Data Science training in Ahmedabad

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Data Science training in Ahmedabad

Data Science training is a new interesting software technology, which is used to apply critical analysis, provide the ability to develop sophisticated models, for massive data sets and drive the business insights. Data Science utilizes the potential and scope of Hadoop, R programming, and machine learning implementation, by making use of Mahout. Best Data Science training in Ahmedabad, learn Data Science and Data Interpretation for Business Intelligence.


What is Data Analytics?
Types of Data Sets and Data Models
Understanding of Business Analytics
Need of Business Analytics
Types of Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Supply Chain Analytics
Health Care Analytics
Marketing Analytics
Human Resource Analytics
Data Management and Business Analytics
Web Analytics and Business Intelligence
Data Science as a Strategic Asset
Data Warehousing and OLAP
Data Visualization using R and Excel
Data Visualization using Tableau
BigData and Data Science

  • Exploring Numeric Variables
  • Measuring the Central Tendency – The Model
  • Measuring Spread – Variance and Standard Deviation
  • Visualizing Numeric Variables – Boxplots and Histograms
  • Understanding Numeric Data – Uniform and Normal Distributions
  • Measuring the Central Tendency – The Mode
  • Exploring Relationships between Variables
  • Visualizing Relationships – Scatterplots
  • Nominal and Ordinal Measurement
  • Interval and Ratio Measurement
  • Statistical Investigation
  • Inferential Statistics
  • Probability and Central Limit Theorem
  • Exploratory Data Analysis
  • Normal Distribution
  • Distance Measures
  • Euclidean & Manhattan Distance Supervised Learning Techniques and the Implementation of Algorithms ETLHIVE
  • Minkowski & Mahalanobis
  • Cosine
  • Correlation
  • PPMC (Pearson Product Moment Coorelation)
  • Importance of Hypothesis Testing in Business
  • Null and Alternate Hypothesis
  • Understanding Types of Errors
  • Contingency Table and Decision Making
  • Confidence Coefficient
  • Upper Tail Test and Test Statistics
  • Understanding Parametric Tests
  • Z-Test and Z-Test in R
  • Chi-Square Test
  • Degree of Freedom
  • One-Way ANOVA Test
  • F-Distribution, F-Ratio Test

• Abstraction and Knowledge Representation
• Generalization
• Assessing the Success of Learning
• Steps to apply Machine Learning to your Data
• Choosing a Machine Learning Algorithm
• Thinking about the Input Data
• Thinking about Types of Machine Learning Algorithms
• Matching your Data to an Appropriate Algorithm

Essentials of Python
Defining Python
History of Python and its Growing Popularity
Features of Python and its Wide Functionality
Python 2 vs Python 3
Setting Up Python Environment for Development
What and How of Python Installation?
IDEs: IDLE, Pscharm, and Enthought Canopy
Running a Python Script
Writing First Python Program
Python Scripts on UNIX and Windows
Installation on Ubuntu-based Machines
Programming on Interactive Shell
Python Identifiers and Keywords
Indentation in Python
Comments and Writing to the Screen
Command Line Arguments and Flow Control
User Input
Python Data Types and Core Objects

Defining Built-in Functions
Variables and their types
Variables – String Variables
Variables – Numeric Types
Variables – Boolean Variables
Boolean Object and None Object
Tuple Object and Operations
Dictionary Object and Operations
Types of Variables – Dictionary
Comparision of Variables
Dictionary Methods and Manipulations
Operators and Logical Operators
Data Structures and Data Processing
Arithmetic Operations on Numeric Values
Operators and Keywords for Sequences
Conditional Statements and Loops

Break Statements and Continue Statements
Using Indentations for defining if & else block
Loops in Python
While, Nested, Demo-Create
How to Control Loops?
Sequence and Iterable Objects
UDF Functions

Types of Functions
Creating UDF Functions
Function Parameters
Unnamed and Named Parameters
Creating and Calling Functions
Python user Defined Functions
Python packages Functions
Anonymous Lambda Function
Understanding String Object Functions
List and Tuple Object Functions
Studying Dictionary Object Functions
Python Packages and File Handling

Studying Types of Modules
os, sys, time, random, datetime, zip modules
How to Create Python User Defined Modules?
Understanding Pythonpath
Creating Python Packages
init File and Package Initialization
What and How of File Handling with Python?
How to Process Text Files using Python?
Read/Write and Append File Object
Test Operations: os.path
Object-Oriented Programming in Python & Exception Handling

