Data Science [6 months]

1.Python for Data Science:

Python Basics

• Variables

• Operands and Expressions

• Conditional Statements

• Loops

• Command Line Arguments

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​Data Structure &File Operations

• Python - Files Input/Output Functions

• Lists and Related Operations

• Tuples and Related Operations

• Strings and Related Operations

• Sets and Related Operations

• Dictionaries and Related Operations

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Functions and Object Oriented Programming

• Function Parameters

• Different Types of Arguments

• Global Variables Keyword

• Variable Scope and Returning Values

• Lambda Functions

• Built-In Functions

• Object-Oriented Concepts

• Built-In Class Attributes

• Public, Protected and Private Attributes, and Methods

• Class Variable and Instance Variable

• Constructor and Destructor

• Decorator in Python

• SOLID Object-Oriented Principles

• Inheritance and Its Types

• Overriding

• Getter and Setter Methods

Working with Modules and Handling Exceptions

• Sys Module

• Os Module

• Math Module

• Date-Time Module

• Random Module

• JSON Module

• Regular Expression

• Exception Handling

Introduction to NumPy

• NumPy – Arrays

• Operations on Arrays

• Indexing Slicing and Iterating

• NumPy Array Attributes

• Matrix Product

• NumPy Functions

• Functions

• Array Manipulation

• File Handling Using NumPy

• Array Creation and Logic Functions

Data Manipulation using pandas

• Series

• Data Frames

• Importing and Exporting Files in Python

• Functionalities of a Data Object

• Merging of Data Objects

• Concatenation of Data Objects

• Types of Joins on Data Objects

• Data Cleaning using pandas

• Exploring Datasets

2.Data Science Primer and Statistics

Exploratory Data Analysis

• Stages of Analytics

• CRISP DM Data Life Cycle

• Data Types

• Introduction to EDA

• First, Second, Third, Fourth Moment Decision

• Correlation

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Feature Engineering

• Feature Engineering

• Feature Engineering Process

• Feature Engineering Techniques

Inferential Statistics & Hypothesis Testing

• Discrete Probability Distributions

• Continuous Probability Distributions

• Central Limit Theorem

• A Decision, And Critical Value Method

3.Machine Learning

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Linear Regression

• Simple Linear Regression

• Simple Linear Regression In Python

• Multiple Linear Regression

• Multiple Linear Regression In Python

• Industry Relevance Of Linear Regression

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Logistic Regression

• Univariate Logistic Regression

• Multivariate Logistic Regression: Model

• Building And Evaluation

• Logistic Regression

KNN classifier

• Data mining classifier technique

• Application of KNN classifier

• Lazy learner classifier

• Altering hyperparameter(k) for better accuracy

Support Vector classifier

• SVM hyperplane

• Max margin hyperplane

• Kernel tricks for non linear spaces

Decision Tree Classifier

• Rule based classification method

• Different nodes for develop decision trees

• Discretization

• Entropy

• Greedy approach

• Information gain

Ensemble Learning

• Reliability and performance of a standalone model

• Homogeneous & Heterogeneous Ensemble Technique

• Bagging & Boosting

• Random forest

• Stacking

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Time Series Analysis

• Difference between cross sectional and time series data

• Different component of time series data

• Visualization techniques for time series data

• ARIMA Model

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Clustering

• Prelims of clustering

• Measuring distance between record and groups

• Linkage functions

• Dendrogram

Dimensionality Reduction

• Dimension reduction

• Application of PCA

• PCA & its working

• SVD & its working

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Market Basket Analysis

• Point of Sale

• Application of Association rules

• Measure of association rules

• Condition probability

• Lift ratio

4.Deep Learning

Introduction to Perceptron, Multilayer Perceptron/ANN

• Intuition of neural networks

• Perceptron algorithm

• Calculation of new weights

• Non linear boundaries in MLP

• Integration function

• Activation function

• Error surface

• Gradient descent

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Deep Learning Black Box Technique – CNN, RNN

• Convolution network applications

• Challenges in classifying the images using MLP

• Parameter explosion

• Pooling layers

• Fully connected layers

• Modeling sequence data

• Vanishing/Gradient descent explode

Platforms for Deep Learning & Deep Learning Software Libraries

• What is a Deep Learning Platform?

