- Statistics for Data Science : 1 Months Training
- Python for Data Science : 1 Months Training
- Machine Learning and Natural Language Processing : 1 Months Training

Statistics for Data Science

Goal: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms different statistical parameters.

Objectives: At the end of this Module, you should be able to:

- Understand various data types
- Learn Various variable types
- List the uses of variable types
- Explain Population and Sample
- Discuss sampling techniques
- Understand Data representation

Topics:

- Introduction to Data Types
- Numerical parameters to represent data
- Mean
- Mode
- Median
- Sensitivity
- Information Gain
- Entropy
- Statistical parameters to represent data

Hands-On/Demo

- Estimating mean, median and mode using python
- Calculating Information Gain and Entropy

Goal: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

Objectives: At the end of this Module, you should be able to:

- Understand rules of probability
- Learn about dependent and independent events
- Implement conditional, marginal and joint probability using Bayes Theorem
- Discuss probability distribution
- Explain Central Limit Theorem

Topics:

- Uses of probability
- Need of probability
- Bayesian Inference
- Density Concepts
- Normal Distribution Curve

Hands-On/Demo:

- Calculating probability using python
- Conditional, Joint and Marginal Probability using Python
- Plotting a Normal distribution curve

Goal: Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint).

Objectives: At the end of this Module, you should be able to:

- Understand the concept of point estimation using confidence margin
- Draw meaningful inferences using margin of error
- Explore hypothesis testing and its different levels

Topics:

- Point Estimation
- Confidence Margin
- Hypothesis Testing
- Levels of Hypothesis Testing

Hands-On/Demo:

- Calculating and generalizing point estimates using python
- Estimation of Confidence Intervals and Margin of Error

Goal: In this module, you should learn the different methods of testing the alternative hypothesis.

Objectives: At the end of this module, you should be able to:

- Understand Parametric and Non-parametric Testing
- Learn various types of parametric testing
- Discuss experimental designing
- Explain a/b testing

Topics:

- Parametric Test
- Parametric Test Types
- Non- Parametric Test
- Experimental Designing
- A/B testing

Hands-On/Demo:

- Perform p test and t tests in python
- A/B testing in python

Goal: Get an introduction to Clustering as part of this Module which forms the basis for machine learning.

Objectives: At the end of this module, you should be able to:

- Understand the concept of association and dependence
- Explain causation and correlation
- Learn the concept of covariance
- Discuss Simpson’s paradox
- Illustrate Clustering Techniques

Topics:

- Association and Dependence
- Causation and Correlation
- Covariance
- Simpson’s Paradox
- Clustering Techniques

Hands-On/Demo:

- Correlation and Covariance in python
- Hierarchical clustering in python
- K means clustering in python

Learning Objective: Through this Module, you will understand in detail about Data Manipulation

Topics:

- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring a Dataset
- Analysing a dataset

Hands On/Demo:

- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
- GroupBy operations
- Aggregation
- Concatenation
- Merging
- Joining

Skills:

- Python in Data Manipulation

Goal: Learn the roots of Regression Modelling using statistics.

Objectives: At the end of this module, you should be able to:

- Understand the concept of Linear Regression
- Explain Logistic Regression
- Implement WOE
- Differentiate between heteroscedasticity and homoscedasticity
- Learn the concept of residual analysis

Topics:

- Logistic and Regression Techniques
- Problem of Collinearity
- WOE and IV
- Residual Analysis
- Heteroscedasticity
- Homoscedasticity

Hands-On/Demo:

- Perform Linear and Logistic Regression in python
- Analyze the residuals using python

Python for Data Science

Learning Objectives: You will get a brief idea of what Python is and touch on the basics.

Topics:

- Overview of Python
- The Companies using Python
- Different Applications where Python is used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the screen

Hands On/Demo:

- Creating “Hello World” code
- Variables
- Demonstrating Conditional Statements
- Demonstrating Loops

Skills:

- Fundamentals of Python programming

Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.

Topics:

- Python files I/O Functions
- Numbers
- Strings and related operations
- Tuples and related operations
- Lists and related operations
- Dictionaries and related operations
- Sets and related operations

Hands On/Demo:

- Tuple – properties, related operations, compared with a list
- List – properties, related operations
- Dictionary – properties, related operations
- Set – properties, related operations

Skills:

- File Operations using Python
- Working with data types of Python

Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

Topics:

- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object-Oriented Concepts
- Standard Libraries
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions

Hands On/Demo:

- Functions – Syntax, Arguments, Keyword Arguments, Return Values
- Lambda – Features, Syntax, Options, Compared with the Functions
- Sorting – Sequences, Dictionaries, Limitations of Sorting
- Errors and Exceptions – Types of Issues, Remediation
- Packages and Module – Modules, Import Options, sys Path

Skills:

- Error and Exception management in Python
- Working with functions in Python

Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.