Defining Classes, Objects, and Initializers
Attributes – Built-In Class
Destroying Objects
Methods – Instance, Class, Static, Private methods, and Inheritance
Data Hiding
Module Aliases and reloading modules
Python Exceptions Handling
Standard Exception Hierarchy
.. except…else
.. finally…clause
Creating Self-Exception Class
User-defined Exceptions
Error Debugging and Regular Expressions

Project Skeleton
Creating and Using the Skeleton
How to use pdb debugger?
Using Pycharm Debugger
Asserting Statement for Debugging
Using UnitTest Framework for Testing
Understanding Regular Expressions
Match Function, Search Function, and the Comparision
Compile and Match, Match and Search
Search and Replace
What and How of Extended Regular Expressions?
Wildcard Characters
Fundamentals of Database Interaction with Python

Understanding CRUD Operations
Creating Database Connection
Python MySQL Database Access
Operations: Create, Insert, Read, Update, Delete
What are DML and DDL Operations?
Performing Transactions
How to Handle Database Errors?
What and How of Disconnecting Database?

• Python Setup
• Network Topology
• Neural Networks: Master Feed-Forward
• Recurrent and Gaussian Neural Network
• The Number of Layers
• The Direction of Information Travel
• The Number of Nodes in Each Layer
• Training Neural Networks with Backpropagation
• Support Vector Machines
• Classification with Hyperplanes
• Finding the Maximum Margin
• The Case of Linearly Separable Data
• The Case of Non-Linearly Separable Data
• Retrieve Data using SQL Statements
• Using Kernels for Non-Linear Spaces
• Performing OCR with SVMs
• Mathematical Computing with Python – NumPy
• Understanding NumPy
• ndarray: Purpose, Properties, Types
• ndarray: Class and Attributes
• How to Access Array Elements?
• Python Environment Setup and Essentials
• Anaconda Python Distribution – Windows, Mac OS, Linux
• Jupyter Notebook Installation
• Variable Assignment
• Understanding Data Types: Integer, Float, String, None, Boolean, Typecasting
• Tuples: Create, Access, and Slice
• Dicts: Create, View, Access, and Modify
• Studying Basic Operations: ‘in’, ‘+’, ‘*

• Data Manipulation and Machine Learning with Python
• Data Manipulation with Python – Pandas
• Understanding Pandas
• Defining Data Structures
• Data Operations and Data Standardization
• Pandas: File Read and Write Support
• SQL Operation
• Machine Learning with Python – Scikit
• Supervised Learning Models: Linear and Logistic Regression, Ridge, Lasso, ElasticNet
• Unsupervised Learning Models: Clustering and Association Rule Mining
• Model Persistence and Model Evaluation
• Natural Language Processing with Scikit
• NLP Environment Setup & Applications
• NLP Sentence Analysis & Libraries
• Scikit – Built-in Modules & Feature Extraction
• Scikit – Grid Search & Parameters
• Data Visualization and Matplotlib
• Python Libraries
• Features of Matplotlib
• Line Properties Plot with (x, y)
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple Plots and SubPlots
• Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Studying Universal Functions
• What is Shape Manipulation?
• Linear Algebra
• Scientific Computing with Python – SciPy
• Understanding SciPy
• Studying SciPy Sub-packages
• Sub-Packages: Integration and Optimize
• Sub-Packages: Statistics, Weave, I O
• Linear Algebra

• Dashboards and Work Sharing
• Python and Hadoop, MapReduce, and Spark
• Importance of Integrating Python with Hadoop
• Understanding BigData Hadoop Architecture
• Working with MapReduce
• Cloudera QuickStart VM Setup
• Studying Apache Spark
• Resilient Distributed Systems (RDD)
• Working with PySpark
• PySpark Integration with Jupyter Notebook
• Python Web Scraping and Data Science
• The Parser
• Searching & Modifying the Tree
• Printing, Formatting, Encoding
• Building Interactive Dashboards
• What are Action Filters?
• How to create Story Boards?
• Best Practices to create Dashboards
• Cover Pages
• What is Data Visualization?
• Scope of Data Visualization
• Tableau Visualization Engine
• Various Visualizations: Text Tables, Pie Charts, Bar, and Line
• Visualizations: Heat Maps, Side by Side Lines, Highlight Tables, Circle Plots
• Visualizations: Tree Maps, Area Charts, Dual Charts, Scatter Plots
• Tableau Workspace
• Dashboard and the Startup Quadrant
• Dashboard Tricks: Reference Lines, Droplines, and Tool Tips

