

• Different Phases of Predictive Modelling

• EDA for exploring the data and identifying any problems with the data

• Identify missing data

• Identify outliers data

• Visualize the data trends and patterns

• What is regression?

• Applications of regression

• Types of regression

• Fitting the regression line

• Simple linear regression

• Simple linear regression in python

• Polynomial regression

• Polynomial regression in python

• Gradient Descent

• Cost function

• Regularization

• Ridge and lasso Regression

• How is classification used?

• Applications of classification

• Logistic Regression, Sigmoid function

• Decision tree

• K-Nearest Neighbors (K-NN)

• SVM

• Naive Bayes

• Confusion Matrix

• Precision, Recall

• F1-score

• RoC, AuC

• n-fold cross validation

• Measuring classifier performance

• Factors affecting classifier performance

• Overfitting

• Ensemble Learning

• Bagging and Boosting

• Application of Unsupervised learning, examples and applications

• Clustering

• Hierarchical Clustering in Python, Agglomerative and Divisive techniques

• Measuring the distance between two clusters

• k-means algorithm

• Limitations of K-means clustering

• SSE and Distortion measurements

• Demo: Agglomerative Hierarchical clustering

• 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

• Web Scraping and Parsing

• Understanding and Searching the Tree

• Navigating options

• Modifying the Tree

• Parsing and Printing the Document