• Theano

• Torch

5.Data Visualization and Story Telling

Basic Visualization Tools

• Bar Charts

• Histograms

• Pie Charts

• Box Plots

Basic Visualization Tools Continued

• Scatter Plots

• Line Plots and Regression

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Specialized Visualization Tools

• Pair plot

• Word Clouds

• Radar Charts

• Waffle Charts

6.Natural Language Processing

Text Mining & Natural Language Processing

• Text data generating sources

• How to give structure to text structure using bag of words

• Terminology used in text data analysis

• DTM & TDM

• TFIDF & its usage

• Word cloud and its interpretation

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7.SQL

Getting Started and Creating, Selecting & Retrieving Data with SQL

• Introduction to Databases

• How to create a Database instance on Cloud?

• Provision a Cloud hosted Database instance.

• What is SQL?

• Thinking About Your Data

• Relational vs. Transactional Models ER Diagram

• CREATE Table Statement and DROP tables

• UPDATE and DELETE Statements

• Retrieving Data with a SELECT Statement

• Creating Temporary Tables

• Adding Comments to SQL

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Filtering,Sorting, and Calculating Data with SQL

• Basics of Filtering with SQL

• Advanced Filtering: IN, OR, and NOT

• Using Wildcards in SQL

• Sorting with ORDER BY

• Math Operations

• Aggregate Functions

• Grouping Data with SQL

Subqueries and Joins in SQL

• Using Subqueries

• Subquery Best Practices and Considerations

• Joining Tables

• Cartesian (Cross) Joins

• Inner Joins

• Aliases and Self Joins

• Advanced Joins: Left, Right, and Full Outer Joins

• Unions

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Modifying and Analyzing Data with SQL

• Working with Text Strings

• Working with Date and Time Strings

• Date and Time Strings Examples

• Case Statements

• Views

• Data Governance and Profiling

• Using SQL for Data Science

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Accessing Databases using Python

• How to access databases using Python?

• Writing code using DB-API

• Connecting to a database using DB API

• Create Database Credentials

• Connecting to a database instance

• Creating tables, loading, inserting, data and querying data

• Analysing data with Python

8.Excel

Analyzing and Visualizing Data using Excel

• Input data & handling large spreadsheets

• Tricks to get your work done faster

• Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)

• Transforming messy data into shape

• Cleaning, Processing and Organizing large data

• Spreadsheet design principles

• Drop-down lists in Excel and adding data validation to the cells.

• Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers and Timelines

• Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.

• Excel features: - Sort, Filter, Search & Replace Go to Special etc…

• Importing and Transforming data (with Power Query)

• Customize the Microsoft Excel interface

• Formatting correctly for professional reports.

• Commenting on cells.

• Automate data entry with Autofill and Flash-fill.

• Writing Excel formulas & referencing to other workbooks / worksheets.

• Printing options

• Charts beyond column and bar charts: - Pareto chart, Histogram, Treemap, Sunburst

9.Tableau

Analyzing and Visualizing Data using Tableau

• Exploring Data using Tableau

• Working with Data using Tableau including Data Extraction and

• Blending

• Various Charts in Tableau(Basics to Advanced)

• Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields

• Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau

• Context Filters

• Groups , Sets and Combined Sets

• Reference Lines, Bands and Distribution

• Parameters, Dynamic Parameters and Actions

• Forecasting-Exponential Smoothening Techniques

• Clustering

• Calculated Fields in Tableau, Quick Tables

• Tableau Mapping Features

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Training highlights:

• 100% Practical Training + Live projects
• In House Placements
• Training by experienced mentors cum developers.
• Daily assignments & notes will be provided.

About ToXSL:

• Tremendous Growth Opportunity!!
• Work with the newest and most cutting edge technologies!
• High Visibility, Autonomous Role in a Collaborative Environment!
• Outstanding Work/Life Balance!
• Competitive Compensation, Benefits, and other Perks!!