Topics:

- NumPy – arrays
- Operations on arrays
- Indexing slicing and iterating
- Reading and writing arrays on files
- Pandas – data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
- matplotlib library
- Grids, axes, plots
- Markers, colours, fonts and styling
- Types of plots – bar graphs, pie charts, histograms
- Contour plots

Hands On/Demo:

- NumPy library- Creating NumPy array, operations performed on NumPy array
- Pandas library- Creating series and dataframes, Importing and exporting data
- Matplotlib – Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot

Skills:

- Probability Distributions in Python
- Python for Data Visualization

- Renaming and Recoding Variables ,Reshaping Data, Merging & Concatenating Datasets
- Using dply to manipulate data frames
- Data Type Conversion
- Data Values

Learning Objective: Through this Module, you will understand in detail about Data Manipulation

Topics:

- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring a Dataset
- Analysing a dataset

Hands On/Demo:

- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
- GroupBy operations
- Aggregation
- Concatenation
- Merging
- Joining

Skills:

- Python in Data Manipulation

Machine Learning and Natural Language Processing

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types. At the end of this module, you should be able to:

- Essential Python Revision
- Necessary Machine Learning Python libraries
- Define Machine Learning
- Discuss Machine Learning Use cases
- List the categories of Machine Learning
- Illustrate Supervised Learning, Unsupervised and Reinforcement Algorithms
- Identify and recognize machine learning algorithms around us
- Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc. At the end of this module, you should be able to:

- Understand What is Supervised Learning?
- Illustrate Logistic Regression
- Define Classification
- Explain different Types of Classifiers such as Decision Tree and Random Forest
- Understand What is Naïve Bayes Classifier
- How Naïve Bayes Classifier works?
- Understand Support Vector Machine
- Illustrate How Support Vector Machine works?
- Hyperparameter optimization
- ANN

Hands On:

- Implementation of Logistic regression, Decision tree, Random forest, SVM, ANN

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. At the end of this module, you should be able to:

- Define Unsupervised Learning
- Discuss the following Cluster Analysis
- K – means Clustering
- DBSCAN
- Hierarchical Clustering

Hands On:

- Implementing K-means Clustering
- Implementing Hierarchical Clustering
- Implementing DBSCAN

Learning Objectives: In this module, you will learn about text mining and the ways of extracting and reading data from some common file types including NLTK corpora

Topics:

- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora

Hands On/Demo:

- Install NLTK Packages using NLTK Downloader
- Accessing your operating system using the OS Module in Python
- Reading & Writing .txt Files from/to your Local
- Reading & Writing .docx Files from/to your Local
- Working with the NLTK Corpora

Learning Objectives: This module will help you understand some ways of text extraction and cleaning using NLTK

Topics:

- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition

Hands On/Demo:

- Tokenization: Regex, Word, Blank line, Sentence Tokenizers
- Bigrams, Trigrams & Ngrams
- Stopword Removal
- POS Tagging
- Named Entity Recognition (NER)

Learning Objective: In this Module, you will learn how to analyse a sentence structure using a group of words to create phrases and sentences using NLP and the rules of English grammar

Topics:

- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing

Hands On/Demo:

- Parsing Syntax Trees
- Chunking
- Chinking
- Automate Text Paraphrasing using CFG’s

Learning Objective: In this module, you will explore text classification, vectorization techniques and processing using scikit-learn

Topics:

- Machine Learning: Brush Up
- Bag of Words
- CountVectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
- Converting text to features and labels
- Naive Bayes Classifier
- SVM Classifier
- Topic Modeling using LDA
- NN Classifier
- Leveraging Confusion Matrix

Hands On/Demo:

- Demonstrate Bag of Words Approach
- Working with CountVectorizer ()
- Using TF & IDF
- Converting text to features and labels
- Demonstrate text classification using Multinomial NB Classifier
- Demonstrate LDA for Topic modelling
- Demonstrate SVM for text Classification
- Demonstrate NN for text classification
- Leveraging Confusion Matrix

These trainings are meant for conducting in colleges. If your college would like us to conduct drop an Enquiry at info@myanalyticsmentor.com.