• Data Organization
• Tableau and Data Connections
• Annotations
• Tool Tips and keyboard short cuts
• Sharing work
• Tableau Online
• Tableau Reader
• Tableau Public

• How to Organize and Simplify Data?
• What is Filtering?
• How to Apply a Filter to a View?
• Filtering on Dimensions
• Totals and Sub totals
• Aggregating Measures and Data Spotlighting
• String Functions and Logical Functions
• How to Sort Data in Tableau?
• Combined Fields
• Group and Aliases
• Advanced Table Calculations
• Calculated Fields and Table Calculations
• Quality Assurance for Table Calculations
• Hierarchies and Sets
• Tableau Bins
• Understanding Data Connections
• How to connect to Tableau Data Server?
• Data Connections: Joining and Blending
• Defining a Join
• Various Kinds of Join
• Usage of Join
• Right Outer Join
• Custom SQL Enabled
• Data Blending and Tableau
• Usage of Data Blending
• Data Blending in Tableau
• What is Kerberos Authentication?
• Working of Kerberos Authentication

• Studying Retail Sector Forecasting
• Building and Analyzing Box Plots
• How to work with Large Data Sources in Tableau?
• Understanding and Implementing Pivot and Split
• Real World Retail and its Data
• Data Source Filters
• Trendlines
• Advanced Timeseries Blending
• Calculating Sales Per Capita
• Forecasting in Tableau
• How to Present a Storyline?
• Creating Animations in Tableau
• Real World Case Study: World Health Trends Investigation
• Building Visualization and Adding Animation
• Manually Sorting Blended Data
• Finalizing the Dashboard and Animations in Tableau
• Fixed Size and Variable Sized Bins
• Drilling and Drilling Methods
• Aggregations
• Formatting and Annotations
• Spatial Analysis and Geo-Coding
• Chart Types: Motion Charts, Gantt Charts
• Box and Whisker Plots
• Mapping and Locations
• Pan Zoom Lasso and Radial Selection


If you miss a lecture, you can attend another session in any other live batch. We have multiple batches running simultaneously.

Yes, we have multiple centers. We are located at Pimple Saudagar, Nal Stop, and Kharadi. You can reach us from every corner of the city.

Yes, we arrange demo sessions for all the courses at all the branches. After attending a demo session, you come to know about the advantages of undergoing training, and about the importance of hands-on tasks.

Our training professionals are highly qualified and have hands-on industry experience.

Yes, the courses available at the ETLhive are a perfect blend of theory and practice. We do arrange Live-Projects so that the trainees get an extensive knowledge about the real-time projects and the allied issues, and consequently develop the ability to tackle real-life scenarios.

Yes, we are into three kinds of training: Customer Training, Corporate Training, and Online Training. The training centers are well-equipped which makes online learning possible, enjoyable and effective. Online training is delivered through the use of Webinars, High Definition Videos, and Audio Capability Servers. We will help you attend the course remotely from your desktop or laptop, with the help of local access.

We make sure we are available for our customers. In case you have any doubts after you complete your course, do not hesitate to contact us. Our support system ensures assistance and we will try to clear all your doubts.

You may attend the next batch in case you are unable to attend the batch you enrolled in. The information about our future batches is always available on our website and on other social media links such as Facebook, Twitter, and Google+.

ETLhive is considered as the leading pioneer in customer, corporate, and online training. Our training professionals impart the best of training experiences with detailed theoretical knowledge and real-time projects, so much so that our students excel in their careers. We provide job assistance in terms of resume preparation and interview etiquettes.

The schedule for all the courses is different. You may check our website or our social media links for latest information. Nevertheless, our support staff will inform you about the schedule of your class via email, SMS, or call.

Yes, we provide various kinds of learning material which will help you master the course. We provide PDFs, PPTs, Recorded Videos, Certification Related PDFs, and Best Practices. We also provide FAQs for Interviews and Sample Resumes.

Yes. We have a wide range of online study material. We provide PDFs, PPTs, Recorded Videos, Certification Related PDFs, and Best Practices. We also provide FAQs for Interviews and Sample Resumes.

You can pay through cash or net banking. We also accept cheques liable to be cleared 24 hours before the first lecture of the batch.

After you complete your course modules, you will have to work on projects. We will provide certificates after evaluating your projects, thereafter you will be considered as certified professionals.

If you have any other query, do not hesitate to consult our counsellors. Feel free to call us at 1800-2000-991/8055020011, or email us on

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